AI-Assisted Requirements Engineering for Agile Software Development: A Transformer-Based Framework for Requirement Classification, Conflict Detection, and Traceability
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V5I4P120Keywords:
Requirements Engineering, Agile Software Development, Transformers, BERT, Conflict Detection, Requirement Classification, Traceability, Software Governance, Machine Learning, Natural Language ProcessingAbstract
Agile software development relies on rapid elicitation, continuous refinement, and iterative delivery of requirements, yet many teams still manage requirements through manual story grooming, spreadsheet-based trace matrices, and ad hoc quality checks. These practices are increasingly insufficient when software systems must satisfy complex functional expectations, non-functional constraints, regulatory obligations, cybersecurity controls, and architecture-dependent deployment risks. This paper proposes T-REACT, a transformer-based requirements engineering framework for agile software development that integrates requirement classification, semantic conflict detection, and automated traceability into a unified decision-support pipeline. Rather than treating requirements engineering as a document-production activity, the proposed framework models backlog items, user stories, acceptance criteria, test cases, defects, architecture decisions, and deployment risks as interconnected semantic artifacts. The framework uses contextual embeddings, fine-tuned requirement classifiers, pairwise contradiction scoring, dependency-aware graph reasoning, and human-in-the-loop governance to support sprint planning, backlog refinement, change impact analysis, and release readiness decisions. The study follows a design-science research approach and presents the framework architecture, data model, model-training workflow, evaluation protocol, agile integration pattern, and governance controls. A demonstration scenario shows how the framework identifies requirement categories, highlights likely conflicts, recommends trace links, and produces auditable explanations for product owners, business analysts, architects, testers, and compliance stakeholders. The paper contributes an end-to-end research artifact that connects transformer-based natural language understanding with agile lifecycle governance, emphasizing explainability, traceability, privacy, continuous learning, and operational adoption. The proposed framework is intended to reduce ambiguity, improve backlog quality, support earlier defect prevention, and strengthen alignment among requirements, design, testing, and release decisions.
References
[1] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30, 5998–6008.
[2] Thalakanti, R. R., & Goud Bandari, S. S. (2024). Intelligent Continuous Integration and Delivery for Banking Systems using Machine Learning Driven Risk Detection with Real World Deployment Evaluation. International Journal of AI, BigData, Computational and Management Studies, 5(4), 168-175. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V5I4P118
[3] Cleland-Huang, J., Settimi, R., Zou, X., & Solc, P. (2007). Automated classification of non-functional requirements. Requirements Engineering, 12(2), 103–120. https://doi.org/10.1007/s00766-007-0045-1
[4] Gudi, S. R. (2023). Enhancing Reliability in Java Enterprise Systems through Comparative Analysis of Automated Testing Frameworks. International Journal of Emerging Trends in Computer Science and Information Technology, 4(2), 151-160. https://doi.org/10.63282/3050-9246.IJETCSIT-V4I2P115
[5] Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 4171–4186.
[6] Bandari, S. S. G., Sivva, S. D., & Thalakanti, R. R. (2024). Regulatory Grade Fraud Detection using Explainable Artificial Intelligence with Auditable Decision Pathways and Empirical Validation on Banking Data. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 5(3), 139-147. https://doi.org/10.63282/3050-9262.IJAIDSML-V5I3P115
[7] Hayes, J. H., Dekhtyar, A., & Osborne, J. (2003). Improving requirements tracing via information retrieval. Proceedings of the 11th IEEE International Requirements Engineering Conference, 151–161.
[8] Gunda SK, Yettapu SDR, Bodakunti S, Bikki SB. Decision Intelligence Methodology for AI-Driven Agile Software Lifecycle Governance and Architecture-Centered Project Management, 2023 Mar. 30;4(1):102-8. https://doi.org/10.63282/3050-9262.IJAIDSML-V4I1P112
[9] ISO/IEC/IEEE. (2018). ISO/IEC/IEEE 29148:2018 Systems and software engineering—Life cycle processes—Requirements engineering. International Organization for Standardization.
[10] Sivva, S. D. (2023). An end-to-end AI-based systems engineering paradigm for lifecycle governance, predictive quality assurance, automation economics, and cybersecurity intelligence. Journal of Frontiers in Multidisciplinary Research, 4(1), 600–604. https://doi.org/10.54660/.JFMR.2023.4.1.600-604
[11] Reimers, N., & Gurevych, I. (2019). Sentence-BERT: Sentence embeddings using Siamese BERT-networks. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, 3982–3992.
[12] Balerao, M. (2023). A converged artificial intelligence architecture for innovation, software lifecycle optimization, and cybersecurity risk mitigation. International Journal of Multidisciplinary Futuristic Development, 4(1), 117–120. https://doi.org/10.54660/IJMFD.2023.4.1.117-120
[13] Kurtanović, Z., & Maalej, W. (2017). Automatically classifying functional and non-functional requirements using supervised machine learning. Proceedings of the 25th IEEE International Requirements Engineering Conference, 490–495.
[14] Gudi, S. R. (2024). AI-Driven Fax-to-Digital Prescription Automation: A Cloud-Native Framework Using OCR, Machine Learning, and Microservices for Pharmacy Operations. International Journal of Emerging Research in Engineering and Technology, 5(1), 111-116. https://doi.org/10.63282/3050-922X.IJERET-V5I1P113
[15] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). RoBERTa: A robustly optimized BERT pretraining approach. arXiv preprint arXiv:1907.11692.
[16] Manga, I., Sivva, S. D. ., & Manga, V. K. (2024). The Adaptive Intelligence in Cloud Systems: A Unified Architecture for AI Enhanced Observability and Automated Root Cause Analysis. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 5(1), 160-166. https://doi.org/10.63282/3050-9262.IJAIDSML-V5I1P115
[17] Dalpiaz, F., Dell’Anna, D., Aydemir, F. B., & Çevikol, S. (2019). Requirements classification with interpretable machine learning and dependency parsing. Proceedings of the 27th IEEE International Requirements Engineering Conference, 142–152.
[18] Thalakanti, R. R., Goud Bandari, S. S., & Sivva, S. D. . (2024). Federated Learning for Privacy Preserving Fraud Detection across Financial Institutions: Architecture Protocols and Operational Governance. International Journal of Emerging Research in Engineering and Technology, 5(2), 108-114. https://doi.org/10.63282/3050-922X.IJERET-V5I2P111
[19] Borg, M., Runeson, P., & Ardö, A. (2014). Recovering from a decade: A systematic mapping of information retrieval approaches to software traceability. Empirical Software Engineering, 19(6), 1565–1616. https://doi.org/10.1007/s10664-013-9255-y
[20] S. K. Gunda, “Comparative Analysis of Machine Learning Models for Software Defect Prediction,” 2024 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS), Chennai, India, 2024, pp. 1-6, https://doi.org/10.1109/ICPECTS62210.2024.10780167
[21] IEEE Computer Society. (1998). IEEE Recommended Practice for Software Requirements Specifications: IEEE Std 830-1998. Institute of Electrical and Electronics Engineers.
[22] Gudi, S. R. (2024). Leveraging Predictive Analytics and Redis-Backed Caching to Optimize Specialty Medication Fulfillment and Pharmacy Inventory Management. International Journal of AI, BigData, Computational and Management Studies, 5(3), 155-160. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V5I3P116
[23] Li, B., Liang, Z., Li, Z., & Zhu, L. (2022). Automatically classifying non-functional requirements using deep neural network. Pattern Recognition, 132, 108948. https://doi.org/10.1016/j.patcog.2022.108948
[24] Mutyam, N. (2024). Graph-based modeling of service dependencies for predicting failure propagation in distributed systems. International Journal of Multidisciplinary Evolutionary Research, 5(1), 113–116. https://doi.org/10.54660/IJMER.2024.5.1.113-116
[25] Elhassan, H., Abaker, M., Abdelmaboud, A., & Rehman, M. B. (2022). Requirements engineering: Conflict detection automation using machine learning. Intelligent Automation & Soft Computing, 33(1), 259–273. https://doi.org/10.32604/iasc.2022.023750
[26] Gunda, S. K. G. (2023). The Future of Software Development and the Expanding Role of ML Models. International Journal of Emerging Research in Engineering and Technology, 4(2), 126-129. https://doi.org/10.63282/3050-922X.IJERET-V4I2P113
[27] Canedo, E. D., & Mendes, B. C. (2020). Software requirements classification using machine learning algorithms. Entropy, 22(9), 1057. https://doi.org/10.3390/e22091057
[28] Sivva, S. D., Thalakanti, R. R., Bandari, S. S. G., & Yettapu, S. D. R. (2023). AI-Driven Decision Intelligence for Agile Software Lifecycle Governance: An Architecture-Centered Framework Integrating Machine Learning Defect Prediction and Automated Testing. International Journal of Emerging Trends in Computer Science and Information Technology, 4(4), 167-172. https://doi.org/10.63282/3050-9246.IJETCSIT-V4I4P118
[29] S. K. Gunda, “Fault Prediction Unveiled: Analyzing the Effectiveness of Random Forest, Logistic Regression, and KNeighbors,” 2024 2nd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS), Erode, India, 2024, pp. 107-113, https://doi.org/10.1109/ICSSAS64001.2024.10760620
[30] Gudi, S. R. (2024). Design and Evaluation of Secure Microservices Architecture for HIPAA-Compliant Prescription Processing on AWS and OpenShift. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 5(2), 144-149. https://doi.org/10.63282/3050-9262.IJAIDSML-V5I2P116
