An Empirical Study on the Use of Defect Prediction for Test Case Prioritization

• Type: Article in International Conference Proceedings Book
• Date: 2019

Authors

Paterson, D (Author) Other; José Campos (Author) Other; Rui Abreu (Author) Other; Kapfhammer, GM (Author) Other; Fraser, G (Author) Other; McMinn, P (Author) Other

Conference proceedings International

Title: 2019 IEEE 12TH CONFERENCE ON SOFTWARE TESTING, VALIDATION AND VERIFICATION (ICST 2019)

Pages: 346-357

12th IEEE International Conference on Software Testing, Verification and Validation (ICST)

Xian, PEOPLES R CHINA, APR 22-27, 2019

Indexing

ISI Web of Knowledge - 12 Citations

Scopus - 18 Citations

Other information

Authenticus ID: P-00Q-RH9

DOI: 10.1109/icst.2019.00041

Abstract (EN): Test case prioritization has been extensively researched as a means for reducing the time taken to discover regressions in software. While many different strategies have been developed and evaluated, prior experiments have shown them to not be effective at prioritizing test suites to find real faults. This paper presents a test case prioritization strategy based on defect prediction, a technique that analyzes code features - such as the number of revisions and authors - to estimate the likelihood that any given Java class will contain a bug. Intuitively, if defect prediction can accurately predict the class that is most likely to be buggy, a tool can prioritize tests to rapidly detect the defects in that class. We investigated how to configure a defect prediction tool, called Schwa, to maximize the likelihood of an accurate prediction, surfacing the link between perfect defect prediction and test case prioritization effectiveness. Using 6 real-world Java programs containing 395 real faults, we conducted an empirical evaluation comparing this paper's strategy, called G-clef, against eight existing test case prioritization strategies. The experiments reveal that using defect prediction to prioritize test cases reduces the number of test cases required to find a fault by on average 9.48% when compared with existing coverage-based strategies, and 10.5% when compared with existing history-based strategies.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 12