Open AccessAutomated Test Case Generation for Effective Spectrum-based Fault Localization
Author(s) -
Bo Yang,
Yuze He,
Lixing Feng
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1576/1/012042
Subject(s) - computer science , fault (geology) , test case , genetic algorithm , iterative and incremental development , statement (logic) , software , process (computing) , test (biology) , quality (philosophy) , data mining , reliability engineering , machine learning , programming language , software engineering , engineering , paleontology , philosophy , regression analysis , epistemology , seismology , political science , law , biology , geology
Software testing is the key to ensuring software quality. Test case generation and software fault localization are two important research objects of testing. Spectrum-based fault localization (SBFL) method is a mainstream dynamic fault localization method, which inputs test cases into the program for execution and collects statement coverage information and execution results. By analysing this information, a fault report can be generated. The performance of SBFL is affected by test case quality. Therefore, this paper proposes a test case generation method based on improved genetic algorithm to assist SBFL. This method uses a small number of initial test cases to execute the program, then uses the XGBoost model to pre-process the spectrum information and generates a list of potential fault statements to guide the iterative process of the genetic algorithm. The experimental results show that the test cases generated using the improved genetic algorithm have certain advantages over the test cases generated by general genetic algorithm and the baseline test cases.