Intelligent Automation Techniques for Software Development Lifecycle Enhancement Using Artificial Intelligence
Keywords:
Artificial Intelligence, Software Development Lifecycle, Intelligent Automation, Machine Learning, Software Testing, NLP, AI in SDLCSynopsis
Artificial Intelligence (AI) is revolutionizing software development by introducing intelligent automation at various stages of the Software Development Lifecycle (SDLC). This study presents a structured overview of how AI techniques such as Natural Language Processing (NLP), Machine Learning (ML), and Deep Learning (DL) are being integrated to automate requirements gathering, code generation, testing, and maintenance. The primary objective is to explore the impact of AI on enhancing software quality, reducing development time, and improving decision-making across SDLC phases. The findings indicate a substantial efficiency gain, error reduction, and better scalability when AI-based automation tools are employed strategically. Moreover, this research identifies challenges such as data dependency, interpretability issues, and tool maturity, offering a roadmap for future enhancements.
References
[1] Tufano, M., Watson, C., Bavota, G., et al. (2019). "An Empirical Study on Learning Bug-Fixing Patches in the Wild." ACM Transactions on Software Engineering and Methodology, Vol. 28, Issue 2.
[2] Gundaboina A. Data Loss Prevention in Healthcare: Advanced Strategies for Protecting PHI in Cloud Environments. Journal of Artificial Intelligence, Machine Learning and Data Science 2023 1(2), 3045-3051. DOI: doi.org/10.51219/JAIMLD/anjan-gundaboina/628
[3] Gupta, R., Sharma, P., & Ranjan, A. (2020). "Reinforcement Learning for Adaptive Test Automation." IEEE Transactions on Software Engineering, Vol. 46, Issue 7.
[4] Ahmad, N., Khan, I., & Baig, M. (2022). "Automated Requirement Classification Using NLP Techniques." Journal of Systems and Software, Vol. 183, Issue 4.
[5] Gundaboina, A. (2024). HITRUST Certification Best Practices: Streamlining Compliance for Healthcare Cloud Solutions. International Journal of Computer Science and Information Technology Research, 5(1), 76–94. https://ijcsitr.org/index.php/home/article/view/IJCSITR_2024_05_01_008
[6] Chen, L., Zhou, Y., & Wang, Z. (2021). "Automated Bug Fixing Using AI-Assisted Techniques." Empirical Software Engineering, Vol. 26, Issue 5.
[7] Vasilescu, B., Filkov, V., & Serebrenik, A. (2020). "Reducing Developer Burnout via Intelligent Automation." Software Quality Journal, Vol. 28, Issue 3.
[8] Uppuluri, V. (2023). Design and Deployment of Predictive Models for Influenza Breakthrough Infections Using Pharmacy Test Data. Journal of Artificial Intelligence, Machine Learning & Data Science, 1(2), 3031–3037. https://doi.org/10.51219/JAIMLD/vijitha-uppuluri/626
[9] Zhang, H., Li, J., & Wei, Y. (2023). "Data Bias in AI Code Completion Tools." Information and Software Technology, Vol. 149, Issue 2.
[10] Menzies, T., & Pecheur, C. (2021). "Trustworthy AI in Software Engineering." IEEE Software, Vol. 38, Issue 1.
[11] Potla, R.B. (2023). Supplier Collaboration Portals for Component Manufacturers: Procure-to-Pay Automation and Working-Capital Outcomes. International Journal of Artificial Intelligence (ISCSITR-IJAI), 4(1), 16–40. https://doi.org/10.63397/ISCSITR-IJAI_04_01_002
[12] Nguyen, A., Kim, D., & Lee, H. (2022). "Neural Code Generation from Natural Language." ACM Computing Surveys, Vol. 54, Issue 9.
[13] Li, J., Sun, L., & Zhao, M. (2020). "AI-Based Software Refactoring Suggestions." Journal of Software Maintenance and Evolution, Vol. 32, Issue 7.
[14] Vallemoni, R.K. (2023). Merchant Onboarding and Risk Scoring: Data Governance, Master Data, and Golden-Record Strategies. ISCSITR - International Journal of Scientific Research in Information Technology (ISCSITR-IJSRIT), 4(1), 16–41. https://doi.org/10.63397/ISCSITR-IJSRIT_04_01_002
[15] Han, Y., & Xu, B. (2021). "Semantic Bug Detection in Machine-Generated Code." Software Testing, Verification & Reliability, Vol. 31, Issue 5.
[16] Costa, M., & Zhao, X. (2019). "Automatic UI Testing with Deep Learning." Automation in Software Engineering, Vol. 17, Issue 3.
[17] Fernando, D., & Singh, R. (2020). "Continuous Integration of AI in DevOps." IEEE Software Engineering Notes, Vol. 45, Issue 6.
[18] Vallemoni, R.K. (2023). Data Lineage and Metadata in Payment Ecosystems: Auditability and Regulatory Readiness across the Life Cycle. Frontiers in Computer Science and Artificial Intelligence, 2(1), 46–58. https://doi.org/10.32996/fcsai.2023.2.1.5
[19] Kaur, P., & Mehta, D. (2023). "Symbolic AI for Requirement Validation." Requirements Engineering Journal, Vol. 29, Issue 2.
[20] Tan, C., & Lin, Z. (2018). "Software Anti-pattern Detection Using AI." Journal of Software Architecture, Vol. 16, Issue 1.
[21] Zhao, F., & Green, J. (2021). "AI Explainability in Software Automation Tools." AI & Society, Vol. 36, Issue 4.
Published
Series
Categories
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
