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\usepackage{cite,graphicx}
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\usepackage{menzies/tricks}
\usepackage{tomi/tricks}
\usepackage{color}
\usepackage{ifthen}

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% *** 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.
% (Unless specifically asked to do so by the journal or conference you plan
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\begin{document}
%\large
%
% paper title
% can use linebreaks \\ within to get better formatting as desired
\title{Applications of Simulation and AI Search: Assessing the Relative Merits of Agile vs Traditional Software Development}


% author names and affiliations
% use a multiple column layout for up to three different
% affiliations
\author{
Bryan Lemon, Aaron Riesbeck,
Tim Menzies, Justin Price,
Joseph D'Alessandro,\\
%Justin Price,\\
Rikard Carlsson,
Tomi Prifiti,
Fayola Peters,
Hiuhua Lu,
Dan Port$^*$\\
Lane Department of Computer Science and Electrical Engineering, West Virginia University\\
$^*$Information Technology Management, University of Hawaii\\ 
\url{bryan@bryanlemon.com, tim@menzies.us, {tprifti | hlu3 }@mix.wvu.edu}\\
\url{{aaron.riesbeck | justin.n.price | jdalessa57 | ricca742 | fayolapeters}@gmail.com, dport@hawaii.edu}}
% conference papers do not typically use \thanks and this command
% is locked out in conference mode. If really needed, such as for
% the acknowledgment of grants, issue a \IEEEoverridecommandlockouts
% after \documentclass

% for over three affiliations, or if they all won't fit within the width
% of the page, use this alternative format:
% 
%\author{\IEEEauthorblockN{Michael Shell\IEEEauthorrefmark{1},
%Homer Simpson\IEEEauthorrefmark{2},
%James Kirk\IEEEauthorrefmark{3}, 
%Montgomery Scott\IEEEauthorrefmark{3} and
%Eldon Tyrell\IEEEauthorrefmark{4}}
%\IEEEauthorblockA{\IEEEauthorrefmark{1}School of Electrical and Computer Engineering\\
%Georgia Institute of Technology,
%Atlanta, Georgia 30332--0250\\ Email: see http://www.michaelshell.org/contact.html}
%\IEEEauthorblockA{\IEEEauthorrefmark{2}Twentieth Century Fox, Springfield, USA\\
%Email: homer@thesimpsons.com}
%\IEEEauthorblockA{\IEEEauthorrefmark{3}Starfleet Academy, San Francisco, California 96678-2391\\
%Telephone: (800) 555--1212, Fax: (888) 555--1212}
%\IEEEauthorblockA{\IEEEauthorrefmark{4}Tyrell Inc., 123 Replicant Street, Los Angeles, California 90210--4321}}




% use for special paper notices
%\IEEEspecialpapernotice{(Invited Paper)}

% make the title area
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\begin{abstract}
%\boldmath
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% IEEEtran.cls defaults to using nonbold math in the Abstract.
% This preserves the distinction between vectors and scalars. However,
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% no keywords


% For peer review papers, you can put extra information on the cover
% page as needed:
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\section{Introduction}
\input{joe/intro}
%\section{The POM Model}
%\input{tomi/words}ke
%\subsection{The Boehm-Turner Model}
%\input{fayola/words}
\section{Simulator Specifications}
\input{joe/words}
%\section{Keys: The Search Engine}
%\input{jprice/keys}
\input{bryan/words}
\section{Results}

\fig{1000} shows the ranges found in KEYS' treatments, while randomly selecting
prioritization policies and the eight inputs on POM2.
The results are split between two
prioritization policies and three levels of
requirements dynamism. Each combination of policy*dynamism was run
1000 times. 

Notice that, in all cases, the treatments found by KEYS increased the performance
scores reported in \tion{perform} by a statistically significant amount
(Mann-Whitney, 95\%).
%The columns in \fig{1000}
%refer to discrete ranges of the input space, called $bins$.
%Each $bins$ covers one-tenth
%of the maximum range on an input. For example, the 
%{\em bin1} column refers to results for (say) 
%\mbox{$.82 \le criticality < .86$}. 
Each cell
in \fig{1000} shows the percent frequency of that bin occurring in the treatments
found by KEYS.  %Empty cells were never found in treatments.
%Darker cells show more frequent values.  

%In the subsequent discussion, we ignore most effects that do not occur in the majority 
%(over 50\%) of the runs.
There are several majority-case effects in \fig{1000} that deserve our attention. 
First, there are 
some strong negative results that hold across all dynamisms and prioritizations.
One input ($criticality$ $modifier$) was never selected by KEYS. 
Two inputs ($initial$ $known$ and $inter-dependency$) were never
selected in the majority case ($> 50\%$ of the runs).

Second, with respect to agile2, there were frequent effects
found for total $size$ of personnel, $team$ $size$
, and $culture$:
\bi
\item
In 72 to 100\% of our agile2 runs, KEYS selected for
the smallest possible total team size.%  variables
%($size=bin1$; i.e. about 3 to 20 people).  
\item
In the upper bins for (medium, high dynamism), 
%If we add up the percentages 
%for $culture$ in $bin7,bin8,bin9,bin10$   then for (medium, high dynamism),
cultural factors appeared in (80,72)\% respectively
of the treatments found by KEYS. 
%This effect is predicted by theory: 
%recalling \eq{culture}, the higher the $culture$ value, the more likely it is
%that teams will react to changing requirements $values$.
\item
%Another somewhat smaller effect for agile2 can be found for the size of individual teams.
%KEYS found that 
For medium and very high dynamism, it is useful to have
team sizes of about 5 to 17 people (bin2, bin3, bin4, bin5).%in $bin2,bin3,bin4,bin5$ (i.e. about 5 to 17 people). 
\ei
Curiously,
smallest size teams (below 5 people) was never selected by KEYS. 
We conclude that 
%agile fails for very small teams working with a large number of other small teams
fails for very small teams when
programmers spend the majority of their time interfacing with other teams.%so much time interfacing with other teams that
%they cannot get anything done themselves.


\begin{figure}[!t]
\small
\begin{center}

Medium dynamism: $\sigma=0.15, \lambda=0.015$

\includegraphics[width=2in]{charts/fr-v-sc-msmi.pdf}

High dynamism: $\sigma=2, \lambda=0.2$

\includegraphics[width=2in]{charts/fr-v-sc-msvh.pdf}
\end{center}
\caption{1000 runs of KEYS controlling $\sigma$ and $\lambda$ while
using  $\frac{1}{10}$-th size (\mbox{$3\le size < 32.17$})
 and top $\frac{1}{10}-th$ criticality ($\ge 1.22$)
and choosing at random for the other inputs.}\label{fig:special}
\end{figure}
Finally, \fig{1000} shows that many results are very similar; e.g. all the agile2 results offer
nearly the same pattern.
In fact, KEYS and \fig{1000} show us that
POM2 contains two major divisions of its 10-dimensional space.
%\bi
%\item
%In division one, we find all
%all the agile2 results, as well as the low dynamism results for plan-base prioritization share the same distrubution.
%This division is characterized by best treatments mentioning very small total team size and high values on the culture scale.
%\item 
%In division two, we find the medium and high dynamism in plan-based prioritization results deviate from the above distribution.
%This division is characterized  by best treatments mentioning very high criticality ranges.
%\ei
In terms of testing our general conclusion,
the place to run tests is in the union of the two divisions:
 at very high criticality and very small overall team size. 
The results of this test is showed in \fig{special}.

When POM2 was run in the union of these two divisions, agile2 produces much larger median scores than plan-based. 
This figure lends support to the general
conclusion of this paper. 
If management is
given a choice between agile2 and plan-based methods, they should adopt agile2. 
%\subsubsection{Why the difference}
%\input{jprice/hybrid}
%\section{Future Work}
%\input{lsheep/words}
%\section{Related Work}
%\subsection{Brahms}

\section{Conclusion}
Building software process models can be complicated by a drought of data. When data is scarce, model-based evidence can be used to make a reasoned
case for reconfiguration of a project. To help mitigate information overdose, it is useful to augment a simulation engine with a search engine
that can find the most interesting regions within the model input/output space.

This paper was a case study with combination of simulator (POM2) and
AI search engine (KEYS).
%POM2 includes concepts such as 
%organizational culture, project criticality and development by multiple large teams. 
Our AI search engine explored the 
state space of our model to find  regions that significantly improved the output performance score of the model.
We found no case where plan-based out-perform agile2. In fact,  sometimes agile2 performed much better than plan-based.
We therefore recommend that:
\begin{quote}{\em
The default development process be agile2. 
}\end{quote}

%Notice that while \fig{special} explores {\em one} interesting region within POM2, it does not explore {\em all} possible
%combinations of subsets of ranges of that model. Such an analysis is beyond the scope of this short paper. However,
%if management ever finds themselves working in a particular part of the POM2 space, our models could be used to
%repeat an analysis like \fig{special} (i.e. to assess different policies for those particular projects in that particular
%space).

Note also that in two areas, POM2 found limits in software engineering theory. In order to mature the conclusions
of this paper, two issues need to be addressed:
\bi
\item What is the effect of mixtures of different types of programmers on a project?
%In \fig{person}, we showed one calculation that 
%concluded that mixtures had a zero effect on a project.  This is an unexpected result
%and one that needs further investigation.
\item What are the patterns of dependencies between requirements? 
%Lacking any guidance from the literature,
%POM2 built its network of dependencies between tasks using the methods of \tion{tree}.  
%More work is required to uncover the expected patterns of dependencies in real-world projects.
\ei

%Finally,
%we do not know why so much
%time is wasted on evidence-less debates in SE.
Very simple simulations/search engines
can automatically explore the nuances  of a debate.
Using such automated tools, it is possible to make interesting conclusions with minimal machinery and effort. 
Our model definitions took just days of work;
the implementation required a few weeks of work; and the runs to gather our data took just one night.
Consequently, our conclusion is that evidence-less debates could be replaced, if possible, with debates that use model-based evidence.
%\ei%\end{document}  % This is where a 'short' article might terminate
%We used model based reasoning by applying the KEYS search engine to the POM2 model. 
We concluded that agile2 performs as good or much better than plan-based development.

%ACKNOWLEDGMENTS are optional



% conference papers do not normally have an appendix


% use section* for acknowledgement

% trigger a \newpage just before the given reference
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% references section

% can use a bibliography generated by BibTeX as a .bbl file
% BibTeX documentation can be easily obtained at:
% http://www.ctan.org/tex-archive/biblio/bibtex/contrib/doc/
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% <OR> 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)
%\begin{thebibliography}{1}

%\bibitem{IEEEhowto:kopka}
%H.~Kopka and P.~W. Daly, \emph{A Guide to \LaTeX}, 3rd~ed.\hskip 1em plus
%  0.5em minus 0.4em\relax Harlow, England: Addison-Wesley, 1999.

%\end{thebibliography}




% that's all folks
\end{document}


