MuKEA-TCP: A Mutant Kill-based Local Search Augmented Evolutionary Algorithm Approach for Test Case Prioritization

Ufuktepe, Ekincan; Ufuktepe, Deniz Kavzak; Karabulut, Korhan

doi:10.1109/compsac51774.2021.00129

Cited by 2 publications

(2 citation statements)

References 23 publications

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“…Thereby, their proposed test case prioritization technique gives higher priority to those test cases that help distinguish all mutants as soon as possible. Other studies have also found that mutation-based test cases prioritization [55], [56] to be effective. Since mutation score is used as an indicator of good quality test cases, using it in test case prioritization gives higher priority to test cases that have a higher chance of exposing faults.…”

Section: Related Workmentioning

confidence: 91%

Application of the Law of Minimum and Dissimilarity Analysis to Regression Test Case Prioritization

Ufuktepe

Tuğlular

2023

IEEE Access

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Regression testing is one of the most expensive processes in testing. Prioritizing test cases in regression testing is critical for the goal of detecting the faults sooner within a large set of test cases. We propose a test case prioritization (TCP) technique for regression testing called LoM-Score inspired by the Law of Minimum (LoM) from biology. This technique calculates the impact probabilities of methods calculated by change impact analysis with forward slicing and orders test cases according to LoM. However, this ordering doesn't consider the possibility that consecutive test cases may be covering the same methods repeatedly. Thereby, such ordering can delay the time of revealing faults that exist in other methods.To solve this problem, we enhance the LoM-Score TCP technique with an adaptive approach, namely with a dissimilarity-based coordinate analysis approach. The dissimilarity-based coordinate analysis uses Jaccard Similarity for calculating the similarity coefficients between test cases in terms of covered methods and the enhanced technique called Dissimilarity-LoM-Score (Dis-LoM-Score) applies a penalty with respective on the ordered test cases. We performed our case study on 10 open-source Java projects from Defects4J, which is a dataset of real bugs and an infrastructure for controlled experiments provided for software engineering researchers. Then, we hand-seeded multiple mutants generated by Major, which is a mutation testing tool. Then we compared our TCP techniques LoM-Score and Dis-LoM-Score with the four traditional TCP techniques based on their Average Percentage of Faults Detected (APFD) results.INDEX TERMS Change impact analysis, regression testing, software testing, test case prioritization.

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Section: Related Workmentioning

confidence: 91%

Application of the Law of Minimum and Dissimilarity Analysis to Regression Test Case Prioritization

Ufuktepe

Tuğlular

2023

IEEE Access

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“…Other techniques for optimization include bat algorithm [9], greedy algorithm [10], cuckoo search algorithm [11] and flower pollination algorithm [11]. These techniques can also be customized and hybrid in optimizing the selection of test cases, such as in [12], [13], [14] and [15].…”

Section: Related Workmentioning

confidence: 99%

Comparative Analysis of Test Case Prioritization Using Ant Colony Optimization Algorithm and Genetic Algorithm

Asyraf,

Sahid,

Zainal

2023

JSCDM

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After it is published, every software system will get an upgrade, requiring it to adapt to meet the ever-changing client needs thus regression testing becomes one of the most important operations in any software system. As it is too expensive to repeat the execution of all the test cases available from a previous version of the software system, numerous ways to optimize the regression test suite have evolved, one of which is test case prioritizing. This study was carried out to test and compare the effectiveness of evolutionary algorithms and swarm intelligence algorithms, represented by the Genetic Algorithm (GA) and Ant Colony Optimization (ACO) algorithms. They will be implemented to find the Average Percentage Fault Detected (APFD), execution time, and Big O notation, as these are critical aspects in software testing to ensure high-quality products are produced on time. This study employs data from two separate investigations on comparable issues, denoted as Case Study One and Case Study Two. TCP has been extensively used in recent years, but not much research has been conducted to analyze and evaluate the performance of genetic algorithms (GA) and ant colony optimization (ACO) in a test case prioritization context. The algorithms were compared using APFD, execution time. The APFD and execution time values of 50, 100, 150, and 200 iterations for ACO and GA for both datasets are conducted. Both algorithms were determined to work on O() notation, which indicates they should scale up their execution process similarly on different input scales. Both algorithms performed well in their respective roles. ACO has shown to be more valuable than GA in terms of APFD and GA has shown to be more valuable than ACO in terms of execution time.

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MuKEA-TCP: A Mutant Kill-based Local Search Augmented Evolutionary Algorithm Approach for Test Case Prioritization

Cited by 2 publications

References 23 publications

Application of the Law of Minimum and Dissimilarity Analysis to Regression Test Case Prioritization

Application of the Law of Minimum and Dissimilarity Analysis to Regression Test Case Prioritization

Comparative Analysis of Test Case Prioritization Using Ant Colony Optimization Algorithm and Genetic Algorithm

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