Software Regression Test Case Prioritization for Object-Oriented Programs using Genetic Algorithm with Reduced-Fitness Severity

Musa, Samaila; Sultan, Abu-Bakar Md; Abd-Ghani, Abdul-Azim Bin; Baharom, Salmi

doi:10.17485/ijst/2015/v8i30/86661

Cited by 5 publications

(6 citation statements)

References 5 publications

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“…On the basis of measured performance gained from the outcomes, GA with decreased strictness of error was able to prioritize chosen test cases with more efficiency as compared to the use of GA with same level of severity of fault and non-prioritized test cases. GA with decreased severity of errors also gives higher priority to designated cases more efficiently when associated to the usage of GA with the similar level of severity of error [18]. In the long run, this success is able to decrease the overall regression esting cost.…”

Section: Machine Learning Approachmentioning

confidence: 95%

“…This method claimed to reduce the cost of regression testing by efficient selection and prioritization of test cases. S. Musa, et al [11] suggested a structure for regression testing for object-oriented software constructed on prolonged system necessity graph model of the program that is being affected. Semantic examination of the code provides a base for this method.…”

Section: Dependency Approachmentioning

confidence: 99%

“…S. Musa, et al [11] suggested the use of Genetic Algorithm to optimize the selected test cases built from a dependency graph model. The proposed approach produced better results in terms of average proportional rate of fault exposure.…”

Section: Machine Learning Approachmentioning

confidence: 99%

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A review of object-oriented approach for test case prioritization

Farooq

Aman

Saringat

2019

IJEECS

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<p>The most essential phase in regression testing is Test Case Prioritization (TCP), with its primary objective to increase the fault detection rate at different stages during testing. Prior to achieving the objective, existing evidence of techniques in TCP must be synthesized and analyzed. At present, fault detection for TCP based on object-oriented features only consider statement, module, and class level. The important features of object-oriented (OO) programming like inheritance and polymorphism have not been fully explored for fault detection in TCP. Such OO concepts are important for test case selection and in turn for ranking the test cases (prioritization). This paper reviews various test case prioritization techniques specific to OO systems. This review is hoped to highlight the importance and usage of TCP in relation to object-oriented software development lifecycle.</p>

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Section: Machine Learning Approachmentioning

confidence: 95%

Section: Dependency Approachmentioning

confidence: 99%

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A review of object-oriented approach for test case prioritization

Farooq

Aman

Saringat

2019

IJEECS

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“…Maunika et al in [9] exploited Bee colony optimization for test case selection and to improve path coverage. Authors in [10] used genetic algorithm for regression test suite prioritization and produced mutants for object oriented codes. Wasiur Rhmann et al in [11] presented their research in which they applied GA for improving test efficiency in early stages of software development.…”

Section: Related Workmentioning

confidence: 99%

Hybrid swarm and GA based approach for software test case selection

Palak¹,

Gulia²

2019

IJECE

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<span>Being a crucial step and deciding factor for software reliability, software testing has evolved a long way and always attracted researchers due to various inherent challenges. The quality of a software application depends on the effectiveness of the testing carried out during development and maintenance phase. Testing is a crucial but time consuming activity that influences the overall cost of software development. Thus a minimal but efficient test suite selection is the need of the hour. This paper presents a hybrid technique based on swarm based search technique and GA (Genetic Algorithm) for selection of promising test cases to reduce the overall development cost and time of the application. We took component based software into consideration as they offer some inherent advantages over traditional software development paradigms.</span>

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“…Regression test cases selection techniques [8,13,14] chose the most appropriate test cases from the existing test cases such that the costs of performing regression testing are reduced according to the information gathered from the program source code and the changed version or on the program specifications. Test suite minimization techniques attempt to reduce the costs of performing regression testing to have earlier feedback.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Cost Cognizant Regression Test Prioritization for Object-Oriented Programs Based on Fault Dependency

Bello¹,

Sultan²,

Ghani³

et al. 2018

IJET

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Regression testing performed to provide confidence on the newly or updated software system is a resource consuming process. To ease this process, various techniques are developed. One such technique, test case prioritization, orders test cases with respect to the goals such that the most important test case in achieving those goals is scheduled earlier during the testing session. Among such performance goals, the rate of faults detections, measure how faults are detected quickly throughout the regression testing process. Improved dependency detection among faults provides faster feedback to the developers which gives chance to debug leading faults earlier in time. One other goal, the rate of fault severity detection, measure how fast severe fault can be detected in the testing process. Although, previous works address these issues but assumed that the costs of executing test cases and severities of detected faults are the same. However, costs of test and severities of faults varied. Furthermore, they did not consider incorporating evolution process such as applying genetic algorithms to their technique. In this work, we proposed an evolutionary cost-cognizant regression testing approach that prioritizes test case according to the rate of severity detection of test cases connected with dependent faults using genetic algorithms. The aim is to reveal more severe leading faults earlier using least cost in executing the test suite and to measure the efficacy of the technique using APFDc.

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Software Regression Test Case Prioritization for Object-Oriented Programs using Genetic Algorithm with Reduced-Fitness Severity

Cited by 5 publications

References 5 publications

A review of object-oriented approach for test case prioritization

A review of object-oriented approach for test case prioritization

Hybrid swarm and GA based approach for software test case selection

Evolutionary Cost Cognizant Regression Test Prioritization for Object-Oriented Programs Based on Fault Dependency

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