Software requirement selection using a combined multi‐objective optimisation technique
Abstract 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 techniqu...
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| Format: | Article |
| Language: | English |
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Wiley
2022-12-01
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| Series: | IET Software |
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| Online Access: | https://doi.org/10.1049/sfw2.12070 |
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| _version_ | 1832559555802824704 |
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| author | Wathiq H. Dukhan Marghny H. Mohamed Ali A. Amer Elnomery Allam Zanaty Omar Reyad |
| author_facet | Wathiq H. Dukhan Marghny H. Mohamed Ali A. Amer Elnomery Allam Zanaty Omar Reyad |
| author_sort | Wathiq H. Dukhan |
| collection | DOAJ |
| description | Abstract 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 multi‐objective 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. |
| format | Article |
| id | doaj-art-5a29a2fc24094e47a972dd506cbc1a9a |
| institution | Kabale University |
| issn | 1751-8806 1751-8814 |
| language | English |
| publishDate | 2022-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Software |
| spelling | doaj-art-5a29a2fc24094e47a972dd506cbc1a9a2025-02-03T01:29:44ZengWileyIET Software1751-88061751-88142022-12-0116655857510.1049/sfw2.12070Software requirement selection using a combined multi‐objective optimisation techniqueWathiq H. Dukhan0Marghny H. Mohamed1Ali A. Amer2Elnomery Allam Zanaty3Omar Reyad4Faculty of Science Sana'a University Sana'a YemenDepartment of Computer Science Faculty of Computer and Information Assiut University Assiut EgyptFaculty of Applied Science Computer Science Department Taiz University Taiz YemenDepartment of Computer Science Faculty of Computers and Artificial Intelligence Sohag University Sohag EgyptFaculty of Science Sohag University Sohag EgyptAbstract 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 multi‐objective 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.https://doi.org/10.1049/sfw2.12070artificial bee colonygenetic algorithmnext release problemsearch‐based software engineering |
| spellingShingle | Wathiq H. Dukhan Marghny H. Mohamed Ali A. Amer Elnomery Allam Zanaty Omar Reyad Software requirement selection using a combined multi‐objective optimisation technique IET Software artificial bee colony genetic algorithm next release problem search‐based software engineering |
| title | Software requirement selection using a combined multi‐objective optimisation technique |
| title_full | Software requirement selection using a combined multi‐objective optimisation technique |
| title_fullStr | Software requirement selection using a combined multi‐objective optimisation technique |
| title_full_unstemmed | Software requirement selection using a combined multi‐objective optimisation technique |
| title_short | Software requirement selection using a combined multi‐objective optimisation technique |
| title_sort | software requirement selection using a combined multi objective optimisation technique |
| topic | artificial bee colony genetic algorithm next release problem search‐based software engineering |
| url | https://doi.org/10.1049/sfw2.12070 |
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