Section: New Results

Software Quality: Taming Software Evolution

APIEvolutionMiner: Keeping API Evolution under Control. During software evolution, source code is constantly refactored. In real-world migrations, many methods in the newer version are not present in the old version (e.g., 60% of the methods in Eclipse 2.0 were not in version 1.0). This requires changes to be consistently applied to reflect the new API and avoid further maintenance problems. We propose a tool to extract rules by monitoring API changes applied in source code during system evolution. In this process, changes are mined at revision level in code history. Our tool focuses on mining invocation changes to keep track of how they are evolving. We also provide three case studies in order to evaluate the tool. [34]

Towards an Automation of the Mutation Analysis Dedicated to Model Transformation. A major benefit of Model Driven Engineering (MDE) relies on the automatic generation of artefacts from high-level models through intermediary levels using model transformations. In such a process, the input must be well-designed and the model transformations should be trustworthy. Due to the specificities of models and transformations, classical software test techniques have to be adapted. Among these techniques, mutation analysis has been ported and a set of mutation operators has been defined. However, mutation analysis currently requires a considerable manual work and suffers from the test data set improvement activity. This activity is seen by testers as a difficult and time-consuming job, and reduces the benefits of the mutation analysis. This work addresses the test data set improvement activity. Model transformation traceability in conjunction with a model of mutation operators, and a dedicated algorithm allow to automatically or semi-automatically produce test models that detect new faults. The proposed approach is validated and illustrated in a case study written in Kermeta. [17]

Predicting software defects with causality tests. We propose a defect prediction approach centered on more robust evidences towards causality between source code metrics (as predictors) and the occurrence of defects. More specifically, we rely on the Granger causality test to evaluate whether past variations in source code metrics values can be used to forecast changes in time series of defects. Our approach triggers alarms when changes made to the source code of a target system have a high chance of producing defects. We evaluated our approach in several life stages of four Java-based systems. We reached an average precision greater than 50% in three out of the four systems we evaluated. Moreover, by comparing our approach with baselines that are not based on causality tests, it achieved a better precision. [19]