.png){kind=icon}
Return
Generating test data for both paths coverage and faults detection using genetic algorithms: multi-path case
.png){kind=icon}
PRE
AI
DOI：10.1007/s11704-014-3372-7.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Generating test data that can expose the faults of the program is an important issue in software testing. Although previous methods of covering path can generate test data to traverse target path, the test data generated by these methods are difficult in detecting some low-probabilistic faults that lie on the covered paths. We present a method of generating test data for covering multiple paths to detect faults in this study. First, we transform the problem of covering multiple paths and detecting faults into a multi-objective optimization problem with constraint, and construct a mathematical model for it. Then, we give a strategy of solving the model based on a weighted genetic algorithm. Finally, we apply our method to several real-world programs, and compare it with several methods. The experimental results confirm that the proposed method can more efficiently generate test data that not only traverse the target paths but also detect faults lying in them than other methods.
Keywords:
software testing
multiple paths coverage
faults detection
multi-objective optimization
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4.6
Papers:
1.6K
Citations:
2.8K
Organization
Cited Papers
.png){kind=icon}
ShareSaveShareSave
View more