ImReMuDF: Redundant Mutants Identification Method Based on Definition and Reference of Variables
Author(s) -
Zhenpeng Liu,
Yang Xian-wei,
Yi Liu,
Yonggang Zhao,
Xiaofei Li
Publication year - 2021
Publication title -
scientific programming
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.269
H-Index - 36
eISSN - 1875-919X
pISSN - 1058-9244
DOI - 10.1155/2021/7543896
Subject(s) - mutation testing , redundancy (engineering) , computer science , identification (biology) , mutant , mutation , reliability engineering , process (computing) , data mining , algorithm , engineering , genetics , biology , programming language , botany , gene
Mutation testing is an effective defect-based software testing method, but a large number of mutants lead to expensive testing costs, which hinders the application of variation testing in industrial engineering. To solve this problem and enable mutation testing to be applied in industrial engineering, this paper improves the method of identifying redundant mutants based on data flow analysis and proposes the inclusion relationship between redundant mutants, so that the redundancy rate of mutants is reduced. In turn, the cost of mutation testing can be reduced. The redundant mutants identification method based on definition and reference of variables (ImReMuDF) was validated and evaluated using 8 C programs. The minimum improvement in redundant mutant identification rate was 34.0%, and the maximum improvement was 71.3% in the 8 C programs tested, and the verification results showed that the method is feasible and effective and has been improved in reducing redundant mutants and effectively reducing the execution time of mutation testing.
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