In this thesis I have demonstrated that, although beneficial in areas other than software defect prediction, locality does not help when classifying defective modules in software defect prediction. I approached locality from several different areas:
\be
\item Within the classifier
\item Filtering the training data in respect to the testing data
\item Creating a unique training dataset for each test instance
\ee
I showed that the locality based classifiers performed no better than, and often times worse than the non-locality based classifiers. I also demonstrated that the Burak effect from Turhan's paper\cite{08Turhan} is no longer present when dealing with just the defective modules\footnote{\tion{burak-reproduction} demonstrates that the Burak filter used in this thesis does follow the original pattern of Turhan's results when using his reporting format}. Finally, I demonstrated that clustering the data, and training a classifier off of each cluster did not help improve classification performance. In each of these three cases, I report that the classification performance of the global algorithms is the same as, or better than, the locality based classifiers or the classifiers with locality based pre-processing.

By the results in \chap{results} and what is reported in \tion{lwlOptions} I show that the defective modules in software defect prediction datasets do not contain an internal structure that benefits from locality based classification. Locality shows promising results while reporting on both defective and non-defective modules. In contrast, it shows no beneficial performance increase when reported on just the defective modules. From this, I conclude that only the non-defective modules are modeled with an internal structure that benefits by locality.

I recommend directing research at developing classifiers which are specifically aimed at detecting defective modules rather than classifying both defective and non-defective modules. I also recommend using methods other that static code metrics when representing modules in datasets.
