Mendeley
TY  _  JOUR
ID  -  1402122646174
TI  -  Investigating an Approach to Identify and Classify Mutants Based on the Characteristics of Mutants with Machine Learning Algorithms
JO  -  Journal of Information and Communication Technology
JA  -  ES
LA  -  fa
SN  -  2717-0411
AU  -  asghari zeinab
AU  -  arasteh bahman
AU  -  Koochari Abbas
AD  -  دانشگاه آزاد اسلامی واحد تبریز
AD  -  دانشگاه آزاد اسلامی واحد علوم و تحقیقات تهران
AD  -  
Y1  -  2024
PY  -  2024
VL  -  61
IS  -  
SP  -  180
EP  -  192
KW  -  software testing
KW  -   mutation testing
KW  -   equivalent mutants
KW  -   test adequacy
KW  -   mutation score.
DO  -  
N2  -  <p>Software mutation testing is one of the effective methods to evaluate code quality and detect hidden errors. However, this method faces challenges such as producing equivalent mutants and the process being time-consuming. The test cases designed for software testing must have the necessary sufficiency. For this purpose, the criterion of mutation test score is used. One of the main issues related to software mutation testing is the generation of equivalent mutants. Equivalent mutants are mutants that do not change the behavior of the program and have the same output as the original program. Identifying these mutants can make the mutation testing process time-consuming and costly. It is also possible that these mutants are mistakenly classified as hard to kill mutants. While hard to kill mutants can be rejected by changing the test items and strengthening them. In this paper, we present an efficient method for classifying program mutants to identify and separate equivalent mutations from hard to kill mutants. Using machine learning algorithms, we intend to optimize this process and improve the efficiency and effectiveness of the method. This is done by extracting different features from the mutants and using them to train machine learning models.</p>
UR  - rimag.ir/en/Article/46174
L1  - rimag.ir/en/Article/Download/46174
ER  -  
