Priority Path for Mutant Repairs on Mutation Testing

Arnomo, Sasa Ani; Ibrahim, Noraini Binti

doi:10.1109/icaiti48442.2019.8982158

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…Usually, software developers who create applications have a lot of code entered, which requires testing to ensure the app is running as expected. With mutation testing, it is possible to detect many mutants that must be corrected [28]. For this reason, software developers need clustering in project categories which shows which projects are prioritized.…”

Section: B Feature In Project Categorymentioning

confidence: 99%

MT-Forward Algorithm for Prediction of Project Categories Based on Selection of Mutants

Arnomo¹,

Ibrahim²

2022

Int. J. Adv. Sci. Eng. Inf. Technol.

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Mutation testing is an effective technique for errors. Although effective, mutation testing has the main limitation that it is awfully expensive because it requires a series of tests on each mutant. Therefore, many researchers have focused on presenting various techniques to reduce the cost of mutation testing. In this study, the MT-Forward algorithm was developed. The concept of MT-Forward is mutation testing that connects the selection and prediction methods. As a result of the selection, the mutant operator can make a more efficient selection of the number of mutants obtained. The shrinkage of the selected mutants was from 1,019 live mutants to 749 live mutants as a priority. There are 15 features used in this study. The required features are obtained from the Locmetrix Test, Coverage Test, and Mutation Testing. The accuracy value of each method is almost the same, which distinguishes this study is the selection of important operators who prioritize improvement programs without reducing the level of accuracy. Where the PMS Method uses 43 Mutant Operators, PMT Method 22 Mutant Operators, while MT-Forward uses 7 mutant operators as priority mutant operators. The method used to evaluate the performance of the algorithm is 5-fold cross-validation. The accuracy of each method is PMS 57.14%, PMT Method 57.14%, and MT-Forward is 95.00%. MT-Forward states that combining mutant selection techniques and project category prediction is important for fixing faults.

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Section: B Feature In Project Categorymentioning

confidence: 99%

MT-Forward Algorithm for Prediction of Project Categories Based on Selection of Mutants

Arnomo¹,

Ibrahim²

2022

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…The test case generates a different set of tests that can be run to detect faults [8]. The results of the execution of an imitation program get a different value from the results of the original program execution, so it can be stated that the mutant status is killed [9], [10]. If the execution value is the same, the mutant is declared alive.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Mutation Testing Challenges to Fixing Faults

Arnomo¹,

Ibrahim²,

Putri³

et al. 2022

Int. J. Adv. Sci. Eng. Inf. Technol.

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One of the challenges of mutation testing is fixing faults. In the debugging phase, all live mutants were repaired. Programs need high mutation scores to be declared reliable program codes. Each mutation test can allow the identification of multiple mutants. This is what confuses the faults fixing process. The objective of this research is to get the shortest route so that it can help in sorting the mutant types during application improvement after testing. The optimization is needed considering the number of mutants in each mutation testing. The problems related to optimization are very complex. It takes a suitable method to find the shortest path by paying attention to each point. There are 30 projects chosen randomly. The operator mutations that are often killed when testing mutations are AOIU and COI. The proposed optimization for mutant repair sequence is the ant colony system (ACS). The route selection using the Ant Colony System algorithm resulted in route optimization of 1.528254. Meanwhile, if the genetic algorithm is used, the score is 1.767643. Optimization results are very helpful for developers in improving code in mutation testing. Research states the best order for handling mutants using ACS. This research can be further developed with the addition of class-level mutant cases which are produced using class mutation operators. Class mutation operators have different characteristics from traditional mutation operators. In particular, it requires changes to the program structure, such as the definition of class variables.

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