Abstract-This research extends an existing source code change taxonomy that was designed to analyze change coupling. The extension expands change types related to statements in order to achieve more granular data about the type of statement that is changed. The extended taxonomy is evaluated to determine if it can be applied to software fault analysis. We found that the extended change types occur consistently and with high frequency in fault fixes for Eclipse 2.0 and 3.0. Faults were then clustered according to the source code changes and analyzed. We found that the types and sizes of clusters are highly correlated, indicating some consistency in the patterns of the fault fixes. Finally, we performed an initial investigation to determine whether faults in the same cluster have similar characteristics. Our results indicate that many of the change types can be used to characterize the type of fault that has been fixed. However, some of the change types obfuscate the true nature of the fix. Ideas for improving the taxonomy based on these findings are provided.
This position paper argues that fault classification provides vital information for software analytics, and that machine learning techniques such as clustering can be applied to learn a project-(or organization-) specific fault taxonomy. Anecdotal evidence of this position is presented as well as possible areas of research for moving toward the posited goal.
A decision support system for fault classification is presented. The fault classification scheme is developed to provide guidance in process improvement and fault-based testing. The research integrates results in fault classification, source code analysis, and fault-based testing research. Initial results indicate that existing change type and fault classification schemes are insufficient for this purpose. Development of sufficient schemes and their evaluation are discussed.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.