A Systematic Literature Review of AI-Based Software Requirements Prioritization Techniques
Software requirements show what the customer desires his software to do. They are the first stepping stone towards a successful software development project. With the increasing complexity of the software due to its size and feature base, it is vital to prioritize the requirements for efficient util...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10360116/ |
| _version_ | 1797376390785400832 |
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| author | Rahila Anwar Muhammad Bilal Bashir |
| author_facet | Rahila Anwar Muhammad Bilal Bashir |
| author_sort | Rahila Anwar |
| collection | DOAJ |
| description | Software requirements show what the customer desires his software to do. They are the first stepping stone towards a successful software development project. With the increasing complexity of the software due to its size and feature base, it is vital to prioritize the requirements for efficient utilization of development resources. To achieve this, industrial organizations are devising new strategies and improved solutions even with the help of artificial intelligence (AI) tool set. Existing requirements prioritization techniques are human-intensive and suffer from several limitations like overlapping outcomes, scalability problems, time consumption, inaccuracy, and so on. Some of the problems can be solved by including artificial intelligence algorithms and strategies. Several AI-based requirements prioritization techniques have been proposed by applying Genetic Algorithms, Fuzzy Logic, Ant Colony Optimization, and Machine Learning. Literature witnesses some good review studies and surveys on conventional prioritization techniques but there exists none for AI-based techniques that identify not only their strengths but also their weaknesses, advantages of machine learning techniques over other AI-based requirements prioritization techniques, and limitations of applying AI-based techniques in requirements prioritization. This study presents a systematic literature review (SLR) of AI-based requirements prioritization approaches covering 46 papers published from 2000 to 2021. We have given this literature review a new dimension by conducting a parametric analysis of AI-based requirements prioritization techniques and we have identified these parameters after a thorough literature study. Some of the chosen parameters are generic (related to the prioritization process) and some are specific (related to AI techniques). This study has greatly helped us draw a clear line among AI-based techniques to show their domain of application to gain maximum advantage. Our findings will assist researchers, requirement analysts, and other stakeholders in making a wise decision to select the best requirements prioritization technique to gain optimal results. |
| first_indexed | 2024-03-08T19:37:51Z |
| format | Article |
| id | doaj.art-f834471e50d14895933cdeeac0fed32e |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T19:37:51Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-f834471e50d14895933cdeeac0fed32e2023-12-26T00:06:46ZengIEEEIEEE Access2169-35362023-01-011114381514386010.1109/ACCESS.2023.334325210360116A Systematic Literature Review of AI-Based Software Requirements Prioritization TechniquesRahila Anwar0https://orcid.org/0009-0002-5095-9474Muhammad Bilal Bashir1https://orcid.org/0000-0002-5938-6361Department of Computing and Technology, Iqra University, Islamabad, PakistanDepartment of Computing and Technology, Iqra University, Islamabad, PakistanSoftware requirements show what the customer desires his software to do. They are the first stepping stone towards a successful software development project. With the increasing complexity of the software due to its size and feature base, it is vital to prioritize the requirements for efficient utilization of development resources. To achieve this, industrial organizations are devising new strategies and improved solutions even with the help of artificial intelligence (AI) tool set. Existing requirements prioritization techniques are human-intensive and suffer from several limitations like overlapping outcomes, scalability problems, time consumption, inaccuracy, and so on. Some of the problems can be solved by including artificial intelligence algorithms and strategies. Several AI-based requirements prioritization techniques have been proposed by applying Genetic Algorithms, Fuzzy Logic, Ant Colony Optimization, and Machine Learning. Literature witnesses some good review studies and surveys on conventional prioritization techniques but there exists none for AI-based techniques that identify not only their strengths but also their weaknesses, advantages of machine learning techniques over other AI-based requirements prioritization techniques, and limitations of applying AI-based techniques in requirements prioritization. This study presents a systematic literature review (SLR) of AI-based requirements prioritization approaches covering 46 papers published from 2000 to 2021. We have given this literature review a new dimension by conducting a parametric analysis of AI-based requirements prioritization techniques and we have identified these parameters after a thorough literature study. Some of the chosen parameters are generic (related to the prioritization process) and some are specific (related to AI techniques). This study has greatly helped us draw a clear line among AI-based techniques to show their domain of application to gain maximum advantage. Our findings will assist researchers, requirement analysts, and other stakeholders in making a wise decision to select the best requirements prioritization technique to gain optimal results.https://ieeexplore.ieee.org/document/10360116/Artificial intelligenceant colonyfuzzy logicgenetic algorithmgeneric parametersmachine learning |
| spellingShingle | Rahila Anwar Muhammad Bilal Bashir A Systematic Literature Review of AI-Based Software Requirements Prioritization Techniques IEEE Access Artificial intelligence ant colony fuzzy logic genetic algorithm generic parameters machine learning |
| title | A Systematic Literature Review of AI-Based Software Requirements Prioritization Techniques |
| title_full | A Systematic Literature Review of AI-Based Software Requirements Prioritization Techniques |
| title_fullStr | A Systematic Literature Review of AI-Based Software Requirements Prioritization Techniques |
| title_full_unstemmed | A Systematic Literature Review of AI-Based Software Requirements Prioritization Techniques |
| title_short | A Systematic Literature Review of AI-Based Software Requirements Prioritization Techniques |
| title_sort | systematic literature review of ai based software requirements prioritization techniques |
| topic | Artificial intelligence ant colony fuzzy logic genetic algorithm generic parameters machine learning |
| url | https://ieeexplore.ieee.org/document/10360116/ |
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