A. Perera, A. Aleti, B. Turhan and M. Boehme, "An Experimental Assessment of Using Theoretical Defect Predictors to Guide Search-Based Software Testing," in IEEE Transactions on Software Engineering, doi: 10.1109/TSE.2022.3147008
An experimental assessment of using theoretical defect predictors to guide search-based software testing
|Author:||Perera, Anjana1; Aleti, Aldeida1; Turhan, Burak2;|
1Faculty of Information Technology, Monash University, Clayton, VIC 3800, Australia
2Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu 90014, Finland
|Online Access:||PDF Full Text (PDF, 3.4 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2022042530264
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2022-04-25
Automated test generators, such as search-based software testing (SBST) techniques are primarily guided by coverage information. As a result, they are very effective at achieving high code coverage. However, is high code coverage alone sufficient to detect bugs effectively In this paper, we propose a new SBST technique, predictive many objective sorting algorithm (PreMOSA), which augments coverage information with defect prediction information to decide where to increase the test coverage in the class under test (CUT). Through an experimental evaluation using 420 labelled bugs on the Defects4J benchmark and using theoretical defect predictors, we demonstrate the improved effectiveness and efficiency of PreMOSA in detecting bugs when using any acceptable defect predictor, i.e., a defect predictor with recall and precision 75%, compared to the state-of-the-art dynamic many objective sorting algorithm (DynaMOSA). PreMOSA detects up to 8.3% more labelled bugs on average than DynaMOSA when given a time budget of 2 minutes for test generation per CUT.
IEEE transactions on software engineering
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
113 Computer and information sciences
© The Author(s) 2022. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.