Abstract
To achieve the goal of creating products for a specific market segment, implementation of Software Product Line (SPL) is required to fulfill specific needs of customers by managing a set of common features and exploiting the variabilities between the products. Testing product-by-product is not feasible in SPL due to the combinatorial explosion of product number, thus, Test Case Prioritization (TCP) is needed to select a few test cases which could yield high number of faults. Among the most promising TCP techniques is similarity-based TCP technique which consists of similarity distance measure and prioritization algorithm. The goal of this paper is to propose an enhanced string distance and prioritization algorithm which could reorder the test cases resulting to higher rate of fault detection. Comparative study has been done between different string distance measures and prioritization algorithms to select the best techniques for similarity-based test case prioritization. Identified enhancements have been implemented to both techniques for a better adoption of prioritizing SPL test cases. Experiment has been done in order to identify the effectiveness of enhancements done for combination of both techniques. Result shows the effectiveness of the combination where it achieved highest average fault detection rate, attained fastest execution time for highest number of test cases and accomplished 41.25% average rate of fault detection. The result proves that the combination of both techniques improve SPL testing effectiveness compared to other existing techniques.
Highlights
Software Product Line (SPL) engineering is based on systematically managing and exploiting the commonalities and variabilities between features to achieve specific goals of customers (Al-Hajjaji, Lity, Lachmann, Thüm, Schaefer., & Saake, 2016)
In terms of APFD scores, enhanced All-yes config algorithm (EA) outperformed other prioritization algorithms for Web Portal, Video Player, Printer and Electronic Shopping case studies while performed second best in Go Phone case study. This proves that combination between EJW and EA is suitable to be used in similarity-based prioritization technique for most case studies
Test cases from both Feature Model (FM) will require much longer experiment completion time. Both case studies showed that EA is the fastest prioritization algorithm with 1568 milliseconds execution time in Battle of Tanks and second best in Printers case study with 69 milliseconds execution time
Summary
Software Product Line (SPL) engineering is based on systematically managing and exploiting the commonalities and variabilities between features to achieve specific goals of customers (Al-Hajjaji, Lity, Lachmann, Thüm, Schaefer., & Saake, 2016). To overcome combinatorial explosion of products, regression testing strategy such as Test Case Prioritization (TCP) is preferred to be used in SPL since TCP is able to reduce the testing resources allocated while preserving the number of test cases and maintaining efficient fault detection. We are motivated to compare between different similarity distance measures and prioritization algorithms in order to find the best combination of both techniques which can be further enhanced to fulfill the goal of increasing the probability of finding faults in test cases. The first is comparing between different similarity distance measures and prioritization algorithms and to identify the best techniques for similaritybased prioritization Enhancing both type of techniques and produce a better method for adoption in SPL testing is the second contribution. The third contribution is performing an experiment to evaluate the effectiveness of the integration of both enhanced techniques and identifying further improvement (if any) compared to the existing techniques
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