A comparative study on evolutionary multi-objective algorithms for next release problem

The next release problem (NRP) refers to implementing the next release of software in the software industry regarding the expected revenues; specifically, constraints like limited budgets indicate that the total cost corresponding to the next software release should be minimized. This paper uses and...

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Veröffentlicht in:Applied soft computing Jg. 144; S. 110472
Hauptverfasser: Rahimi, Iman, Gandomi, Amir H., Nikoo, Mohammad Reza, Chen, Fang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.09.2023
Schlagworte:
Evolutionary algorithm
Multi-objective
Next release problem
Software engineering
Next release problem
Multi-objective
Evolutionary algorithm
Software engineering
ISSN:1568-4946, 1872-9681
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Zusammenfassung:The next release problem (NRP) refers to implementing the next release of software in the software industry regarding the expected revenues; specifically, constraints like limited budgets indicate that the total cost corresponding to the next software release should be minimized. This paper uses and investigates the comparative performance of nineteen state-of-the-art evolutionary multi-objective algorithms, including NSGA-II, rNSGA-II, NSGA-III, MOEAD, EFRRR, tDEA, KnEA, MOMBIII, SPEA2, RVEA, NNIA, HypE, ANSGA-III, BiGE, GrEA, IDBEA, SPEAR, SPEA2SDE, and MOPSO, that can tackle this problem. The problem was designed to maximize customer satisfaction and minimize the total required cost. Three indicators, namely hyper-volume (HV), spread, and runtime, were examined to compare the algorithms. Two types of datasets, i.e., classic and realistic data, from small to large scale, were also examined to verify the applicability of the results. Overall, NSGA-II exhibited the best CPU run time in all test scales, and, also, the results show that the HV and spread values of 1st and 2nd best algorithms (NNIA and SPEAR), for which most HV values for NNIA are bigger than 0.708 and smaller than 1, while the HV values for SPEAR vary between 0.706 and 0.708. Finally, the conclusion and direction for future works are discussed. •Addressing 19 state-of-the-art evolutionary algorithms on MONRP.•Applying the algorithms on real datasets namely, Eclipse, Gnome, and Mozilla.•Comparing and ranking the algorithms using different metrics.
ISSN:1568-4946
1872-9681
DOI:10.1016/j.asoc.2023.110472