Identifying Behavioural Implications of Source Code Changes

The dynamic behaviour of a software system changes as a consequence of developer’s static source code modifications. In this thesis, we improve upon a previous approach that combines static and dynamic analyses to categorize behavioural changes by greatly improving its accuracy through polymorphic mapping. We further refine the previous model by introducing a change-centric state transition model that captures the flow of call pairs among different partitions based on static and dynamic call graphs. We also extend the approach by incorporating complete dynamic call stacks into the analysis. Finally, we perform a longitudinal analysis of three software systems to categorize how they have dynamically evolved across 100 program versions. In our evaluation, the polymorphic mapping algorithm decreased mismatches between the static and dynamic analyses by 53%. In particular, we decreased the mismatch by 71% in the most important category of changes from the developer’s point of view. We found that developers introduce new behaviour more often than eliminating old behaviour. Our results show that developers are more likely to remove unexecuted/dead code than code that is executed dynamically. In terms of change types, we found that changes made to fix defects encountered the least inconsistent and unexpected behaviour, while changes made to add new functionality experienced the highest unexecuted behaviour. Finally, we argue that augmenting the dynamic analyses with call stacks provides useful information that helps developers analyze the implications of the call pairs highlighted by our analyses

Visualization and analysis of software clones

Code clones are identical or similar fragments of code in a software system. Simple copy-paste programming practices of developers, reusing existing code fragments instead of implementing from the scratch, limitations of both programming languages and developers are the primary reasons behind code cloning. Despite the maintenance implications of clones, it is not possible to conclude that cloning is harmful because there are also benefits in using them (e.g. faster and independent development). As a result, researchers at least agree that clones need to be analyzed before aggressively refactoring them. Although a large number of state-of-the-art clone detectors are available today, handling raw clone data is challenging due to the textual nature and large volume. To address this issue, we propose a framework for large-scale clone analysis and develop a maintenance support environment based on the framework called VisCad. To manage the large volume of clone data, VisCad employs the Visual Information Seeking Mantra: overview first, zoom and filter, then provide details-on-demand. With VisCad users can analyze and identify distinctive code clones through a set of visualization techniques, metrics covering different clone relations and data filtering operations. The loosely coupled architecture of VisCad allows users to work with any clone detection tool that reports source-coordinates of the found clones. This yields the opportunity to work with the clone detectors of choice, which is important because each clone detector has its own strengths and weaknesses. In addition, we extend the support for clone evolution analysis, which is important to understand the cause and effect of changes at the clone level during the evolution of a software system. Such information can be used to make software maintenance decisions like when to refactor clones. We propose and implement a set of visualizations that can allow users to analyze the evolution of clones from a coarse grain to a fine grain level. Finally, we use VisCad to extract both spatial and temporal clone data to predict changes to clones in a future release/revision of the software, which can be used to rank clone classes as another means of handling a large volume of clone data. We believe that VisCad makes clone comprehension easier and it can be used as a test-bed to further explore code cloning, necessary in building a successful clone management system

Mining software repositories to support software evolution

Software evolution represents a major phase in the development life cycle of software systems. In recent years, software evolution has been recognized as one of the most important and challenging areas in the field of software engineering. Studies even show that 65-80% of the system lifetime will be spent on maintenance and evolution activities. Software repositories, such as versioning and bug tracking systems are essential parts of various software maintenance activities. Given the often large amounts of information stored in these repositories, researchers have proposed to mine and analyze these large knowledge bases in order to study and support various aspects of the evolution of a software system. In this thesis, we introduce a common ontological representation to support the mining and analysis of software repositories. In addition to this common representation, we introduce the SVN-Ontologizer and Bugzilla-Ontologizer tools that provide automation for both data extraction from remote repositories and ontology populations. A case study is presented to illustrate the applicability of the present approach in supporting software maintainers during the analysis and mining of these software repositorie