Open AccessIdentification of propagated defects to reduce software testing cost via mutation testing
Author(s) -
Dong Gun Lee,
Yeong-Seok Seo
Publication year - 2022
Publication title -
mathematical biosciences and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.451
H-Index - 45
eISSN - 1551-0018
pISSN - 1547-1063
DOI - 10.3934/mbe.2022286
Subject(s) - regression testing , reliability engineering , test suite , computer science , software testing , test case , keyword driven testing , software , non regression testing , mutation testing , process (computing) , white box testing , mutation , identification (biology) , test strategy , set (abstract data type) , data mining , software system , engineering , machine learning , software construction , programming language , biology , genetics , regression analysis , gene , botany
In software engineering, testing has long been a research area of software maintenance. Testing is extremely expensive, and there is no guarantee that all defects will be found within a single round of testing. Therefore, fixing defects that are not discovered by a single round of testing is important for reducing the test costs. During the software maintenance process, testing is conducted within the scope of a set of test cases called a test suite. Mutation testing is a method that uses mutants to evaluate whether the test cases of the test suite are appropriate. In this paper, an approach is proposed that uses the mutants of a mutation test to identify defects that are not discovered through a single round of testing. The proposed method simultaneously applies two or more mutants to a single program to define and record the relationships between different lines of code. In turn, these relationships are examined using the defects that were discovered by a single round of testing, and possible defects are recommended from among the recorded candidates. To evaluate the proposed method, a comparative study was conducted using the fault localization method, which is commonly employed in defect prediction, as well as the Defects4J defect prediction dataset, which is widely used in software defect prediction. The results of the evaluation showed that the proposed method achieves a better performance than seven other fault localization methods (Tarantula, Ochiai, Opt2, Barinel, Dstar2, Muse, and Jaccard).