Using test case reduction and prioritization to improve symbolic execution

Scaling symbolic execution to large programs or programs with complex inputs remains difficult due to path explosion and complex constraints, as well as external method calls. Additionally, creating an effective test structure with sym-bolic inputs can be difficult. A popular symbolic execution strategy in practice is to perform symbolic execution not “from scratch ” but based on existing test cases. This paper proposes that the effectiveness of this approach to symbolic execution can be enhanced by (1) reducing the size of seed test cases and (2) prioritizing seed test cases to maximize ex-ploration efficiency. The proposed test case reduction strat-egy is based on a recently introduced generalization of delta-debugging, and our prioritization techniques include novel methods that, for this purpose, can outperform some tradi-tional regression testing algorithms. We show that applying these methods can significantly improve the effectiveness of symbolic execution based on existing test cases

Hybrid and dynamic static criteria models for test case prioritization of web application regression testing

In software testing domain, different techniques and approaches are used to support the process of regression testing in an effective way. The main approaches include test case minimization, test case selection, and test case prioritization. Test case prioritization techniques improve the performance of regression testing by arranging test cases in such a way that maximize fault detection could be achieved in a shorter time. However, the problems for web testing are the timing for executing test cases and the number of fault detected. The aim of this study is to increase the effectiveness of test case prioritization by proposing an approach that could detect faults earlier at a shorter execution time. This research proposed an approach comprising two models: Hybrid Static Criteria Model (HSCM) and Dynamic Weighting Static Criteria Model (DWSCM). Each model applied three criteria: most common HTTP requests in pages, length of HTTP request chains, and dependency of HTTP requests. These criteria are used to prioritize test cases for web application regression testing. The proposed HSCM utilized clustering technique to group test cases. A hybridized technique was proposed to prioritize test cases by relying on assigned test case priorities from the combination of aforementioned criteria. A dynamic weighting scheme of criteria for prioritizing test cases was used to increase fault detection rate. The findings revealed that, the models comprising enhanced of Average Percentage Fault Detection (APFD), yielded the highest APFD of 98% in DWSCM and 87% in HSCM, which have led to improve effectiveness prioritization models. The findings confirmed the ability of the proposed techniques in improving web application regression testing