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

Shweta Singhal, Nishtha Jatana (), Kavita Sheoran, Geetika Dhand, Shaily Malik, Reena Gupta, Bharti Suri, Mudligiriyappa Niranjanamurthy, Sachi Nandan Mohanty and Nihar Ranjan Pradhan ()
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Shweta Singhal: Department of Computer Science and Information Technology, Indira Gandhi Delhi Technical University for Women, New Delhi 110006, India
Nishtha Jatana: Department of Computer Science and Engineering, Maharaja Surajmal Institute of Technology, New Delhi 110058, India
Kavita Sheoran: Department of Computer Science and Engineering, Maharaja Surajmal Institute of Technology, New Delhi 110058, India
Geetika Dhand: Department of Computer Science and Engineering, Maharaja Surajmal Institute of Technology, New Delhi 110058, India
Shaily Malik: Department of Computer Science and Engineering, Maharaja Surajmal Institute of Technology, New Delhi 110058, India
Reena Gupta: University School of Information & Communication Technology, Guru Gobind Singh Indraprastha University, New Delhi 110078, India
Bharti Suri: University School of Information & Communication Technology, Guru Gobind Singh Indraprastha University, New Delhi 110078, India
Mudligiriyappa Niranjanamurthy: Department of AI and ML, BMS Institute of Technology and Management, Bengaluru 560064, India
Sachi Nandan Mohanty: School of Computer Science & Engineering, VIT-AP University, Amaravati 522237, India
Nihar Ranjan Pradhan: School of Computer Science & Engineering, VIT-AP University, Amaravati 522237, India

Mathematics, 2023, vol. 11, issue 13, 1-21

Abstract: 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.

Keywords: ant colony optimization; regression testing; test suite prioritization; metaheuristic technique; nature-inspired technique (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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