Understanding the effect of time-constraint bounded novel technique for regression test selection and prioritization

Suri, Bharti; Singhal, Shweta

doi:10.1007/s13198-014-0244-3

Cited by 8 publications

(3 citation statements)

References 18 publications

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“…ACO variants have been applied rigorously by various researchers working in the area. Some of them include: epistasis based ACO [26], using ACO for prioritizing test cases with secure features [27] time-constraint-based ACO [28], history-based prioritization using ACO [29], prioritizing test cases based on test factors using ACO [30]. The widespread usage of ACO for test case selection and prioritization highlights the fact that ACO is a very prolific technique in the area.…”

Section: Related Workmentioning

confidence: 99%

Multi-Objective Fault-Coverage Based Regression Test Selection and Prioritization Using Enhanced ACO_TCSP

Singhal

Jatana²,

Sheoran³

et al. 2023

Mathematics

Self Cite

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Regression testing of the software during its maintenance phase, requires test case prioritization and selection due to the dearth of the allotted time. The resources and the time in this phase are very limited, thus testers tend to use regression testing methods such as test case prioritization and selection. The current study evaluates the effectiveness of testing with two major goals: (1) Least running time and (2) Maximum fault coverage possible. Ant Colony Optimization (ACO) is a well-known soft computing technique that draws its inspiration from nature and has been widely researched, implemented, analyzed, and validated for regression test prioritization and selection. Many versions of ACO approaches have been prolifically applied to find solutions to many non-polynomial time-solvable problems. Hence, an attempt has been made to enhance the performance of the existing ACO_TCSP algorithm without affecting its time complexity. There have been efforts to enhance the exploration space of various paths in each iteration and with elite exploitation, reducing the total number of iterations required to converge to an optimal path. Counterbalancing enhanced exploration with intelligent exploitation implies that the run time is not adversely affected, the same has also been empirically validated. The enhanced algorithm has been compared with the existing ACO algorithm and with the traditional approaches. The approach has also been validated on four benchmark programs to empirically evaluate the proposed Enhanced ACO_TCSP algorithm. The experiment revealed the increased cost-effectiveness and correctness of the algorithm. The same has also been validated using the statistical test (independent t-test). The results obtained by evaluating the proposed approach against other reference techniques using Average Percentage of Faults Detected (APFD) metrics indicate a near-optimal solution. The multiple objectives of the highest fault coverage and least running time were fruitfully attained using the Enhanced ACO_TCSP approach without compromising the complexity of the algorithm.

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Section: Related Workmentioning

confidence: 99%

Multi-Objective Fault-Coverage Based Regression Test Selection and Prioritization Using Enhanced ACO_TCSP

Singhal

Jatana²,

Sheoran³

et al. 2023

Mathematics

Self Cite

View full text Add to dashboard Cite

show abstract

“…It is noticed that proposed multi objective approach scheduled the test cases optimally and earlier. Suri and Singhal presented ACO based technique for regression testing and prioritization [41]. Further, it is seen that a time bounded constraint is incorporated in proposed approach to determine optimal test cases.…”

Section: Related Workmentioning

confidence: 99%

Test Case Prioritization Using Cat Swarm Optimization

2020

IJATCSE

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Regression testing is one of time taking and expensive task. It is an indispensable part of SDLC process. The main task of regression testing is to scuttleall test cases of a given test suite. But it is time consuming and tedious task and also needed more efforts and time. The effort can be described in term of human work. One of the solutions for aforementioned task is to automate the entire process through prioritizing algorithm. The aim of thee algorithms is to determine optimal test-cases for regression testing from a given test suite. The other objective of these algorithmsis also addressed the time and effort issues of regression testing through achievinghigher faults detection. Hence, in this work, a new technique based on CSO techniqueis implemented to prioritize test cases. Prior to apply the CSO method for prioritizing test-cases, some amendmentsare inculcated in CSO algorithm to achieve optimum results in terms of fault detection. The experiment demonstratesthe applicability of CSO based prioritization technique for attaining effective results especially prioritizing task.

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“…It is noticed that proposed multi objective approach scheduled the test cases optimally and earlier. Suri and Singhal presented ACO based technique for regression testing and prioritization [24]. Further, it is seen that a time bounded constraint is incorporated in proposed approach to determine optimal test cases.…”

Section: Related Workmentioning

confidence: 99%

Artificial Bee Colony Based Prioritization Algorithm for Test Case Prioritization Problem

2020

IJATCSE

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In software engineering filed, regression testing can be described as one of important task to test the testcases. In regression testing, test cases from test suites are executed repeatedly such that modifications incorporated in software cannot lead to unpredictable outcomes. As, the functionality increases in software's, the size of test suites also increased. In turn, regression testing becomes one of time consuming and costly task. So, to reduce cost and time, instead of complete test suite can run, a subset of test cases is selected. The selection of this subset is known as test case prioritization (TCP). TCP becomes the NP-hard problem when size of test suites and test cases increases. Hence, there is a need of prioritization algorithm that can select optimal subset of test cases to overcome the aforementioned problems. Recent studies showed that artificial bee colony (ABC is one of effective technique that can be solved the diverse optimization problem. Hence, ABC based prioritization algorithm is proposed to choose optimal subset of test cases. The results of ABC based prioritization algorithm are compared with sequential and random ordering. Results confirmed that it can address the TCP problem in competent manner.

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Understanding the effect of time-constraint bounded novel technique for regression test selection and prioritization

Cited by 8 publications

References 18 publications

Multi-Objective Fault-Coverage Based Regression Test Selection and Prioritization Using Enhanced ACO_TCSP

Multi-Objective Fault-Coverage Based Regression Test Selection and Prioritization Using Enhanced ACO_TCSP

Test Case Prioritization Using Cat Swarm Optimization

Artificial Bee Colony Based Prioritization Algorithm for Test Case Prioritization Problem

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