Virtual Savant as a generic learning approach applied to the basic independent Next Release Problem

Массобрио, Рензо; Nesmachnow, Sergio; Palomo-Lozano, Francisco; Dorronsoro, Bernabè

doi:10.1016/j.asoc.2021.107374

Cited by 5 publications

(1 citation statement)

References 15 publications

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“…It has been utilised to showcase the functionality of Nautilus' primary, and how it is adjusted to tackle software engineering problems. Renzo et al [38] showed the utility of Virtual Savant (VS) to learn tackling NRP automatically, and in an accurate and swift procedure leveraging an exact method. It was a generic problem-solving method contingent on machine learning models which imitate the solution generation to problem instances using the reference programme.…”

Section: Related Workmentioning

confidence: 99%

Software requirement selection using a combined multi‐objective optimisation technique

et al. 2022

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The optimal requirements selection set aims primarily at careful search for the best requirements set of the next release of software during development process. This procedure is widely defined as the next release problem (NRP), which is also classified as NP-hard dilemma. Several techniques, in literature, have been proposed to tackle NRP. However, in real examples, the earlier studies still immature as NRP still suffers interactions and restrictions that makes the problem more complicated. Although few interesting works have been presented, yet NRP, based on our study, could be further investigated and effectively tackled. In this research, therefore, NRP is devised as a multiobjective optimisation problem. Two clashing objectives (satisfaction and cost) and two constraints (interactions forms) are formulated. To tackle NRP effectively, a new hybrid genetic and artificial bee colony algorithm (HGABC) is introduced. HGABC combines features of genetic and artificial bee colony algorithms. Experimental study, using case studies and three criteria, have been conducted to show HGABC's power of generating non-dominated effective Pareto solutions versus the state-of-the-art algorithms. Results indicate that HGABC does not just outperform its rivals, yet also gives better Pareto solutions in terms of diversity and quality for almost all the instances of this problem.

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Section: Related Workmentioning

confidence: 99%