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Available: % http://www.ctan.org/tex-archive/macros/latex/contrib/supported/eqparbox/ % *** Do not adjust lengths that control margins, column widths, etc. *** % *** Do not use packages that alter fonts (such as pslatex). *** % There should be no need to do such things with IEEEtran.cls V1.6 and later. % correct bad hyphenation here \hyphenation{op-tical net-works semi-conduc-tor} \newcommand{\bi}{\begin{itemize}} \newcommand{\ei}{\end{itemize}} \newcommand{\bd}{\begin{description}} \newcommand{\ed}{\end{description}} \newcommand{\bbi}{\begin{itemize}} \newcommand{\eei}{\end{itemize}} \newcommand{\be}{\begin{enumerate}} \newcommand{\ee}{\end{enumerate}} \newcommand{\fig}[1]{Figure~\ref{fig:#1}} \newcommand{\eq}[1]{Equation~\ref{eq:#1}} \newcommand{\tion}[1]{\S\ref{sec:#1}} \begin{document} % % paper title \title{Controlling Randomized Unit Testing With Genetic Algorithms} % % % author names and IEEE memberships % note positions of commas and nonbreaking spaces ( ~ ) LaTeX will not break % a structure at a ~ so this keeps an author's name from being broken across % two lines. % use \thanks{} to gain access to the first footnote area % a separate \thanks must be used for each paragraph as LaTeX2e's \thanks % was not built to handle multiple paragraphs \author{James~H.~Andrews,~\IEEEmembership{Member,~IEEE,} Tim~Menzies,~\IEEEmembership{Member,~IEEE,} and~Felix~C.~H.~Li% <-this % stops a space \thanks{Manuscript received January 1, 2001; revised January 1, 2001.}% <-this % stops a space \thanks{J.\ Andrews and F.\ Li are with the Department of Computer Science, University of Western Ontario, London, Ont., Canada, N6A 2B7. E-mail: andrews@csd.uwo.ca.}% \thanks{T.\ Menzies is with the Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV 26506-610. E-mail: tim@menzies.us.}} % note the % following the last \IEEEmembership and also the first \thanks - % these prevent an unwanted space from occurring between the last author name % and the end of the author line. i.e., if you had this: % % \author{....lastname \thanks{...} \thanks{...} } % ^------------^------------^----Do not want these spaces! % % a space would be appended to the last name and could cause every name on that % line to be shifted left slightly. This is one of those "LaTeX things". For % instance, "A\textbf{} \textbf{}B" will typeset as "A B" not "AB". If you want % "AB" then you have to do: "A\textbf{}\textbf{}B" % \thanks is no different in this regard, so shield the last } of each \thanks % that ends a line with a % and do not let a space in before the next \thanks. % Spaces after \IEEEmembership other than the last one are OK (and needed) as % you are supposed to have spaces between the names. For what it is worth, % this is a minor point as most people would not even notice if the said evil % space somehow managed to creep in. % % The paper headers \markboth{IEEE Transactions on Software Engineering,~Vol.~1, No.~1,~January~2001}{Andrews \MakeLowercase{\textit{et al.}}: Using a Genetic Algorithm to Control Randomized Unit Testing} % The only time the second header will appear is for the odd numbered pages % after the title page when using the twoside option. % If you want to put a publisher's ID mark on the page % (can leave text blank if you just want to see how the % text height on the first page will be reduced by IEEE) \pubid{0000--0000/00\$00.00~\copyright~2002 IEEE} % use only for invited papers %\specialpapernotice{(Invited Paper)} % make the title area \maketitle \begin{abstract} Randomized testing is an effective method for testing software units. Thoroughness of randomized unit testing varies widely according to the settings of certain parameters, such as the relative frequencies with which methods are called. In this paper, we describe Nighthawk, a system which uses a genetic algorithm (GA) to find parameters for randomized unit testing that optimize test coverage. We show that NIGHTHAWK achieves the same coverage as previous studies did while retaining the positive attributes of randomized testing, and that we can achieve high coverage on real-world software units. Designing GAs is somewhat of a black art. We therefore use a feature subset selection (FSS) tool to assess the gene types inside our GA. Using that tool, we can prune back 90\% of our gene types, while still achieving most of the coverage found using all the mutators. Our pruned GA achieves almost the same results as the full system, but in only 10\% of the time. These pruning results suggest that FSS for mutator pruning could significantly optimize meta-heuristic search-based software engineering tools. \end{abstract} \begin{keywords} Software testing, randomized testing, genetic algorithms, feature subset selection, search-based optimization \end{keywords} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Introduction} % XXX NOT MORE THAN 35 PAGES IN THIS FORMAT \IEEEPARstart{S}oftware testing involves running a piece of software (the software under test, or SUT) on selected input data, and checking the outputs for correctness. The goals of software testing are to force failures of the SUT, and to be thorough. The more thoroughly we have tested an SUT without forcing failures, the more sure we are of the reliability of the SUT. Randomized testing is the practice of using randomization for some aspects of test input data selection. % A software unit is a % method, group of methods, module or class; a unit test is % usually built upon a sequence of method calls. Several independent studies \cite{miller-fuzz-cacm,andrews-etal-rute-rt,% pacheco-etal-icse2007,groce-etal-icse2007} have found that randomized testing of software units is effective at forcing failures in even well-tested units. However, there remains a question of whether randomized testing can be thorough enough. Using various code coverage measures to measure thoroughness, researchers have come to varying conclusions about the ability of randomized testing to be thorough \cite{michael-etal-ga-tcg,visser-etal-issta06,andrews-etal-rute-rt}. The thoroughness of randomized unit testing is dependent on parameters that control when and how randomization is applied. These parameters include the number of method calls to make, the relative frequency with which different methods are called, and the ranges from which numeric arguments are chosen. The manner in which previously-used arguments or previously-returned values are used in new method calls, which we call the {\it value reuse policy}, is also a crucial factor. It is often difficult to work out the optimal values of the parameters and the optimal value reuse policy by hand. This paper describes the Nighthawk unit test data generator. Nighthawk has two levels. The lower level is a randomized unit testing engine which tests a set of methods according to parameter values specified as genes in a chromosome, including parameters that encode a value reuse policy. The upper level is a genetic algorithm (GA) which uses fitness evaluation, selection, mutation and recombination of chromosomes to find good values for the genes. Goodness is evaluated on the basis of test coverage and number of method calls performed. Users can use Nighthawk to find good parameters, and then perform randomized unit testing based on those parameters. The randomized testing can quickly generate many new test cases that achieve high coverage, and can continue to do so for as long as users wish to run it. This paper also discusses optimization methods for GA tools like Nighthawk. Using feature subset selection methods, we show that we can prune many of Nighthawk's mutators (gene types) without compromising coverage. The pruned Nighthawk tool achieves nearly the same coverage as full Nighthawk (90\%) and does so ten times faster. Therefore, we recommend that meta-heuristic search-based SE tools should also routinely perform subset selection. % XXXX why this is great \subsection{Randomized Unit Testing} Unit testing is variously defined as the testing of a single method, a group of methods, a module or a class. We will use it in this paper to mean the testing of a group $M$ of methods, called the {\it target methods}. A unit test is a sequence of calls to the target methods, with each call possibly preceded by code that sets up the arguments and the receiver\footnote{ We use the word ``receiver'' to refer to the object that a method is called on. For instance, in the Java method call ``{\tt t.add(3)}'', the receiver is {\tt t}. }, and with each call possibly followed by code that stores and checks results. {\it Randomized unit testing} is unit testing where there is some randomization in the selection of the target method call sequence and/or arguments to the method calls. Many researchers \cite{doong-frankl-tosem94,antoy-hamlet-tse-jan2000,% claessen-hughes-quickcheck,pacheco-etal-icse2007,% sen-etal-cute,visser-etal-issta06, andrews-etal-rute-rt} have performed randomized unit testing, sometimes combined with other tools such as model checkers. % A key concept in randomized unit testing is that of {\it value reuse}. We use this term to refer to how the testing engine reuses the receiver, arguments or return values of past method calls when making new method calls. In previous research, value reuse has mostly taken the form of making a sequence of method calls all on the same receiver object. In our previous research, we developed a GUI-based randomized unit testing engine called RUTE-J \cite{andrews-etal-rute-rt}. To use RUTE-J, users write their own customized test wrapper classes, hand-coding such parameters as relative frequencies of method calls. Users also hand-code a value reuse policy by drawing receiver and argument values from value pools, and placing return values back in value pools. Finding good parameters quickly, however, requires experience with the tool. The Nighthawk system described in this paper significantly builds on this work by automatically determining good parameters. The lower, randomized-testing, level of Nighthawk initializes and maintains one or more value pools for all relevant types, and draws and replaces values in the pools according to a policy specified in a chromosome. The chromosome also specifies relative frequencies of methods, method parameter ranges, and other testing parameters. The upper, genetic-algorithm, level performs a search for the parameter setting that causes the lower level to achieve a high value of a coverage-related measure. The information that Nighthawk uses about the SUT is only information about type names, method names, parameter types, method return types, and which classes are subclasses of others; this makes its general approach robust and adaptable to other languages. Designing a GA means making decisions about what features are worthy of modeling and mutating. For example, much of the effort on this project was a laborious trial-and-error process of trying different types of genes within a chromosome. To simplify that process, we describe experiments here with automatic feature subset selection (FSS), which lead us to propose that automatic feature subset selection should be a routine part of the design of any large GA system. % First, we try a very large and elaborate set of %gene types. Next, we filter that set using automatic feature %subset selection. The filtered set ran over 40\% more quickly, with %no loss of coverage. We therefore propose that automatic %feature subset selection should be a routine part of the design %of any large GA system. \subsection{Contributions and Paper Organization} The main contributions of this paper are as follows. \begin{enumerate} \item We describe the implementation of a novel two-level genetic-random testing system, Nighthawk. In particular, we describe how we encode a value reuse policy in a manner amenable to meta-heuristic search. \item We compare Nighthawk to prior research, showing that it can achieve the same coverage levels, while retaining the desirable properties of randomized testing, such as quick generation of new test cases. \item We describe the results of a case study carried out on real-world units (the Java 1.5.0 Collection and Map classes) to determine the effects of different option settings on the basic algorithm. We show that Nighthawk can achieve high coverage automatically on these units. We also show that one option setting is clearly preferable as a default, making it feasible to run Nighthawk by giving only the name of the unit under test. \item We demonstrate the value of feature subset selection (FSS) for optimizing genetic algorithms. Using FSS, we can prune many of Nighthawk's gene types while achieving nearly the same coverage. Compared to the runtimes of full Nighthawk, this coverage is achieved ten times faster. \end{enumerate} We discuss related work in Section \ref{related-work-section}. A description of our system is contained in Section \ref{system-description-section}. An empirical comparison of our work to three other published systems is in Section \ref{comparison-section}. A case study of applying Nighthawk to a real-world set of units (the Java collection and map classes) is described in Section \ref{case-study-section}; this section also contains the results of experiments we ran to determine the best default settings for two Nighthawk parameters. Gene type pruning using feature subset selection is described in Section \ref{optimizing-section}. Section \ref{threats-section} describes threats to validity, and Section \ref{conclusions-section} concludes. \section{Related Work} \label{related-work-section} \subsection{Randomized Unit Testing} ``Random'' or ``randomized'' testing has a long history, being mentioned as far back as 1973 \cite{hetzel-book-1973}; Hamlet \cite{Hamlet94randomtesting} gives a good survey. The key benefit of randomized testing is the ability to generate many distinct test inputs in a short time, including test inputs that may not be selected by test engineers but which may nevertheless force failures. There are, however, two main problems with randomized testing: the oracle problem and and the question of thoroughness. Since randomized testing depends on the generation of many inputs, it is infeasible to get a human to check all test outputs; an automated test oracle \cite{weyuker-oracles} is needed. There are two main approaches to the oracle problem. The first is to use general-purpose, ``high-pass'' oracles that pass many executions but check properties that should be true of most software. For instance, Miller et al.\ \cite{miller-fuzz-cacm} judge a randomly-generated GUI test case as failing only if the software crashes or hangs; Csallner and Smaragdakis \cite{jcrasher-spe} judge a randomly-generated unit test case as failing if it throws an exception; and Pacheco et al.\ \cite{pacheco-etal-icse2007} check general-purpose contracts for units, such as one that states that a method should not throw a ``null pointer'' exception unless one of its arguments is null. Despite the use of high-pass oracles, all these authors found randomized testing to be effective in forcing failures. % The second approach to the oracle problem for randomized testing is to write oracles in order to check properties specific to the software \cite{andrews-etal-rute-rt,ciupa-etal-icse2008}. These oracles, like all formal unit specifications, are non-trivial to write; tools such as Daikon for automatically deriving likely invariants \cite{ernst-daikon} could help here. Since randomized unit testing does not use any intelligence to guide its search for test cases, there has always been justifiable concern about how thorough it can be, given various measures of thoroughness, such as coverage and fault-finding ability. % Michael et al.\ \cite{michael-etal-ga-tcg} performed randomized testing on the well-known Triangle program; this program accepts three integers as arguments, interprets them as sides of a triangle, and reports whether the triangle is equilateral, isosceles, scalene, or not a triangle at all. They concluded that randomized testing could not achieve 50\% condition/decision coverage of the code, even after 1000 runs. % Visser et al.\ \cite{visser-etal-issta06} compared randomized unit testing with various model-checking approaches and found that while randomized testing was good at achieving block coverage, it failed to achieve optimal coverage for a measure derived from Ball's predicate coverage \cite{ball-pred-coverage}. Other researchers, however, have found that the thoroughness of randomized unit testing depends on how exactly it is implemented. % Doong and Frankl \cite{doong-frankl-tosem94} tested several units using randomized sequences of method calls, and found that by varying some parameters of the randomized testing, they could greatly increase or decrease the likelihood of finding injected faults. The parameters included number of operations performed, ranges of integer arguments, and the relative frequencies of some of the methods in the call sequence. % Antoy and Hamlet \cite{antoy-hamlet-tse-jan2000}, who checked the Java {\tt Vector} class against a formal specification using random input, similarly found that if they avoided calling some of the methods (essentially setting the relative frequencies of those methods to zero), they could cover more code in the class. % Andrews and Zhang \cite{andrews-zhang-tse2003}, performing randomized unit testing on C data structures, found that varying the ranges from which integer key and data parameters were chosen increased the fault-finding ability of the random testing. Pacheco et al.\ \cite{pacheco-etal-icse2007} show that randomized testing can be enhanced via randomized breadth-first search of the search space of possible test cases, pruning branches that lead to redundant or illegal values which would cause the system to waste time on unproductive test cases. Of the cited approaches, the approach described in this paper is most similar to Pacheco et al.'s. The primary difference is that we achieve thoroughness by generating long sequences of method calls on different receivers, while they do so by deducing shorter sequences of method calls on a smaller set of receivers. % The focus of our research is also different. Pacheco et al.\ focus on identifying contracts for units and finding test cases that violate them. In contrast, we focus on maximizing code coverage; coverage is an objective measure of thoroughness that applies regardless of whether failures have been found, for instance in situations in which most bugs have been eliminated from a unit. \subsection{Analysis-Based Test Data Generation Approaches} Approaches to test data generation via symbolic execution date back to 1976 \cite{clarke-76-testdata,king-symbolic-1976}; typically these approaches generate a thorough set of test cases by deducing which combinations of inputs will cause the software to follow given paths. TESTGEN \cite{korel-testgen}, for example, transforms each condition in the program to one of the form $e < 0$ or $e \leq 0$, and then searches for values that minimize (resp.\ maximize) $e$, thus causing the condition to become true (resp.\ false). Other source code analysis tools have applied iterative relaxation of a set of constraints on input data \cite{gupta-etal-gen-test-data} and generation of call sequences using goal-directed reasoning \cite{leow-etal-icse2004}. Some recent approaches use model checkers such as Java Pathfinder \cite{visser-etal-issta04}. These approaches are sometimes augmented with ``lossy'' randomized search for paths, as in the DART and CUTE systems \cite{godefroid-etal-dart,sen-etal-cute}, the Lurch system \cite{owen-menzies-lurch}, and the Java Pathfinder-based research of Visser et al.\ \cite{visser-etal-issta06}. Some analysis-based approaches limit the range of different conditions they consider; for instance, TESTGEN's minimization strategy \cite{korel-testgen} cannot be applied to conditions involving pointers. In addition, most analysis-based approaches incur heavy memory and processing time costs. These limitations are the primary reason why researchers have explored the use of heuristic and metaheuristic approaches to test case generation. \subsection{Genetic Algorithms for Testing} Genetic algorithms (GAs) were first described by Holland \cite{holland-ga-book}. Candidate solutions are represented as ``chromosomes'', with various solution options represented as ``genes'' in the chromosomes. The possible chromosomes form a search space and are associated with a fitness function, which typically represents how good a solution the chromosome encodes. Search proceeds by evaluating the fitness of each of a population of chromosomes, and then performing point mutations and recombination on the most successful chromosomes. GAs can defeat purely random search in finding solutions to many complex problems. Goldberg \cite{goldberg-ga-book} argues that their power stems from being able to engage in ``discovery and recombination of building blocks'' for solutions in a solution space. Meta-heuristic search methods such as GAs have often been applied to the problem of test suite generation. In Rela's review of 122 applications of meta-heuristic search in SE \cite{rela04}, 44\% of the applications related to testing. Approaches to GA test suite generation can be black-box (requirements-based) or white-box (code-based); here we focus on four representative white-box approaches, since our approach focuses on increasing coverage, and is therefore also white-box. Pargas et al.\ \cite{pargas-etal-ga-tcg} represent a set of test data as a chromosome, in which each gene encodes one input value. Michael et al.\ \cite{michael-etal-ga-tcg} represent test data similarly, and conduct experiments comparing various strategies for augmenting the GA search. Both of these approaches evaluate the fitness of a chromosome by measuring how close the input is to covering some desired statement or condition direction. Guo et al.\ \cite{guo-etal-genetic} generate unique input-output (UIO) sequences for protocol testing using a genetic algorithm; the sequence of genes represents a sequence of inputs to a protocol agent, and the fitness function computes a measure related to the coverage of the possible states and transitions of the agent. Finally, Tonella's approach to class testing \cite{tonella-issta04} represents the sequence of method calls in a unit test as a chromosome; the approach features customized mutation operators, such as one that inserts method invocations. \subsection{Analytic Comparison of Approaches} Once a large community starts comparatively evaluating some technique, then {\em evaluation methods} for different methods become just as important as the {\em generation of new methods}. %To place this comment in an historical perspective, we note that %evaluation bias is an active research area in the field of data %mining \cite{bouck03,demsar06}. %Much of our future work should hence focus on a meta-level %analysis of the advantages and %disadvantages of different assessment criteria. Currently, there are no clear conventions on how this type of work should be assessed. However, we can attempt some analytic comparisons. It is clear that there are situations in which a source code analysis-based approach such as symbolic evaluation or model checking will be superior to any randomized approach. For instance, for an {\tt if} statement decision of the form {\tt (x==742 \verb/&&/ y==113)}, random search of the space of all possible {\tt x,y} pairs is unlikely to produce a test case that executes the decision in the true direction, while a simple analysis of the source code will be successful. The question is how often these situations arise in real-world programs. The Nighthawk system of this paper cannot guess at constants like 742, but is still able to cover the true direction of decisions of the form {\tt x==y} because the value reuse policies it discovers will often choose {\tt x} and {\tt y} from the same value pool. % It is therefore likely that randomized testing and analysis-based approaches have complementary strengths. Groce et al.\ \cite{groce-etal-icse2007} conclude that randomized testing is a good first step, before model checking, in achieving high quality software. %, especially where the %existence of a reference implementation allows differential %randomized testing \cite{mckeeman-differential}. %\begin{itemize} %\item Static code analysis can direct the generation of the test % cases. Our method, on the other hand, generates test cases at % random, so it is possible that we cover some code more than is % necessary, and that parts of our value pools are useless. % Our view is that this is not a major issue since our % runtimes, on real-world systems, are quite impressive. % Nevertheless, there needs to be more discussion on how to % assess test suite generation; i.e.\ runtimes versus % superfluous tests versus any other criteria. %\end{itemize} \begin{figure} \centering %\includegraphics[width=2.5in]{myfigure.pdf} \includegraphics[width=3in]{xyPlot.pdf} \caption{Spiky search space resulting from poor fitness function.} \label{spiky-search-space-fig} \end{figure} Genetic algorithms do not perform well when the search space is mostly flat, with steep jumps in fitness score. Consider the problem of generating two integer input values $x$ and $y$ that will cover the true direction of the decision ``$x$\verb/==/$y$''. If we cast the problem as a search for the two values themselves, and the score as whether we have found two equal values, the search space is shaped as in Figure \ref{spiky-search-space-fig}: a flat plain of zero score with spikes along the diagonal. Most approaches to GA white-box test data generation address this problem with fitness functions that detect ``how close'' the target decision is to being true, often using analysis-based techniques. For instance, Michael et al.\ \cite{michael-etal-ga-tcg} use fitness functions that specifically take account of such conditions by measuring how close {\tt x} and {\tt y} are. Watkins and Hufnagel \cite{watkins-hufnagel-fitness} enumerate and compare fitness functions proposed for GA-based test case generation. \begin{figure} \centering %\includegraphics[width=2.5in]{myfigure.eps} \includegraphics[width=3in]{lohiPlot.pdf} \caption{Smooth search space resulting from recasting problem.} \label{smooth-search-space-fig} \end{figure} In contrast, in our research, we essentially recast the problem as a search for the best values of two variables $lo$ and $hi$ that will be used as the lower and upper bound for random generation of $x$ and $y$, and the score as whether we have generated two equal values of $x$ and $y$. Seen in this way, the search space landscape still contains a spiky ``cliff'', as seen in Figure \ref{smooth-search-space-fig}, but the cliff is approached on one side by a gentle slope. If the input values in Figure \ref{spiky-search-space-fig} were floating-point numbers rather than integers, then the search space would consist of a flat plain of zero score with a thin, sharp ridge along the diagonal. In this case the solution depicted in Figure \ref{smooth-search-space-fig} would yield only a small improvement. This is where value pools come in. We draw each parameter value from a value pool of finite size; each numeric value pool has some size $s$ and bounds $lo$ and $hi$, and is initially seeded with values randomly drawn from the range $lo$ to $hi$. For the example problem, we will be more likely to choose equal $x$ and $y$ as $s$ becomes smaller, regardless of the value of $lo$ and $hi$ and regardless of whether the values are integers or floating-point numbers, because the smaller the value pool, the more likely we are to pick the same value for $x$ and $y$. This approach generalizes to non-numeric data. Each type of interest is associated with one or more value pools; the number and size of the value pools are controlled by genes. At any time, a value in a value pool may be chosen as the receiver or parameter of a method call, which may in turn change the value in the pool. Also, at any time a value in a value pool may be replaced by a return value from a method call. Which value pools are drawn on for which parameters, and which value pools receive the return values of which methods, are also controlled by genes. A test case may consist of hundreds of randomized method calls, culminating in the creation of values in value pools which, when used as parameters to a method call, cause that method to execute code not executed before. The choice of gene values therefore makes this more or less likely to happen. To the best of our knowledge, each run of previous GA-based tools has resulted in a single test case, which is meant to reach a particular target. A test suite is built up by aiming the GA at different targets, resulting in a fixed-size test suite that achieves coverage of all targets. %Herein lies a potential disadvantage of such approaches. However, Frankl and Weiss \cite{frankl-weiss-tse93} have shown that both size and coverage exert an influence over test suite effectiveness, and Rothermel et al.\ \cite{rothermel-etal-effects-minimization} have shown that reducing test suite size while preserving coverage can significantly reduce its fault detection capability. Therefore, given a choice between three systems achieving the same coverage, (a) which generates one fixed set of test cases, (b) which generates many different test cases slowly, and (c) which generates many different test cases quickly, (c) is the optimal choice. % Any deterministic algorithm %for generating test cases is in class (a), whereas a A GA which generates one test case per run is in class (a) or (b) (it may generate different test cases on each run as a result of random mutation). In contrast, Nighthawk is in class (c) because each run of the GA results only in a setting of randomized testing parameters that achieves high coverage; new high-coverage test suites can then be generated quickly at low cost. % Our previous research %\cite{andrews-zhang-tse2003,andrews-ase2004} indicates that under such %conditions, randomized testing can be highly effective. %\begin{center} %\begin{tabular}{c|ccccccc|ccccc} %$x|y$ & 1 & 2 & 3 & 4 & 5 & ~~~ & %$lo|hi$ & 1 & 2 & 3 & 4 & 5 \\ %\cline{1-6} \cline{8-13} %1 & 1.0 & .00 & .00 & .00 & .00 & & %1 & 1.0 & .50 & .33 & .25 & .20 \\ %2 & .00 & 1.0 & .00 & .00 & .00 & & %2 & .00 & 1.0 & .50 & .33 & .25 \\ %3 & .00 & .00 & 1.0 & .00 & .00 & & %3 & .00 & .00 & 1.0 & .50 & .33 \\ %4 & .00 & .00 & .00 & 1.0 & .00 & & %4 & .00 & .00 & .00 & 1.0 & .50 \\ %5 & .00 & .00 & .00 & .00 & 1.0 & & %5 & .00 & .00 & .00 & .00 & 1.0 \\ %\end{tabular} %\end{center} % %\caption{Chance of selecting two identical integers $x$ and $y$. %(left) Search space as space of $(x,y)$ pairs. %(right) Search space as space of lower and upper bounds %for random generation of $x$ and $y$.} %\label{search-space-fig} %\end{figure*} All analysis-based approaches share the disadvantage of requiring a robust parser and source code analyzer that can be updated to reflect changes in the source language. % Such tools are not often %provided by language providers. As an example from the domain of formal specification, Java 1.5 was released in 2004, but as of this writing, the widely-used specification language JML does not fully support Java 1.5 features \cite{cok-adapting-jml}. Our approach does not require source code or bytecode analysis, instead depending only on class and method parameter information (such as that supplied by the robust Java reflection mechanism) and commonly-available coverage tools. For instance, our code was initially written with Java 1.4 in mind, but worked seamlessly on the Java 1.5 versions of the {\tt java.util} classes, despite the fact that the source code of many of the units had been heavily modified to introduce templates. However, model-checking approaches have other strengths, such as the ability to analyze multi-threaded code \cite{havelund-pathfinder}, further supporting the conclusion that the two approaches are complementary. % Look up Baresel paper BSS02!! %We also generalize the problem domain from the heuristic to the %meta-heuristic by adding the GA level. % %\section{Exploratory Study} %\label{exploratory-section} % %To test the merit of a %genetic-random system, we conducted an exploratory study. %In this section, we describe the prototype software we developed, %the design of the study and its results. % %\subsection{Software Developed} % %Using code from RUTE-J (see above) and the open-source genetic %algorithm package JDEAL \cite{jdeal}, we constructed a prototype %two-level genetic-random unit testing system that took Java %classes as its testing units. For each unit under test (UUT) with %$n$ methods to call, the GA level constructed a chromosome with %$2+n$ integer genes; these genes represented %the number of method calls to make in each %test case, the number of test cases to generate, and the %relative weights (calling frequencies) of the $n$ methods. All %other randomized testing parameters %were hard-coded in the test wrappers. % %The evaluation of the fitness of each chromosome $c$ proceeded as %follows. We got the random testing level to generate the number %of test cases of the length specified in $c$, using %the method weights specified in $c$. We then %measured the number of coverage points covered using %Cobertura \cite{cobertura-website}, %which measures line coverage. If we had based the %fitness function {\it only} on coverage, however, then any chromosome %would have benefitted from having a larger number of %method calls and test cases, since every new method call has the %potential of covering more code. %We therefore built in a brake to prevent these values from %getting unfeasibly high. We calculated the fitness function %as: %\begin{center} %(number of coverage points covered) $*$ (coverage factor) \\ % $-$ (number of method calls performed overall) %\end{center} %We set the coverage factor to 1000, meaning that we were willing %to make 1000 more method calls (but not more) if that meant %covering one more coverage point. % %\subsection{Experiment Design} % %For subject programs, we used three units from the Java %1.4.2 edition of {\tt java.util}: {\tt BitSet}, {\tt HashMap} %and {\tt TreeMap}. These units are widely used, %and {\tt TreeMap} had been used in the %experiments of other researchers \cite{visser-etal-issta04}. %For each UUT, we wrote a %test wrapper class containing methods that called selected %target methods of the UUT (16 methods for BitSet, 8 for HashMap %and 9 for TreeMap). %Each wrapper contained a simple oracle for checking correctness. %We instrumented each UUT using Cobertura. % %We ran the two-level algorithm 30 times on each of the three %test wrappers, and recorded the amount of time taken and the %parameters in the final chromosome. %To test whether the weights in the chromosomes were %useful given the length and number of method calls, for each %final chromosome $c$ we created a variant chromosome $c'$ %with the same length and number of method calls but with all %weights equal. We then compared the coverage achieved by %$c$ and $c'$ on 30 paired trials. Full results from the %experiment are available in \cite{cli-msc}. % %\subsection{Results} % %We performed two statistical tests to evaluate whether the %system was converging on a reasonable solution. First, we %ordered the average weights discovered for each method in each %class, and performed a $t$ test %with Bonferroni correction between each pair of %adjacent columns. %We found that for the {\tt HashMap} and {\tt TreeMap} units, the %{\tt clear} method (which removes all data from the map) had a %statistically significantly lower weight than the other methods, %indicating that the algorithm was consistently converging on a %solution in which it had a lower weight. %This is because much of the code in these %units can be executed only when there is a large amount of data %in the container objects. Since the {\tt clear} method clears %out all the data, executing it infrequently ensured that %the objects would get large enough. % %We also found that for the {\tt TreeMap} unit, the {\tt remove} %and {\tt put} methods had a statistically significantly higher %weight than the other methods. This is explainable by the large %amount of complex code in these methods and the private methods %that they call; it takes more calls to %cover this code than it does for the simpler code of %the other methods. Another reason is that sequences of {\it put} %and {\it remove} were needed to create data structures through %which code in some of the other methods was accessible. % %The second statistical test we performed tested %whether the weights found by the GA were efficient. %For this, we used the 30 trials comparing the discovered %chromosome $c$ and the equal-weight variant $c'$. We found that %for all three units, the equal-weight chromosome covered less %code than the %original, to a statistically significant level (as measured by a %$t$ test with $\alpha=0.05$). This can be interpreted as %meaning that the GA was correctly choosing a good {\it combination} %of parameters. % %In the course of the experiment, we found a bug in the Java %1.4.2 version of {\tt BitSet}: when a call to {\tt set()} is %performed on a range of bits of length 0, the unit could later %return an incorrect ``length'' for the {\tt BitSet}. %We found that a bug report for this bug %had already been submitted to Sun's bug database. It has been %corrected in the Java 1.5.0 version of the library. % %In summary, the experiment indicated that the two-level %algorithm was potentially useful, and was consistently %converging on similar solutions that were more optimal than %calling all methods equally often. \section{Nighthawk: System Description} \label{system-description-section} Our early exploratory studies~\cite{andrews07} suggested that GAs for unit test generation should search method parameter ranges, value reuse policy and other randomized testing parameters. This section describes Nighthawk's implementation of that search. We first outline the lower, randomized-testing, level of Nighthawk, and then describe the chromosome that controls its operation. We then describe the genetic-algorithm level and the end user interface. Finally, we describe the use of automatically-generated test wrappers for precondition checking, result evaluation and coverage enhancement. \begin{figure} \centering \includegraphics[width=3in]{valuepools.pdf} \caption{ High-level view of value pool initialization and use. In stage 1), random values are seeded into the value pools for primitive types such as {\tt int}, according to bounds associated with the pools. In stage 2), values are seeded into non-primitive type classes that have initializer constructors, by calling those constructors. In stage 3), the rest of the test case is constructed and run, by repeatedly randomly choosing a method and receiver and parameter values. Each method call may also result in a return value, which is placed back into a value pool (not shown). } \label{valuepools-fig} \end{figure} \subsection{Randomized Testing Level} Nighthawk's lower level constructs and runs one test case. The algorithm takes two parameters: a set $M$ of Java methods, and a GA chromosome $c$ appropriate to $M$. The chromosome controls aspects of the algorithm's behaviour, such as the number of method calls to be made, and will be described in more detail in the next subsection. We say that $M$ is the set of ``target methods''. $I_M$, the {\it types of interest} corresponding to $M$, is the union of the following sets of types\footnote{ In this paper, the word ``type'' refers to any primitive type, interface, or abstract or concrete class. }: \begin{itemize} \item All types of receivers, parameters and return values of methods in $M$. %\item All types of parameters of methods in $M$. %\item All types of return values of methods in $M$. \item All primitive types that are the types of parameters to constructors of other types of interest. \end{itemize} Each type $t \in I_M$ is associated with an array of {\it value pools}, and each value pool for $t$ contains an array of values of type $t$. Each value pool for a range primitive type (a primitive type other than {\tt boolean} and {\tt void}) has bounds on the values that can appear in it. The number of value pools, number of values in each value pool, and the range primitive type bounds are specified by the chromosome $c$. See Figure \ref{valuepools-fig} for a high-level view of how the value pools are initialized and used in the test case generation process. The algorithm chooses initial values for primitive type pools, before considering non-primitive type pools. A constructor method is an {\it initializer} if it has no parameters, or if all its parameters are of primitive types. A constructor is a {\it reinitializer} if it has no parameters, or if all its parameters are of types in $I_M$. (All initializers are also reinitializers.) We define the set $C_M$ of {\it callable methods} to be the methods in $M$ plus the reinitializers of the types in $I_M$ (Nighthawk calls these {\it callables} directly). A {\it call description} is an object representing one method call that has been constructed and run. It consists of the method name, an indication of whether the method call succeeded, failed or threw an exception, and one {\it object description} for each of the receiver, the parameters and the result (if any). %The object description of an object of primitive type or a %string consists of the object itself. The object description of %other objects consists of the class of interest, the value pool %number and the value number associated with the object; this is %the source from which the object came (for receivers and %parameters) or the place where the value was put (for results). A {\it test case} is a sequence of call descriptions, together with an indication of whether the test case succeeded or failed. \begin{figure} {\scriptsize Input: a set $M$ of target methods; a chromosome $c$. \\ Output: a test case. \\ Steps: \begin{enumerate} \item For each element of each value pool of each primitive type in $I_M$, choose an initial value that is within the bounds for that value pool. \item For each element of each value pool of each other type $t$ in $I_M$: \begin{enumerate} \item If $t$ has no initializers, then set the element to {\tt null}. \item Otherwise, choose an initializer method $i$ of $t$, and call {\sf tryRunMethod}$(i, c)$. If the call returns a non-null value, place the result in the destination element. \end{enumerate} \item Initialize test case $k$ to the empty test case. \item Repeat $n$ times, where $n$ is the number of method calls to perform: \begin{enumerate} \item Choose a target method $m \in C_M$. \item Run {\sf tryRunMethod}$(m, c)$. Add the returned call description to $k$. \item If {\sf tryRunMethod} returns a method call failure indication, return $k$ with a failure indication. \end{enumerate} \item Return $k$ with a success indication. \end{enumerate} } \caption{Algorithm {\sf constructRunTestCase}.} \label{constructRunTestCase-fig} \end{figure} \begin{figure}[!t] {\scriptsize Input: a method $m$; a chromosome $c$. \\ Output: a call description. \\ Steps: \begin{enumerate} \item If $m$ is non-static and not a constructor: \begin{enumerate} \item Choose a type $t \in I_M$ which is a subtype of the receiver of $m$. \item Choose a value pool $p$ for $t$. \item Choose one value $recv$ from $p$ to act as a receiver for the method call. \end{enumerate} \item For each argument position to $m$: \begin{enumerate} \item Choose a type $t \in I_M$ which is a subtype of the argument type. \item Choose a value pool $p$ for $t$. \item Choose one value $v$ from $p$ to act as the argument. \end{enumerate} \item If the method is a constructor or is static, call it with the chosen arguments. Otherwise, call it on $recv$ with the chosen arguments. \item If the call throws {\tt AssertionError}, return a failure indication call description. \item Otherwise, if the call threw another exception, return a call description with an exception indication. \item Otherwise, if the method return is not {\tt void}, \& the return value $ret$ is non-null: \begin{enumerate} \item Choose type $t \in I_M$ that is a supertype of the the return value. \item Choose a value pool $p$ for $t$. \item If $t$ is not a primitive type, or if $t$ is a primitive type and $ret$ does not violate the $p$ bounds, then replace an element of $p$ with $ret$. \item Return a call description with a success indication. \end{enumerate} \end{enumerate} } \caption{Algorithm {\sf tryRunMethod}.} \label{tryRunMethod-fig} \end{figure} Nighthawk's randomized testing algorithm is referred to as {\sf constructRunTestCase}, and is described in Figure \ref{constructRunTestCase-fig}. It takes a set $M$ of target methods and a chromosome $c$ as inputs. It begins by initializing value pools, and then constructs and runs a test case, and returns the test case. It uses an auxiliary method called {\sf tryRunMethod} (Figure \ref{tryRunMethod-fig}), which takes a method as input, calls the method and returns a call description. In the algorithm descriptions, the word ``choose'' is always used to mean specifically a random choice which may partly depend on $c$. {\sf tryRunMethod} considers a method call to fail if and only if it throws an {\tt AssertionError}. It does not consider other exceptions to be failures, since they might be correct responses to bad input parameters. We facilitate checking correctness of return values and exceptions by providing a generator for ``test wrapper'' classes. The generated test wrapper classes can be instrumented with assertions; see Section \ref{test-wrappers-section} for more details. Return values may represent new object instances never yet created during the running of the test case. If these new instances are given as arguments to method calls, they may cause the method to execute statements never yet executed. Thus, the return values are valuable and are returned to the value pools when they are created. For conciseness, the algorithm descriptions omit some details which we now fill in. These concern % the treatment of nulls, the treatment of {\tt String} and the treatment of {\tt Object}. % %The receiver of a method call cannot be null. No parameter %can be null unless {\sf tryRunMethod} chooses it to be. If %{\sf tryRunMethod} cannot find a non-null value, %it reports failure of the {\it attempt} to call the %method. {\sf constructRunTestCase} tolerates a certain number %of these attempt failures before terminating the %test case generation process. % {\tt java.lang.String} is treated as if it is a primitive type, the values in the value pools being initialized with ``seed strings''. Some default seed strings are supplied by the system, and the user can supply more. % \label{object-section} Formal parameters of type {\tt java.lang.Object} stand for some arbitrary object, but it is usually sufficient to use a small number of specific types as actual parameters; Nighthawk uses only {\tt int} and {\tt String} by default. A notable exception to this rule is the parameter to the {\tt equals()} method, which can be treated specially by test wrapper objects (see Section \ref{test-wrappers-section}). Although we have targeted Nighthawk specifically at Java, note that its general principles apply to any object-oriented or procedural language. For instance, for C, we would need only information about the types of parameters and return values of functions, and the types of fields in {\tt struct}s. {\tt struct} types and pointer types could be treated as classes with special constructors, getters and setters; functions could be treated as static methods of a single target class. \subsection{Chromosomes} \begin{figure*} {\footnotesize \begin{center} \begin{tabular}{|l|l|c|l|} Gene type & Occurrence & Type & Description \\ \hline \parbox[t]{1.3in}{\tt numberOfCalls} & \parbox[t]{2in}{One for whole chromosome} & int & \parbox[t]{2in}{the number $n$ of method calls to be made\vspace{1mm}} \\ % that % {\sf constructRunTestCase} is to run} \\ \hline \parbox[t]{1.3in}{\tt methodWeight} & \parbox[t]{2in}{One for each method $m \in C_M$} & int & \parbox[t]{2in}{The relative weight of the method, i.e.\ the likelihood that it will be chosen\vspace{1mm}} \\ % at step 4(a) % of {\sf constructRunTestCase}} \\ \hline \parbox[t]{1.3in}{\tt numberOf- ValuePools} & \parbox[t]{2in}{One for each type $t \in I_M$} & int & \parbox[t]{2in}{The number of value pools for that type\vspace{1mm}} \\ \hline \parbox[t]{1.3in}{\tt numberOfValues} & \parbox[t]{2in}{One for each value pool of each type $t \in I_M$ except for {\tt boolean}\vspace{1mm}} & int & \parbox[t]{2in}{The number of values in the pool} \\ \hline \parbox[t]{1.3in}{\tt chanceOfTrue} & \parbox[t]{2in}{One for each value pool of type {\tt boolean}} & int & \parbox[t]{2in}{The percentage chance that the value {\it true} will be chosen from the value pool\vspace{1mm}} \\ \hline \parbox[t]{1.3in}{\tt lowerBound, upperBound} & \parbox[t]{2in}{One for each value pool of each range primitive type $t \in I_M$} & \parbox[t]{0.3in}{int or float} & \parbox[t]{2in}{Lower and upper bounds on pool values; initial values are drawn uniformly from this range} \\ \hline \parbox[t]{1.3in}{\tt chanceOfNull} & \parbox[t]{2in}{One for each argument position of non-primitive type of each method $m \in C_M$} & int & \parbox[t]{2in}{The percentage chance that {\tt null} will be chosen as the argument\vspace{1mm}} \\ \hline \parbox[t]{1.3in}{\tt candidateBitSet} & \parbox[t]{2in}{One for each parameter and quasi-parameter of each method $m \in C_M$} & BitSet & \parbox[t]{2in}{Each bit represents 1 candidate type, signifying if the argument will be of that type\vspace{1mm}} \\ \hline \parbox[t]{1.3in}{\tt valuePool- ActivityBitSet} & \parbox[t]{2in}{One for each candidate type of each parameter and quasi-parameter of each method $m \in C_M$} & BitSet & \parbox[t]{2in}{Each bit represents one value pool, and signifies whether the argument will be drawn from that value pool\vspace{1mm}} \\ \hline \end{tabular} \end{center} } \caption{Nighthawk gene types.} \label{gene-types-fig} \end{figure*} Aspects of the test case execution algorithms are controlled by the genetic algorithm chromosome given as an argument. A {\it chromosome} is composed of a finite number of {\it genes}. Each gene is a pair consisting of a name and an integer, floating-point, or {\tt BitSet} value. Figure \ref{gene-types-fig} summarizes the different types of genes that can occur in a chromosome. We refer to the receiver (if any) and the return value (if non-{\tt void}) of a method call as {\it quasi-parameters} of the method call. Parameters and quasi-parameters have {\it candidate types}: \begin{itemize} \item A type is a {\it receiver candidate type} if it is a subtype of the type of the receiver. These are the types from whose value pools the receiver can be drawn. \item A type is a {\it parameter candidate type} if it is a subtype of the type of the parameter. These are the types from whose value pools the parameter can be drawn. \item A type is a {\it return value candidate type} if it is a supertype of the type of the return value. These are the types into whose value pools the return value can be placed. \end{itemize} Note that the gene types {\tt candidateBitSet} and {\tt valuePoolActivityBitSet} encode value reuse policies by determining the pattern in which receivers, arguments and return values are drawn from and placed into value pools. %Every Nighthawk chromosome contains a gene specifying the number %$n$ of method calls that {\sf constructRunTestCase} is to %run. In addition, a chromosome appropriate to a set $M$ %of target methods contains the following genes: %\begin{itemize} %%\item The number $n$ of method calls to perform. %\item For each method in $C_M$, the relative weight of the % method, i.e.\ the likelihood that it will be chosen at step % 4(a) of {\sf constructRunTestCase}. %\item For each type of interest in $I_M$, the number of value % pools for that type. %\item For each value pool, the number of values in the pool. %\item For each value pool of a range primitive type, the upper % and lower bounds on values in the pool. Initial values are % drawn from this range with a uniform distribution. %\item For each method in $C_M$ and every argument position, the % chance that {\tt null} will be chosen as an argument. %\item For each method in $C_M$, the types of interest that will % be chosen from to find a receiver, and, for each of those % types, the value pools that will be chosen from. %\item For each method in $C_M$ and every argument position, the % types of interest that will be chosen from to find an % argument, and, for each of those types, the value pools % that will be chosen from. %\item For each method in $C_M$, the types of interest that will % be chosen from to find an element that will be replaced by % the return value, % and, for each of those types, the value pools that will be % chosen from. %\end{itemize} %The last three kinds of genes are expressed as bit vectors; %each bit stands for one of the types of interest that is a %subtype of the declared type (resp.\ one of the value pools). %These bit vectors thus encode the value reuse policy %expressed by the chromosome. It is clear that different gene values in the chromosome may cause dramatically different behavior of the algorithm on the methods. We illustrate this point with two concrete examples. Consider the ``triangle'' unit from \cite{michael-etal-ga-tcg}. If the value pool for all three parameter values contains 65536 values in the range -32768 to 32767, then the chance that the algorithm will ever choose two or three identical values for the parameters (needed for the ``isosceles'' and ``equilateral'' cases) is very low. If, on the other hand, the value pool contains only 30 integers each chosen from the range 0 to 10, then the chance rises dramatically due to reuse of previously-used values (the additional coverage this would give would depend on the SUT, but is probably $>0$). Consider further a container class with {\tt put} and {\tt remove} methods, each taking an integer key as its only parameter. If the parameters to the two methods are taken from two different value pools of 30 values in the range 0 to 1000, there is little chance that a key that has been put into the container will be successfully removed. If, however, the parameters are taken from a single value pool of 30 values in the range 0 to 1000, then the chance is very good that added keys will be removed, again due to value reuse. A {\tt remove} method for a typical data structure executes different code for a successful removal than it does for a failing one. \subsection{Genetic Algorithm Level} We take the space of possible chromosomes as a solution space to search, and apply the GA approach to search it for a good solution. We chose GAs over other metaheuristic approaches such as simulated annealing because of our belief that recombining parts of successful chromosomes would result in chromosomes that are better than their parents. However, other metaheuristic approaches may have other advantages and should be explored in future work. % For the randomized %unit testing problem, the ``building blocks'' are individual %choices for the gene values; discovering and recombining good %building blocks leads to better overall solutions. The parameter to Nighthawk's GA is the set $M$ of target methods. The GA performs the usual steps of chromosome evaluation, fitness selection, mutation and recombination. The GA first derives an initial template chromosome appropriate to $M$, constructs an initial population of size $p$ as clones of this chromosome, and mutates the population. It then performs a loop, for the desired number $g$ of generations, of evaluating each chromosome's fitness, retaining the fittest chromosomes, discarding the rest, cloning the fit chromosomes, and mutating the genes of the clones with probability $m$\% using point mutations and crossover (exchange of genes between chromosomes). The evaluation of the fitness of each chromosome $c$ proceeds as follows. Nighthawk gets the random testing level to generate and run a test case, using the parameters encoded in $c$. It then collects the number of lines covered by the test case. If we based the fitness function {\it only} on coverage, then any chromosome would benefit from having a larger number of method calls and test cases, since every new method call has the potential of covering more code. % We therefore built in a brake %to prevent these values from getting unfeasibly high. Nighthawk therefore calculates the fitness of the chromosome as: \begin{center} (number of coverage points covered) $*$ (coverage factor) \\ $-$ (number of method calls performed overall) \end{center} We set the coverage factor to 1000, meaning that we are willing to make 1000 more method calls (but not more) if that means covering one more coverage point. %It is recognized that the design of genetic algorithms is a %``black art'' \cite{ga-blackart}, and that very little is known %about why GAs work when they do work and why they do not work %when they do not. For the three variables mentioned above, Nighthawk uses default settings of $p=20, g=50, m=20$. These settings are different from those taken as standard in GA literature \cite{dejong-spears-genetic}, and are motivated by a need to do as few chromosome evaluations as possible (the primary cost driver of the system). The population size $p$ and the number of generations $g$ are smaller than standard, resulting in fewer chromosome evaluations; to compensate for the lack of diversity in the population that would otherwise result, the mutation rate $m$ is larger. The settings of other variables, such as the retention percentage, are consistent with the literature. To enhance availability of the software, Nighthawk uses the popular open-source coverage tool Cobertura \cite{cobertura-website} to measure coverage. Cobertura can measure only line coverage (each coverage point corresponds to a source code line, and is covered if any code on the line is executed). % \footnote{ % Cobertura (v.\ 1.8) also reports what it calls ``decision coverage'', % but this is coverage of lines containing decisions. %}. However, Nighthawk's algorithm is not specific to this measure; indeed, our empirical studies (see below) show that Nighthawk performs well when using other coverage measures. \subsection{Top-Level Application} \label{deep-section} The Nighthawk application takes several switches and a set of class names as command-line parameters. The default behavior is to consider the command-line class names as a set of ``target classes''. If, however, the ``{\tt --deep}'' switch is given to Nighthawk, the public declared methods of the command-line classes are explored, and all non-primitive types of parameters and return values of those methods are added to the set of target classes. The set $M$ of target {\it methods} is computed as all public declared methods of the target {\it classes}. Intuitively, therefore, the {\tt --deep} switch performs a ``deep target analysis'' by getting Nighthawk to call methods in the layer of classes surrounding the command-line classes. Nighthawk runs the GA, monitoring the chromosomes and retaining the first chromosome that has the highest fitness ever encountered. This most fit chromosome is the final output of the program. % \label{run-chromosome-sec} After finding the most fit chromosome, test engineers apply the specified randomized test. To do this, they run a separate program, RunChromosome, which takes the chromosome description as input and runs test cases based on it for a user-specified number of times. Randomized unit testing generates new test cases with new data every time it is run, so if Nighthawk finds a parameter setting that achieves high coverage, a test engineer can automatically generate a large number of distinct, high-coverage test cases with RunChromosome. \subsection{Test Wrappers} \label{test-wrappers-section} We provide a utility program that, given a class name, generates the Java source file of a ``test wrapper'' class. Running Nighthawk on an unmodified test wrapper is the same as running it on the target class; however, test wrappers can be customized for precondition checking, result checking or coverage enhancement. % A test wrapper for class X is a class with one private field of class X (the ``wrapped object''), and one public method with an identical declaration for each public declared method of class X. Each wrapper method passes calls to the wrapped object. To improve test wrapper precondition checking, users can add checks in a wrapper method before the target method call. If the precondition is violated, the wrapper method just returns. To customize a test wrapper for test result checking, the user can insert any result-checking code after the target method call; examples include normal Java assertions and JML \cite{jml-overview} contracts. %For instance, a user can choose to customize a wrapper method so %that it throws an {\tt AssertionError} (signaling test case failure) %whenever the target method throws an {\tt ArrayOutOfBoundsException}. We offer switches to the test wrapper generation program that cause the wrapper to check commonly-desired properties, such as that a method throws no {\tt NullPointerException} unless one of its arguments is null. The switch \verb/--pleb/ generates a wrapper that checks all the Java Exception and Object contracts from Pacheco et al.\ \cite{pacheco-etal-icse2007}. To improve test wrapper coverage, users may add methods to execute extra code. The switch \verb/--checkTypedEquals/ adds a method to the test wrapper for class X that takes one argument of type X and passes it to the {\tt equals} method of the wrapped object. This differs from normal wrapper methods that call {\tt equals}, which have an argument of type {\tt Object} and would therefore by default receive arguments only of type {\tt int} or {\tt String} (see Section \ref{object-section}). For classes that override the {\tt equals} method, the typed-equals method executes more code. Tailored serialization is accomplished in Java via specially-named private methods that are inaccessible to Nighthawk. The test wrapper generation program switch \verb/--checkSerialization/ adds a method to the test wrapper that writes the object to a byte array and reads it back again. This causes Nighthawk to be able to execute the code in the serialization methods. \section{Comparison with Previous Results} \label{comparison-section} We compared Nighthawk with three well-documented systems in the literature by running it on the same software and measuring the results. \subsection{Pure GA Approach} To compare our genetic-random approach against the purely genetic approach of Michael et al.\ \cite{michael-etal-ga-tcg}, we ported their C code for the Triangle program to Java, transforming each decision so that each condition and decision direction corresponded to an executable line of code measurable by Cobertura. Nighthawk was then run 10 times on the resulting class. %6782 gen 8 %6914 gen 10 %6527 gen 10 %5008 gen 7 %3346 gen 5 %1790 gen 2 %2486 gen 5 %7945 gen 13 %18363 gen 21 %2509 gen 4 We found that Nighthawk reached 100\% of feasible condition/decision coverage on average after 8.5 generations (sample standard deviation: 5.5, 95\% confidence interval for mean: (4.6, 12.4)), in an average of 6.2 seconds of clock time (sample standard deviation: 4.8, 95\% confidence interval for mean: (2.7, 9.6))\footnote{ All empirical studies in this paper were performed on a Sun UltraSPARC-IIIi running SunOS 5.10 and Java 1.5.0\_11. }. %There were 22 decisions having a total of 28 condition %and decision directions. %All condition and decision directions were coverable %except the false direction of the last ``if'' statement; Michael %et al.\ found that their best method also achieved 95\% coverage. % Michael et al.\ had found that a purely random approach could not achieve even 50\% condition/decision coverage. The discrepancy between the results may be due to Nighthawk being able to find a setting of the randomized testing parameters that is more optimal than the one Michael et al.\ were using. Inspection revealed that the chromosomes encoded value reuse policies that guaranteed frequent selection of the same values. \subsection{Model-Checking and Feedback-Directed Randomization} To compare our method against the model-checking approach of Visser et al.\ \cite{visser-etal-issta06} and the feedback-directed random testing of Pacheco et al.\ \cite{pacheco-etal-icse2007}, we downloaded the data structure units used in those studies (these had been hand-instrumented to record coverage of the deepest basic blocks in the code). % We first wrote restricted test wrapper classes that called only the methods used by previous researchers. We ran Nighthawk giving these test wrapper classes as command-line classes, and observed the number of instrumented basic blocks covered, and the number of lines covered as measured by Cobertura. \begin{figure} {\footnotesize \begin{center} \begin{tabular}{|c|c|c|c|c|c|c|} \hline & \multicolumn{3}{c|}{Instr Blk Cov} & Time & \multicolumn{2}{c|}{Line Cov} \\ UUT & JPF & RP & NH & (sec) & Restr & Full \\ \hline BinTree & .78 & .78 & .78 & .58 & .84 & 1 \\ \hline BHeap & .95 & .95 & .95 & 4.1 & .88 & .92 \\ \hline FibHeap & 1 & 1 & 1 & 5.1 & .74 & .92 \\ \hline TreeMap & .72 & .72 & .72 & 5.4 & .76 & .90 \\ \hline \end{tabular} \end{center} } \caption{Comparison of results on the JPF subject units.} \label{jpf-results-fig} \end{figure} Figure \ref{jpf-results-fig} shows the results of the comparison. We show the block coverage ratio achieved by the best Java-Pathfinder-based technique from Visser et al.\ (JPF), by Pacheco et al.'s tool Randoop (RP), and by Nighthawk using the restricted test wrappers. Nighthawk was able to achieve the same coverage as the previous tools. The Time column shows the clock time in seconds needed by Nighthawk to achieve its greatest coverage. For BHeap and FibHeap, Nighthawk runs more quickly than JPF, but for the other two units it runs more slowly than both JPF and Randoop. This difference in run times may be in part because Nighthawk relies on general-purpose Cobertura instrumentation, which slows down programs, rather than the efficient, but application-specific, hand instrumentation that the other methods used (it may also be in part due to our use of a different machine architecture than Pacheco et al.). The JPF subject units were run by the other researchers by calling only selected methods. In order to study how Nighthawk performed when not restricted to the selected methods, we then ran it giving the target classes themselves as command-line classes (bypassing the test wrappers), and observed the number of lines covered. The ``Line Cov'' columns show the line coverage ratio achieved when using the restricted wrappers and on the full target classes. Using the full target classes, Nighthawk could cover more lines of code, including all the blocks covered by the previous studies. \begin{figure} %\begin{center} %\begin{tabular}{|c|c|c|c|} %\hline % & \multicolumn{3}{c|}{Number of cond value combinations} \\ %UUT & Total & Unreached & Rch/Uncov \\ %\hline %BinTree & 34 & 6 & 0 \\ %\hline %BHeap & 75 & 0 & 5 \\ %\hline %FibHeap & 57 & 10 & 3 \\ %\hline %TreeMap & 157 & 31 & 19 \\ %\hline %\end{tabular} %\end{center} {\footnotesize \begin{center} \begin{tabular}{|c|c|c|c|} \hline & \multicolumn{3}{c|}{Number of cond value combinations} \\ UUT & Total & Reachable & Covered \\ \hline BinTree & 34 & 28 & 28 (.82, 1.0) \\ \hline BHeap & 75 & 75 & 70 (.93, .93) \\ \hline FibHeap & 57 & 47 & 44 (.77, .94) \\ \hline TreeMap & 157 & 126 & 107 (.68, .85) \\ \hline \end{tabular} \end{center} } \caption{Multiple condition coverage of the subject units.} \label{jpf-mcc-results-fig} \end{figure} Visser et al.\ and Pacheco et al.\ also studied a form of predicate coverage \cite{ball-pred-coverage} whose implementation is linked to the underlying Java Pathfinder code, and is difficult for Nighthawk to access. While this predicate coverage is an interesting assessment criterion, there is no consensus in the literature on the connection of this criterion to other measures. For comparison, we therefore studied multiple condition coverage (MCC), a standard coverage metric which is, like predicate coverage, intermediate in strength between decision/condition and path coverage. We instrumented the source code so that every combination of values of conditions in every decision caused a separate line of code to be executed. We then ran Nighthawk on the test wrappers, thus effectively causing it to optimize MCC rather than just line coverage. In Figure \ref{jpf-mcc-results-fig}, we list the total number of valid condition value combinations in all the code, and the number that were in decisions reachable by calling only the methods called by the other research groups. We also show the combinations covered by Nighthawk, both as a raw total and as a fraction of the total combinations and the reachable combinations. Nighthawk achieved between 68\% and 93\% of MCC, or between 85\% and 100\% when only reachable condition combinations were considered. One coverage tool vendor website \cite{cornett-minimum-coverage} states that ``code coverage of 70-80\% is a reasonable goal for system test of most projects with most coverage metrics'', and suggests 90\% coverage during unit testing. Berner et al.\ \cite{berner-etal-icse07}, reporting on a study of the commercial use of unit testing on Java software, report unit test coverage of no more than 85\% over the source code of the whole system, on a variety of coverage measures. We therefore consider the coverage levels achieved by Nighthawk to be very good. In summary, our comparisons show Nighthawk achieving good coverage with respect to the results achieved by previous researchers, even when strong coverage measures such as decision/condition and MCC were taken into consideration. \section{Case Study} \label{case-study-section} In order to study the effects of different test wrapper generation and command-line switches to Nighthawk, we studied the Java 1.5.0 Collection and Map classes; these are the 16 concrete classes with public constructors in {\tt java.util} that inherit from the {\tt Collection} or {\tt Map} interface. The source files total 12137 LOC, and Cobertura reports that 3512 of those LOC contain executable code. These units are ideal subjects because they are heavily used and contain complex code, including templates and inner classes. For each unit, we generated test wrappers of two kinds: plain test wrappers (P), and enriched wrappers (E) generated with the {\tt --checkTypedEquals} and {\tt --checkSerializable} switches (see Section \ref{test-wrappers-section}). We studied two different option sets for Nighthawk: with no command-line switches (N), and with the {\tt --deep} switch (see Section \ref{deep-section}) turned on (D). For each $\langle$UUT, test wrapper, option set$\rangle$ triple, we ran Nighthawk for 50 generations and saved the best chromosome it found. For each triple, we then executed RunChromosome (see Section \ref{run-chromosome-sec}) specifying that it generate 10 test cases with the given chromosome, and we measured the coverage achieved. \begin{figure} {\footnotesize \begin{center} \begin{tabular}{|l|c|c|c|c|c|} Source file & SLOC & PN & EN & PD & ED \\ \hline ArrayList & 150 & 111 & 140 & 109 & 140 (.93) \\ \hline EnumMap & 239 & 7 & 9 & 10 & 7 (.03) \\ \hline HashMap & 360 & 238 & 265 & 305 & 347 (.96) \\ \hline HashSet & 46 & 24 & 40 & 26 & 44 (.96) \\ \hline Hashtable & 355 & 205 & 253 & 252 & 325 (.92) \\ \hline IHashMap & 392 & 156 & 196 & 283 & 333 (.85) \\ \hline LHashMap & 103 & 27 & 37 & 28 & 96 (.93) \\ \hline LHashSet & 9 & 6 & 6 & 7 & 9 (1.0) \\ \hline LinkedList & 227 & 156 & 173 & 196 & 225 (.99) \\ \hline PQueue & 203 & 98 & 123 & 140 & 155 (.76) \\ \hline Properties & 249 & 101 & 102 & 102 & 102 (.41) \\ \hline Stack & 17 & 17 & 17 & 17 & 17 (1.0) \\ \hline TreeMap & 562 & 392 & 415 & 510 & 526 (.94) \\ \hline TreeSet & 62 & 44 & 59 & 41 & 59 (.95) \\ \hline Vector & 200 & 183 & 184 & 187 & 195 (.98) \\ \hline WHashMap & 338 & 149 & 175 & 274 & 300 (.89) \\ \hline \hline Total & 3512 & 1914 & 2194 & 2487 & 2880 \\ \hline Ratio & & .54 & .62 & .71 & .82 \\ \hline \end{tabular} \end{center} } \caption{ Coverage achieved by configurations of Nighthawk on the {\tt java.util} Collection and Map classes. } \label{collection-map-cov} \end{figure} \begin{figure} {\footnotesize \begin{center} \begin{tabular}{|l|c|c|c|c|c|c|} Source file & PN & EN & PD & ED & RC \\ \hline ArrayList & 75 & 91 & 29 & 48 & 15 \\ \hline EnumMap & 3 & 9 & 6 & 5 & 8 \\ \hline HashMap & 63 & 37 & 136 & 176 & 30 \\ \hline HashSet & 25 & 29 & 27 & 39 & 22 \\ \hline Hashtable & 8 & 110 & 110 & 157 & 25 \\ \hline IHashMap & 31 & 41 & 59 & 134 & 34 \\ \hline LHashMap & 1 & 5 & 4 & 129 & 25 \\ \hline LHashSet & 1 & 4 & 6 & 24 & 16 \\ \hline LinkedList & 32 & 61 & 41 & 53 & 17 \\ \hline PQueue & 23 & 40 & 242 & 103 & 13 \\ \hline Properties & 104 & 19 & 49 & 47 & 18 \\ \hline Stack & 5 & 10 & 5 & 26 & 8 \\ \hline TreeMap & 80 & 131 & 231 & 106 & 26 \\ \hline TreeSet & 110 & 93 & 98 & 186 & 26 \\ \hline Vector & 106 & 83 & 156 & 176 & 20 \\ \hline WHashMap & 37 & 35 & 92 & 110 & 21 \\ \hline \hline Total & 704 & 798 & 1291 & 1519 & 324 \\ \hline \end{tabular} \end{center} } \caption{ Time in seconds taken by configurations of Nighthawk to achieve highest coverage on the {\tt java.util} Collection and Map classes. } \label{collection-times} \end{figure} The column labeled SLOC in Figure \ref{collection-map-cov} shows the total number of source lines of code reported by Cobertura (including inner classes) in the source file associated with the class. Column PN shows the SLOC covered by Nighthawk with the plain test wrappers and no Nighthawk switches; columns EN, PD and ED show the other combinations, and column ED also shows the coverage ratio with respect to total SLOC. % EN shows the effect of using the %enriched wrappers, and column PD the effect of using deep target %class analysis with the plain wrappers; column ED shows the effect of %using both the enriched wrappers and deep target class analysis. The second last line shows the totals for each column, and the last line shows the coverage ratio attained. With enriched test wrappers and deep target class analysis, Nighthawk performs well, achieving over 90\% coverage on 11 out of the 16 classes, and 82\% coverage overall. The Shapiro-Wilk normality test ($\alpha=.05$) was performed on each of the columns PN, EN, PD and ED from Figure \ref{collection-map-cov}, and was unable to reject the null hypothesis that the data was normally distributed ($p$ values .0665, .1444, .0635 and .1256 respectively). Both the paired $t$ test and the paired Wilcoxon test with Bonferroni correction (corrected $\alpha = .00833$) on each pair of columns in the table show that there are statistically significant differences between every pair except (PN, EN) and (EN, PD), though Wilcoxon suggests that there is a statistically significant difference between (PN, EN) as well. % PN, EN p= .009301, .006008 (t, Wilcoxon) % PN, PD p= .005655, .004458 % PN, ED p= .001805, .001936 % EN, PD p= .01884, .02571 % EN, ED p= .001768, .001662 % PD, ED p= .004065, .006008 Nighthawk performed poorly on the {\tt EnumMap} class. The main constructor to {\tt EnumMap} expects an enumerated type as one of the parameters. Nighthawk can not find such a type, so only a few lines of error code in constructors were executed. When we customized the test wrapper class to use a fixed enumerated type, Nighthawk with the ED configuration covered 204 lines of code (coverage ratio .85), raising the total coverage ratio for all classes to .88. %It also performed poorly on the {\tt Properties} class, due to %the fact that it could not automatically generate a useful %{\tt InputStream} argument for several of the methods. %We expect that these deficiencies could be at least partially %addressed by hand-customizing the test wrappers for the classes. Table \ref{collection-times} shows the amount of time taken by Nighthawk on the various configurations. In columns PN-ED, we report the number of seconds of clock time taken for Nighthawk to first achieve its best coverage. Shapiro-Wilk tests ($\alpha=.05$) showed that two of the columns of data (PN and PD) were not normally distributed, so we performed only paired Wilcoxon tests with Bonferroni correction (corrected $\alpha=.00833$) on each pair of columns. These tests showed that the only pairs of columns that were different to a statistically significant level were (PN, ED) and (EN, ED); in particular, using the enriched wrappers (PN vs.\ EN, PD vs.\ ED) did not take significantly longer. These statistical results suggest that generating the enriched wrappers allowed Nighthawk to cover significantly more code without running significantly longer; the deep target class analysis also caused Nighthawk to cover significantly more code, but took significantly longer (though still less than 100 seconds per unit on average). For normal use of Nighthawk, we would therefore recommend setting enriched wrappers and deep target class analysis as the defaults. With these in place as defaults, Nighthawk needs only the wrapper class name as a parameter. In column RC of Table \ref{collection-times}, we report the number of CPU seconds needed for the RunChromosome program to create and run the 10 new test cases with the parameters chosen by Nighthawk in the ED configuration. This time includes JVM startup time. The results show that with the parameters chosen by Nighthawk, RunChromosome can automatically generate many new test cases that achieve high coverage, in an average of approximately 2 seconds per test case. %\input{fssold} \input{fssnew-v2} \section{Threats to Validity} \label{threats-section} The representativeness of the units under test is the major threat to external validity. We studied Java collection classes because these are complex, heavily-used units that have high quality requirements. However, other units might have characteristics that cause Nighthawk to perform poorly. Randomized unit testing schemes in general require many test cases to be executed, so they perform poorly on methods that do a significant amount of disk I/O or thread generation. % They also %perform poorly on methods expecting string inputs that are %sentences in a grammar; we expect Nighthawk to do the same %unless it is given valid sentences as seed strings. Nighthawk uses Cobertura, which measures line coverage, a weak coverage measure. The results that we obtained may not extend to stronger coverage measures. However, the Nighthawk algorithm %treats the coverage measurement as a black-box integer, and does not perform special checks particular to line coverage. The comparison studies suggest that it still performs well when decision/condition coverage and MCC are simulated. The question of whether code coverage measures are a good indication of the thoroughness of testing is still, however, an area of active debate in the software testing community, making this a threat to construct validity. Also, time measurement is a construct validity threat. We measured time using Java's \linebreak[4] {\tt systemTimeInMillis}, which reports total wall clock time, not CPU time. This may show run times that do not reflect the testing cost to a real user. \section{Conclusions and Future Work} \label{conclusions-section} Randomized unit testing is a promising technology that has been shown to be effective, but whose thoroughness depends on the settings of test algorithm parameters. In this paper, we have described Nighthawk, a system in which an upper-level genetic algorithm automatically derives good parameter values for a lower-level randomized unit test algorithm. We have shown that Nighthawk is able to achieve the same coverage as earlier studies, and high coverage of complex, real-world Java units, while retaining the most desirable feature of randomized testing: the ability to generate many new high-coverage test cases quickly. We have also shown that we were able to optimize and simplify meta-heuristic search tools. Metaheuristic tools (such as genetic algorithms and simulated annealers) typically mutate some aspect of a candidate solution and evaluate the results. If the effect of mutating each aspect is recorded, then each aspect can be considered a feature and is amenable to the FSS processing described here. In this way, FSS can be used to automatically find and remove superflous parts of the search control. Future work includes the integration into Nighthawk of useful facilities from past systems, such as failure-preserving or coverage-preserving test case minimization, and further experiments on the effect of program options on coverage and efficiency. We also wish to integrate a feature subset selection learner into the GA level of the Nighthawk algorithm for dynamic optimization of the GA. Further, we can see a promising line of research where the cost/benefits of a particular meta-heuristic are tuned to the particulars of a specific problem. Here, we have shown that if we surrender $\frac{1}{10}$-th of the coverage, we can run Nighthawk ten times faster. While this is an acceptable trade-off in many domains, it may be depreciated in safety critical applciations. More work is required to understand how to best match meta-heuristics (with or without FSS) to particular problem domains. %\begin{itemize} %\item While the predicate coverage proposed by Visser et al.\ is % an interesting assessment criteria, there is no consensus in the % literature on the connection of this criteria to other measures. %\item Static code analysis can direct the generation of the test % cases. Our method, on the other hand, generates test cases at % random, so it is possible that we cover some code more than is % necessary, and that parts of our value pools are % useless. Our view is that this is not a major issue since our % runtimes, on real-world systems, are quite impressive. % Nevertheless, there needs to be more discussion on how to % assess test suite generation; i.e.\ runtimes versus % superfluous tests versus any other criteria. %\end{itemize} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Reminder: the "draftcls", not "draft", class option should be used if % it is desired that the figures are to be displayed while in draft mode. % An example of a floating figure using the graphicx package. % Note that \label must occur AFTER (or within) \caption. % For figures, \caption should occur after the \includegraphics. % %\begin{figure} %\centering %\includegraphics[width=2.5in]{myfigure.eps} %\caption{Simulation Results} %\label{fig_sim} %\end{figure} % An example of a double column floating figure using two subfigures. % (The subfigure.sty package must be loaded for this to work.) % The subfigure \label commands are set within each subfigure command, the % \label for the overall fgure must come after \caption. % \hfil must be used as a separator to get equal spacing % %\begin{figure*} %\centerline{\subfigure[Case I]{\includegraphics[width=2.5in]{subfigcase1.eps} %\label{fig_first_case}} %\hfil %\subfigure[Case II]{\includegraphics[width=2.5in]{subfigcase2.eps} %\label{fig_second_case}}} %\caption{Simulation results} %\label{fig_sim} %\end{figure*} % An example of a floating table. Note that, for IEEE style tables, the % \caption command should come BEFORE the table. Table text will default to % \footnotesize as IEEE normally uses this smaller font for tables. % The \label must come after \caption as always. % %\begin{table} %% increase table row spacing, adjust to taste %\renewcommand{\arraystretch}{1.3} %\caption{An Example of a Table} %\label{table_example} %\centering %% The array package and the MDW tools package offers better commands %% for making tables than plain LaTeX2e's tabular which is used here. %\begin{tabular}{|c||c|} %\hline %One & Two\\ %\hline %Three & Four\\ %\hline %\end{tabular} %\end{table} % if have a single appendix: %\appendix[Proof of the Zonklar Equations] % or %\appendix % for no appendix heading % do not use \section anymore after \appendix, only \section* % is possibly needed % use appendices with more than one appendix % then use \section to start each appendix % you must declare a \section before using any % \subsection or using \label (\appendices by itself % starts a section numbered zero.) % % Use this command to get the appendices' numbers in "A", "B" instead of the % default capitalized Roman numerals ("I", "II", etc.). % However, the capital letter form may result in awkward subsection numbers % (such as "A-A"). Capitalized Roman numerals are the default. %\useRomanappendicesfalse % \appendices % you can choose not to have a title for an appendix % if you want by leaving the argument blank %\section{} %Appendix two text goes here. % use section* for acknowledgment \section*{Acknowledgment} % optional entry into table of contents (if used) %\addcontentsline{toc}{section}{Acknowledgment} Thanks to Willem Visser for making the source code of the Java Pathfinder subject units available, and to the JDEAL development team for their tool package. Thanks also for interesting comments and discussions to Rob Hierons, Charles Ling, Bob Mercer and Andy Podgurski. This research is supported by the first author's grant from the Natural Sciences and Research Council of Canada (NSERC). % trigger a \newpage just before the given reference % number - used to balance the columns on the last page % adjust value as needed - may need to be readjusted if % the document is modified later %\IEEEtriggeratref{8} % The "triggered" command can be changed if desired: %\IEEEtriggercmd{\enlargethispage{-5in}} % references section % NOTE: BibTeX documentation can be easily obtained at: % http://www.ctan.org/tex-archive/biblio/bibtex/contrib/doc/ % can use a bibliography generated by BibTeX as a .bbl file % standard IEEE bibliography style from: % http://www.ctan.org/tex-archive/macros/latex/contrib/supported/IEEEtran/ \bibliographystyle{IEEEtran.bst} % argument is your BibTeX string definitions and bibliography database(s) \bibliography{my} % % manually copy in the resultant .bbl file % set second argument of \begin to the number of references % (used to reserve space for the reference number labels box) % biography section % % If you had an eps/pdf photo file (graphicx package needed) % the extra braces prevent the LaTeX parser from getting confused % when it sees the complicated \includegraphics command within an % optional argument. You can create your own macro to make things % simpler here. %\begin{biography}[{\includegraphics[width=1in,height=1.25in,clip,keepaspectratio]{mshell.eps}}]{Michael Shell} % or if you just want to reserve a space for a photo: \begin{IEEEbiography}{James H.\ Andrews} received the BSc and MSc degrees in computer science from the University of British Columbia, and the PhD degree in computer science from the University of Edinburgh. He worked from 1982 to 1984 at Bell-Northern Research, from 1991 to 1995 at Simon Fraser University, and from 1996 to 1997 on the FormalWare project at the University of British Columbia, Hughes Aircraft Canada and MacDonald Dettwiler. He is currently an Associate Professor in the Department of Computer Science at the University of Western Ontario, in London, Canada, where he has been since 1997. His research interests include software testing, semantics of programming languages, and formal specification. He is a member of the IEEE. \end{IEEEbiography} \begin{IEEEbiography}{Felix C.\ H.\ Li} received the BSc and MSc degrees in Computer Science from the University of Western Ontario, in 2005 and 2007 respectively. He is currently a software developer based in Toronto. \end{IEEEbiography} \begin{IEEEbiography}{Tim Menzies} received the CS and PhD degrees from the University of New South Wales and is the author of more than 160 publications. He is an associate professor at the Lane Department of Computer Science at West Virginia University (USA), and has been working with NASA on software quality issues since 1998. His recent research concerns modeling and learning with a particular focus on lightweight modeling methods. His doctoral research explored the validation of possibly inconsistent knowledge-based systems in the QMOD specification language. He is a member of the IEEE. \end{IEEEbiography} % if you will not have a photo %\begin{biographynophoto}{John Doe} %Biography text here. %\end{biographynophoto} % insert where needed to balance the two columns on the last page %\newpage %\begin{biographynophoto}{Jane Doe} %Biography text here. %\end{biographynophoto} % You can push biographies down or up by placing % a \vfill before or after them. The appropriate % use of \vfill depends on what kind of text is % on the last page and whether or not the columns % are being equalized. %\vfill % Can be used to pull up biographies so that the bottom of the last one % is flush with the other column. %\enlargethispage{-5in} % that's all folks \end{document}