An Effective Model to Predict the Extension of Code Changes in Bug Fixing Process Using Text Classifiers

Sepahvand, Reza; Akbari, Reza; Hashemi, Sattar; Boushehrian, Omid

doi:10.1007/s40998-021-00458-1

Cited by 1 publication

(1 citation statement)

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“…The method is used to get an optimal solution to the problem in an acceptable time. In recent years, artificial intelligence methods have been successfully applied to cope with software development problems (Nayyar et al, 2019, Kukkar et al, 2020, Sepahvand et al, 2022. Dealing with combinatorial optimization problems, in most cases, is time-consuming so this kind of problem has been an alive area of research for many decades.…”

Section: Introductionmentioning

confidence: 99%

A Method for Feature Subset Selection in Software Product Lines

Nahid

Peyman

Akbari

2022

International Journal of Software Innovation

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Software product line (SPL) represents methods, tools, and techniques for creating a group of related software systems. Each product is a combination of multiple features. So, the task of production can be mapped to a feature subset selection problem, which is an NP-hard problem. This issue is very significant when the number of features in a software product line is huge. This chapter is aimed to address the feature subset selection in software product lines. Furthermore, the authors aim at studying the performance of a proposed multi-objective method in solving this NP-hard problem. Here, a multi-objective method (MOBAFS) is presented for feature selection in SPLs. The MOBAFS is a an optimization algorithm, which is inspired by the foraging behavior of honeybees. This technique is evaluated on five large-scale real-world software product lines in the range of 1,244 to 6,888 features. The proposed method is compared with the SATIBEA. According to the results of three solution quality indicators and two diversity metrics, the proposed method, in most cases, surpasses the other algorithm.

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Section: Introductionmentioning

confidence: 99%