regression testing

Test case prioritization has been often used to alleviate the costs associated with software regression testing. Current techniques have attempted to estimate the fault exposing potential of test cases using code coverage information and rank them using a heuristic. However, studies show that coverage does not strongly correlate with the effectiveness and fault exposing potential of test cases. Relying on the results of studies that demonstrated the effectiveness of code metrics in fault prediction, we speculate that code metric information can be leveraged to design a new effective technique for test case prioritization. Based on our hypothesis, in this paper, a new prioritization technique is proposed that works based on data fusion on code complexity metrics. The novelty of our technique lies in its original viewpoint to estimate fault exposing potential of test cases in prioritization. To evaluate the proposed technique, we have conducted experiments on faulty versions of seven Java benchmarks. In the experiments, we often observed at least 70% performance in prioritization measured in terms of average percentage of fault detection, which validates our hypothesis.

The increase in complexity and the rate of technological changes in modern software development have led to a demand for systematic methods that raise the abstraction level for system maintenance and regression testing. Model Driven Engineering (MDE) has promised to reduce extra coding efforts in software maintenance activities using traceable change management, especially in rapidly changing application. The paper presents a Z-notation based framework, called Changed-based Regression Testing (ChbRT), for formal modeling of regression testing in the context of MDE. The framework proposes to automatically propagate the changes from a software specification to testing artifacts in order to preserve consistency after system evolution. The framework is enriched by providing a new category of coverage metrics for change-based regression testing. The proposed framework is expected to be beneficial in both platform independent and specific levels of ChbRT by identifying the suitable coverage according to available testing resources. The accuracy and efficiency of the proposed framework have been evaluated and analyzed on three case studies.