Multi-label software requirement smells classification using deep learning
Abstract Software requirement smell detection is an important part of establishing high-quality software specifications. These smells, which frequently indicate difficulties like ambiguity, vagueness, or incompleteness, can lead to misunderstandings and mistakes in the latter phases of software deve...
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Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-86673-w |
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| author | Ashagrew Liyih Alem Ketema Keflie Gebretsadik Shegaw Anagaw Mengistie Muluye Fentie Admas |
| author_facet | Ashagrew Liyih Alem Ketema Keflie Gebretsadik Shegaw Anagaw Mengistie Muluye Fentie Admas |
| author_sort | Ashagrew Liyih Alem |
| collection | DOAJ |
| description | Abstract Software requirement smell detection is an important part of establishing high-quality software specifications. These smells, which frequently indicate difficulties like ambiguity, vagueness, or incompleteness, can lead to misunderstandings and mistakes in the latter phases of software development. Traditionally, identifying requirement smells was a manual process, time-consuming, prone to inconsistency, and human mistakes. Moreover, the previous machine learning and deep learning research was insufficient for detecting multiple smells in a single requirement statement. To address this problem, we developed a multi-label software requirement smell model to detect multiple software requirement smells in a single requirement. Therefore, this study explores a deep learning-based approach to multi-label classification of software requirement smells, incorporating advanced neural network architectures such as LSTM, Bi-LSTM, and GRU with combined word embedding like ELMo and Word2Vec. We collected and prepared an 8120 requirements dataset from different sources categorized into 11 linguistic aspects and we used a binary relevance multi-label classification strategy in which each category was treated independently and used the F1-macro average of each label of the smell. Next, we built models that can classify software requirement smell in a multi-label manner using deep learning algorithms. After executing numerous experiments with different parameters in the Bi-LSTM, LSTM, and GRU models, we obtained 90.3%, 89%, and 88.7% of F1-score macro averages with ELMo, respectively. Therefore, Bi-LSTM achieved a greater F1-score macro average than the other algorithms. |
| format | Article |
| id | doaj-art-eeb019d8ae824c1c8c8afe0517465388 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-eeb019d8ae824c1c8c8afe05174653882025-08-20T02:15:06ZengNature PortfolioScientific Reports2045-23222025-02-0115111610.1038/s41598-025-86673-wMulti-label software requirement smells classification using deep learningAshagrew Liyih Alem0Ketema Keflie Gebretsadik1Shegaw Anagaw Mengistie2Muluye Fentie Admas3Department of Software Engineering, Debre Markos UniversityDepartment of Software Engineering, Debre Markos UniversityDepartment of Business, University of Southeastern NorwayDepartment of Information Technology, Debre Markos UniversityAbstract Software requirement smell detection is an important part of establishing high-quality software specifications. These smells, which frequently indicate difficulties like ambiguity, vagueness, or incompleteness, can lead to misunderstandings and mistakes in the latter phases of software development. Traditionally, identifying requirement smells was a manual process, time-consuming, prone to inconsistency, and human mistakes. Moreover, the previous machine learning and deep learning research was insufficient for detecting multiple smells in a single requirement statement. To address this problem, we developed a multi-label software requirement smell model to detect multiple software requirement smells in a single requirement. Therefore, this study explores a deep learning-based approach to multi-label classification of software requirement smells, incorporating advanced neural network architectures such as LSTM, Bi-LSTM, and GRU with combined word embedding like ELMo and Word2Vec. We collected and prepared an 8120 requirements dataset from different sources categorized into 11 linguistic aspects and we used a binary relevance multi-label classification strategy in which each category was treated independently and used the F1-macro average of each label of the smell. Next, we built models that can classify software requirement smell in a multi-label manner using deep learning algorithms. After executing numerous experiments with different parameters in the Bi-LSTM, LSTM, and GRU models, we obtained 90.3%, 89%, and 88.7% of F1-score macro averages with ELMo, respectively. Therefore, Bi-LSTM achieved a greater F1-score macro average than the other algorithms.https://doi.org/10.1038/s41598-025-86673-wMulti-label classificationSoftware RequirementRequirement smellWord embeddingNLPDeep learning |
| spellingShingle | Ashagrew Liyih Alem Ketema Keflie Gebretsadik Shegaw Anagaw Mengistie Muluye Fentie Admas Multi-label software requirement smells classification using deep learning Scientific Reports Multi-label classification Software Requirement Requirement smell Word embedding NLP Deep learning |
| title | Multi-label software requirement smells classification using deep learning |
| title_full | Multi-label software requirement smells classification using deep learning |
| title_fullStr | Multi-label software requirement smells classification using deep learning |
| title_full_unstemmed | Multi-label software requirement smells classification using deep learning |
| title_short | Multi-label software requirement smells classification using deep learning |
| title_sort | multi label software requirement smells classification using deep learning |
| topic | Multi-label classification Software Requirement Requirement smell Word embedding NLP Deep learning |
| url | https://doi.org/10.1038/s41598-025-86673-w |
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