Automated requirements engineering framework for agile model-driven development
IntroductionAdvances in requirements engineering, driven by various paradigms and methodologies, have significantly influenced software development practices. The integration of agile methodologies and model-driven development (MDE) has become increasingly critical in modern software engineering. MD...
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| Veröffentlicht in: | Frontiers in computer science (Lausanne) Jg. 7 |
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26.02.2025
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| Abstract | IntroductionAdvances in requirements engineering, driven by various paradigms and methodologies, have significantly influenced software development practices. The integration of agile methodologies and model-driven development (MDE) has become increasingly critical in modern software engineering. MDE emphasizes the use of models throughout the development process, necessitating structured approaches for handling requirements written in natural language.MethodsThis paper proposes an automated requirements engineering framework for agile model-driven development to enhance the formalization and analysis of textual requirements. The framework employs machine learning models to extract essential components from requirements specifications, focusing specifically on class diagrams. A comprehensive dataset of requirements specification problems was developed to train and validate the framework's effectiveness.ResultsThe framework was evaluated using comparative evaluation and two real-world experimental studies in the medical and information systems domains. The results demonstrated its applicability in diverse and complex software development environments, highlighting its ability to enhance requirements formalization.DiscussionThe findings contribute to the advancement of automated requirements engineering and agile model-driven development, reinforcing the role of machine learning in improving software requirements analysis. The framework's success underscores its potential for widespread adoption in software development practices. |
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| AbstractList | IntroductionAdvances in requirements engineering, driven by various paradigms and methodologies, have significantly influenced software development practices. The integration of agile methodologies and model-driven development (MDE) has become increasingly critical in modern software engineering. MDE emphasizes the use of models throughout the development process, necessitating structured approaches for handling requirements written in natural language.MethodsThis paper proposes an automated requirements engineering framework for agile model-driven development to enhance the formalization and analysis of textual requirements. The framework employs machine learning models to extract essential components from requirements specifications, focusing specifically on class diagrams. A comprehensive dataset of requirements specification problems was developed to train and validate the framework's effectiveness.ResultsThe framework was evaluated using comparative evaluation and two real-world experimental studies in the medical and information systems domains. The results demonstrated its applicability in diverse and complex software development environments, highlighting its ability to enhance requirements formalization.DiscussionThe findings contribute to the advancement of automated requirements engineering and agile model-driven development, reinforcing the role of machine learning in improving software requirements analysis. The framework's success underscores its potential for widespread adoption in software development practices. |
| Author | Umar, Muhammad Aminu Abubakar, Abdullahi Kutiriko Lano, Kevin |
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| Cites_doi | 10.1109/ASE.2013.6693146 10.1007/s10270-021-00942-6 10.1007/s00766-023-00411-0 10.1145/2068783.2068786 10.1007/978-3-540-76292-8_28 10.1145/2590748.2590768 10.1109/CCECE.2017.7946792 10.1109/ASE.2009.48 10.1109/ICCRD.2010.71 10.1109/IACS.2015.7103200 10.1109/SERA.2008.34 10.1109/ICTER.2016.7829928 10.1016/j.patter.2021.100336 10.1007/s00766-017-0270-1 10.1109/WCCAIS.2014.6916558 10.1109/ICSE.2003.1201189 10.1007/s00766-009-0079-7 10.1145/1953355.1953378 10.1016/j.procs.2018.04.010 10.1016/j.infsof.2018.04.010 10.1007/s10270-023-01105-5 10.1109/ICIMT.2009.47 10.1016/j.comcom.2023.11.002 10.18417/emisa.18.3 10.1109/ICECCME57830.2023.10253030 10.1016/j.infsof.2013.03.008 10.1016/j.procs.2020.03.148 10.1016/j.chb.2014.10.046 10.1109/SERA.2016.7516136 10.1007/978-981-10-4154-9_56 10.1145/2699697 10.1109/AIRE.2015.7337625 10.1145/3550356.3561592 10.1145/3444689 10.1109/PIC.2016.7949577 10.1023/A:1022916028950 10.1016/j.jss.2013.08.036 10.1109/FormaliSE.2017.21 |
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