Section: New Results

Software Quality: Bugs and Debuggers

BugMaps-Granger: A Tool for Causality Analysis between Source Code Metrics and Bugs. Despite the increasing number of bug analysis tools for exploring bugs in software systems, there are no tools supporting the investigation of causality relationships between internal quality metrics and bugs. We propose an extension of the BugMaps tool called BugMaps-Granger that allows the analysis of source code properties that caused bugs. For this purpose, we relied on Granger Causality Test to evaluate whether past changes to a given time series of source code metrics can be used to forecast changes in a time series of defects. Our tool extracts source code versions from version control platforms, generates source code metrics and defects time series, computes Granger, and provides interactive visualizations for causal analysis of bugs. We also provide a case study in order to evaluate the tool. [22]

Mining Architectural Patterns Using Association Rules

Software systems usually follow many programming rules prescribed in an architectural model. However, developers frequently violate these rules, introducing architectural drifts in the source code. We present a data mining approach for architecture conformance based on a combination of static and historical software analysis. For this purpose, the proposed approach relies on data mining techniques to extract structural and historical architectural patterns. In addition, we propose a methodology that uses the extracted patterns to detect both absences and divergences in source-code based architectures. We applied the proposed approach in an industrial strength system. As a result we detected 137 architectural violations, with an overall precision of 41.02%. [27]

Heuristics for Discovering Architectural Violations

Software architecture conformance is a key software quality control activity that aims to reveal the progressive gap normally observed between concrete and planned software architecture. We present ArchLint, a lightweight approach for architecture conformance based on a combination of static and historical source code analysis. For this purpose, ArchLint relies on four heuristics for detecting both absences and divergences in source code based architectures. We applied ArchLint in an industrial-strength system and as a result we detected 119 architectural violations, with an overall precision of 46.7% and a recall of 96.2%, for divergences. We also evaluated ArchLint with four open-source systems, used in an independent study on reflexion models. In this second study, ArchLint achieved precision results ranging from 57.1% to 89.4%. [26]