A Critical Review of Artificial Intelligence for Code Generation, Testing and Security Assurance Across the Software Development Lifecycle

Emmanuel E. Ajala¹, Toussida Fatah Tanguy Minoungou², Harouna Wendpanga Yann Christian Sankara³, Oluwademilade Oreoluwa Jooda⁴, Chukwunonyelum Rosalyn Ezeako⁵

¹ Department of Computer Science, University of Alabama at Birmingham, Birmingham, Alabama, United States.

² DonorsChoose, New York, United States.

³ Five9, San Ramon, California, United States.

⁴ Goldman Sachs, Dallas, Texas, United States.

⁵ University of Central Missouri, Warrensburg, Missouri, United States.

Abstract

Large language models trained on source code have moved artificial intelligence from a research curiosity at the edges of software engineering to a working component of everyday development practice. This review examines what the published evidence establishes about that shift across five phases of the software development lifecycle: requirements and design, implementation, testing, review and security assurance, and operations and maintenance. It synthesises more than 120 sources published between 2012 and 2025, from peer-reviewed empirical studies and benchmarks to practitioner frameworks and standards, and appraises the 24 primary empirical studies on which its assessments most depend against four criteria that separate replicated findings from claims resting on a single study or vendor-reported figures. The review finds that productivity gains from code assistants are real but uneven, that generated code carries a measurable security debt that users under-detect, that learned vulnerability detectors lose most of their reported accuracy when evaluated on realistic data, and that test and fuzz generation is the most consistently positive application area. It identifies benchmark contamination, label noise in vulnerability datasets, and package hallucination as three defects in the evidence base that distort current conclusions. The paper proposes an assurance loop that treats every model output as an untrusted contribution subject to static, dynamic, and human gates with recorded provenance.

Keywords: Large language models; Code generation; Software testing; Vulnerability detection; Secure software engineering; DevSecOps; Empirical software engineering

References

1. Adams, V. (2023) ‘Preserving critical thinking in the era of generative artificial intelligence: evidence-based frameworks for human-centered learning’, International Journal of Social Sciences and Management Research, 9(11), pp. 321-332. https://doi.org/10.56201/ijssmr.v9.no11.2023.pg321.332
2. Adebayo, A., Adegbite, M.P. and Ahmed, M.O. (2022) ‘Adversarial machine learning in critical infrastructure: a conceptual framework for threat modeling AI-enabled OT systems’, World Journal of Innovation and Modern Technology, 6(1), pp. 184-234. https://doi.org/10.56201/wjimt.v6.no1.2022.pg184.234
3. Adebayo, A., Adegbite, M.P. and Ahmed, M.O. (2023) ‘AI-augmented threat detection in industrial control systems: a systematic review of machine learning approaches for ICS anomaly detection’, International Journal of Engineering and Modern Technology, 9(3), pp. 287-340. https://doi.org/10.56201/ijcsmt.v9.no3.2023.pg287.340
4. Adegbite, M.P., Adebayo, A. and Ahmed, M.O. (2024a) ‘A vulnerability governance architecture for power and utilities corporations: from exposure mapping to remediation verification’, World Journal of Innovation and Modern Technology, 8(6), pp. 185-246. https://doi.org/10.56201/wjimt.v8.no6.2024.pg185.246
5. Adegbite, M.P., Adebayo, A. and Ahmed, M.O. (2024b) ‘An AI-driven security operations architecture for utility sector SOCs: integrating threat intelligence, behavioral analytics, and automated response’, International Journal of Engineering and Modern Technology, 10(11), pp. 197-256. https://doi.org/10.56201/ijemt.v10.no11.2024.pg197.256
6. Adeniji, I.O., Shittu, H., Shittu, M.A., et al. (2025) ‘Mitigating harmonic distortion in urban distribution networks using hybrid AI-based control models’, International Journal of Advanced Multidisciplinary Research and Studies, 5(6), pp. 2055-2068.
7. Ahmed, K.S., Odejobi, O.D. and Oshoba, T.O. (2020) ‘Predictive model for cloud resource scaling using machine learning techniques’, Journal of Frontiers in Multidisciplinary Research, 1(1), pp. 173-183.
8. Ahmed, M.O., Adegbite, M.P. and Adebayo, A. (2021) ‘Zero trust architecture for operational technology in North American critical infrastructure: a framework for implementation and resilience optimization’, International Journal of Engineering and Modern Technology, 7(1), pp. 66-113. https://doi.org/10.56201/ijemt.vol.7.no1.2021.pg66.113
9. Ahmed, T., Ghosh, S., Bansal, C., et al. (2023) ‘Recommending root-cause and mitigation steps for cloud incidents using large language models’. Proceedings of the 45^th IEEE/ACM International Conference on Software Engineering (ICSE 2023). Melbourne, Australia, pp. 1737-1749. https://doi.org/10.1109/ICSE48619.2023.00149
10. Aileme, J.I., Sule, A. and Obukadata, V.O. (2024) ‘From ambiguity to AI-ready requirements: a business analysis framework for structuring business problems for intelligent process automation’, International Journal of Multidisciplinary Research and Growth Evaluation, 5(6), pp. 2088-2117. https://doi.org/10.54660/IJMRGE.2024.5.6.2088-2117
11. Aileme, J.I., Sule, A. and Obukadata, V.O. (2025) ‘Requirements elicitation for AI-enabled process automation: a comparative review of Agile, Waterfall, and Hybrid implementation approaches’, Gulf Journal of Computer Sciences, 1(4), pp. 116-169.
12. Ailile, C., Mbonu, I.S. and Iwuanyanwu, U. (2023) ‘A conceptual framework for continuous cloud misconfiguration monitoring and enterprise risk mitigation strategies’, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 9(10), pp. 373-394. https://doi.org/10.32628/CSEIT2361071
13. Ailile, C., Mbonu, I.S., Uzoka, E., et al. (2025) ‘A review of AI-assisted continuous auditing systems in technology risk and cybersecurity oversight’, Gyansharyam, International Scientific Refereed Research Journal, 8(4), pp. 210-250. https://doi.org/10.32628/GISRRJ258369
14. Ajagbe, S.A., Akindolani, A., Akande, T.O., Adeyanju, K.I., Aiyeniko, O. and Ajiboye, G.O. (2025) ‘Intrusion detection system with feature selection on machine learning algorithm’. Proceedings of the 6th International Conference and Workshop on Engineering and Technology Research (ICWETR) LAUFET 2025. pp. 24-39.
15. Amadi, C., Adebayo, A. and Ijagbemi, A.O. (2025a) ‘AI-based security posture management as an enabling layer for real-time NIST Cybersecurity Framework adherence: a theory-grounded conceptual framework’, World Journal of Innovation and Modern Technology, 9(8), pp. 99-147. https://doi.org/10.56201/wjmt.v9.no8.2025.pg99.147
16. Amadi, C., Adebayo, A. and Ijagbemi, A.O. (2025b) ‘The cyber-physical attack surface of legacy SCADA networks: a systematic review of vulnerabilities, threats, and detection gaps in critical infrastructure’, International Journal of Computer Science and Mathematical Theory, 11(8), pp. 135-169. https://doi.org/10.56201/ijcsmt.vol.11.no8.2025.pg135.169
17. Amershi, S., Begel, A., Bird, C., et al. (2019) ‘Software engineering for machine learning: a case study’. Proceedings of the 41^st IEEE/ACM International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP 2019). Montreal, Canada, pp. 291-300. https://doi.org/10.1109/ICSE-SEIP.2019.00042
18. Annan, A.O. (2024) ‘Algorithmic accountability and trade secret protection in artificial intelligence’, Shodhshayaram, International Scientific Refereed Research Journal, 7(5), pp. 315-347.
19. Arora, C., Grundy, J. and Abdelrazek, M. (2024) ‘Advancing requirements engineering through generative AI: assessing the role of LLMs’. In: Generative AI for effective software development; Nguyen-Duc, A., Abrahamsson, P. and Khomh, F. Cham: Springer, pp. 129-148. https://doi.org/10.1007/978-3-031-55642-5_6
20. Asare, O., Nagappan, M. and Asokan, N. (2023) ‘Is GitHub’s Copilot as bad as humans at introducing vulnerabilities in code?’, Empirical Software Engineering, 28, 129. https://doi.org/10.1007/s10664-023-10380-1
21. Badmus, O., Dosunmu, A.A. and Anunagba, C.O. (2025) ‘A governance framework for AI-assisted CRM workflows: Agentforce deployment, risk management, and organizational readiness in enterprise Salesforce environments’, International Journal of Scientific Research in Humanities and Social Sciences, 2(1), pp. 59-80.
22. Badmus, O., Dosunmu, A.A. and Ozowara, D.E. (2018) ‘A systematic review of CI/CD pipeline strategies in Salesforce DevOps: tools, practices, and deployment outcomes’, Iconic Research and Engineering Journals, 2(6), pp. 319-333.
23. Badmus, O., Dosunmu, A.A., Ozowara, D.E., et al. (2020) ‘A comparative framework for Salesforce DevOps tooling: evaluating Copado, Flosum, and Salesforce DX across enterprise deployment contexts’, International Journal of Multidisciplinary Research and Growth Evaluation, 1(5), pp. 1021-1031. https://doi.org/10.54660/IJMRGE.2020.1.5.1021-1031
24. Badmus, O., Jooda, D., Ozowara, D.E., et al. (2022) ‘A framework for Git-based version control and code review governance in Salesforce development teams’, Shodhshauryam, International Scientific Refereed Research Journal, 5(2), pp. 473-493.
25. Barke, S., James, M.B. and Polikarpova, N. (2023) ‘Grounded Copilot: how programmers interact with code-generating models’. Proceedings of the ACM on Programming Languages, 7(OOPSLA1), 78, pp. 85-111. https://doi.org/10.1145/3586030
26. Becker, B.A., Denny, P., Finnie-Ansley, J., et al. (2023) ‘Programming is hard, or at least it used to be: educational opportunities and challenges of AI code generation’. Proceedings of the 54^th ACM Technical Symposium on Computer Science Education (SIGCSE 2023). Toronto, Canada, pp. 500-506. https://doi.org/10.1145/3545945.3569759
27. Borah, S., Aliliele, K.C., Rakshit, S., et al. (2022) ‘Applications of artificial intelligence in software testing’. In: Cognitive informatics and soft computing; Mallick, P.K. et al. (ed.). Lecture Notes in Networks and Systems, 375. Singapore: Springer, pp. 727-736. https://doi.org/10.1007/978-981-16-8763-1_60
28. Carlini, N., Tramèr, F., Wallace, E., et al. (2021) ‘Extracting training data from large language models’. Proceedings of the 30^th USENIX Security Symposium. pp. 2633-2650.
29. Chakraborty, S., Krishna, R., Ding, Y., et al. (2022) ‘Deep learning based vulnerability detection: are we there yet?’, IEEE Transactions on Software Engineering, 48(9), pp. 3280-3296. https://doi.org/10.1109/TSE.2021.3087402
30. Chen, M., Tworek, J., Jun, H., et al. (2021) ‘Evaluating large language models trained on code’, arXiv preprint, arXiv:2107.03374 (technical report). https://doi.org/10.48550/arXiv.2107.03374
31. Chen, Y., Xie, H., Ma, M., et al. (2024) ‘Automatic root cause analysis via large language models for cloud incidents’. Proceedings of the Nineteenth European Conference on Computer Systems (EuroSys 2024). Athens, Greece, pp. 674-688. https://doi.org/10.1145/3627703.3629553
32. Chibwaye, I.P. and Oteri, O. (2023) ‘Advances and conceptual models in data integrated ERP migration for large scale government workforce transformation’, International Journal of Advanced Multidisciplinary Research and Studies, 3(6), pp. 2712-2726.
33. Croft, R., Babar, M.A. and Kholoosi, M.M. (2023) ‘Data quality for software vulnerability datasets’. Proceedings of the 45^th IEEE/ACM International Conference on Software Engineering (ICSE 2023). Melbourne, Australia, pp. 121-133. https://doi.org/10.1109/ICSE48619.2023.00022
34. Dakhel, A.M., Majdinasab, V., Nikanjam, A., et al. (2023) ‘GitHub Copilot AI pair programmer: asset or liability?’, Journal of Systems and Software, 203, 111734. https://doi.org/10.1016/j.jss.2023.111734
35. Dang, Y., Lin, Q. and Huang, P. (2019) ‘AIops: real-world challenges and research innovations’. Proceedings of the 41^st IEEE/ACM International Conference on Software Engineering: Companion Proceedings (ICSE-Companion 2019). Montreal, Canada, pp. 4-5.
36. Deng, Y., Xia, C.S., Peng, H., et al. (2023) ‘Large language models are zero-shot fuzzers: fuzzing deep-learning libraries via large language models’. Proceedings of the 32^nd ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA 2023). Seattle, USA, pp. 423-435. https://doi.org/10.1145/3597926.3598067
37. Ding, Y., Fu, Y., Ibrahim, O., et al. (2025) ‘Vulnerability detection with code language models: how far are we?’. Proceedings of the 47^th IEEE/ACM International Conference on Software Engineering (ICSE 2025). Ottawa, Canada, pp. 469-481.
38. Dosunmu, A.A. and Ogundele, P.O. (2022) ‘Threat intelligence integration frameworks supporting proactive enterprise cybersecurity decision making’, Gyanshauryam, International Scientific Refereed Research Journal, 5(3), pp. 397-416.
39. Dosunmu, A.A. and Ogundele, P.O. (2025) ‘Security orchestration and automation models for accelerating incident detection and response’, Computer Science and IT Research Journal, 6(11), pp. 878-894. https://doi.org/10.51594/csitrj.v6i11.2163
40. Elebe, O. and Anunagba, C.O. (2025) ‘Responsible artificial intelligence in IT auditing through evidence validation, human oversight, and regulatory compliance’, International Journal of Technology, Health and Sustainability, 1(2), pp. 410-428.
41. Essandoh, S., Sakyi, J.K., Ibrahim, A.K., et al. (2025) ‘Artificial intelligence and the future of work: impacts on employment and job roles’, International Journal of Multidisciplinary Futuristic Development, 6(1), pp. 31-41. https://doi.org/10.54660/IJMFD.2025.6.1.31-41
42. Eyetsemitan, R.A., Ambali, K.B., Oyeleye, A.O., et al. (2023) ‘User acceptance testing in small business technology deployment: a structured validation framework for lean operational environments’, International Journal of Multidisciplinary Research and Growth Evaluation, 4(6), pp. 1512-1531. https://doi.org/10.54660/IJMRGE.2023.4.6.1512-1531
43. Fadayomi, O., Abolaji, T.O., Edivri, J., et al. (2019) ‘Risk-based cybersecurity assurance and data availability: limitations, advances and future research opportunities’, Iconic Research and Engineering Journals, 2(12), pp. 602-617. https://doi.org/10.64388/IREV2I12-1713779
44. Fadero, S., Jooda, D., Oyesiji, S.O., et al. (2025) ‘IT services decoupling after mergers, acquisitions, and ownership transitions: a framework for secure infrastructure separation’, International Journal of Advanced Multidisciplinary Research and Studies, 5(6), pp. 2471-2485.
45. Fan, A., Gokkaya, B., Harman, M., et al. (2023) ‘Large language models for software engineering: survey and open problems’. Proceedings of the 2023 IEEE/ACM International Conference on Software Engineering: Future of Software Engineering (ICSE-FoSE). Melbourne, Australia, pp. 31-53. https://doi.org/10.1109/ICSE-FoSE59343.2023.00008
46. Feng, Z., Guo, D., Tang, D., et al. (2020) ‘CodeBERT: a pre-trained model for programming and natural languages’. Findings of the Association for Computational Linguistics: EMNLP 2020. pp. 1536-1547. https://doi.org/10.18653/v1/2020.findings-emnlp.139
47. Fraser, G. and Arcuri, A. (2011) ‘EvoSuite: automatic test suite generation for object-oriented software’. Proceedings of the 19^th ACM SIGSOFT Symposium and the 13^th European Conference on Foundations of Software Engineering (ESEC/FSE 2011). Szeged, Hungary, pp. 416-419. https://doi.org/10.1145/2025113.2025179
48. Fu, M. and Tantithamthavorn, C. (2022) ‘LineVul: a transformer-based line-level vulnerability prediction’. Proceedings of the 19^th International Conference on Mining Software Repositories (MSR 2022). Pittsburgh, USA, pp. 608-620. https://doi.org/10.1145/3524842.3528452
49. Greshake, K., Abdelnabi, S., Mishra, S., et al. (2023) ‘Not what you’ve signed up for: compromising real-world LLM-integrated applications with indirect prompt injection’. Proceedings of the 16^th ACM Workshop on Artificial Intelligence and Security (AISec 2023). Copenhagen, Denmark, pp. 79-90. https://doi.org/10.1145/3605764.3623985
50. Hall, T., Beecham, S., Bowes, D., et al. (2012) ‘A systematic literature review on fault prediction performance in software engineering’, IEEE Transactions on Software Engineering, 38(6), pp. 1276-1304. https://doi.org/10.1109/TSE.2011.103
51. Hindle, A., Barr, E.T., Su, Z., et al. (2012) ‘On the naturalness of software’. Proceedings of the 34^th International Conference on Software Engineering (ICSE 2012). Zurich, Switzerland, pp. 837-847. https://doi.org/10.1109/ICSE.2012.6227135
52. Hou, X., Zhao, Y., Liu, Y., et al. (2024) ‘Large language models for software engineering: a systematic literature review’, ACM Transactions on Software Engineering and Methodology, 33(8), 220. https://doi.org/10.1145/3695988
53. Idika, C.N., Salami, E.O., Ijiga, O.M., et al. (2021) ‘Deep learning driven malware classification for cloud-native microservices in edge computing architectures’, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 7(4), pp. 710-732.
54. Ji, Z., Lee, N., Frieske, R., et al. (2023) ‘Survey of hallucination in natural language generation’, ACM Computing Surveys, 55(12), 248. https://doi.org/10.1145/3571730
55. Jimenez, C.E., Yang, J., Wettig, A., et al. (2024) ‘SWE-bench: can language models resolve real-world GitHub issues?’. Proceedings of the Twelfth International Conference on Learning Representations (ICLR 2024). Vienna, Austria.
56. Kang, S., Yoon, J. and Yoo, S. (2023) ‘Large language models are few-shot testers: exploring LLM-based general bug reproduction’. Proceedings of the 45th IEEE/ACM International Conference on Software Engineering (ICSE 2023). Melbourne, Australia, pp. 2312-2323. https://doi.org/10.1109/ICSE48619.2023.00194
57. Khoury, R., Avila, A.R., Brunelle, J., et al. (2023) ‘How secure is code generated by ChatGPT?’. Proceedings of the 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2023). Honolulu, USA, pp. 2445-2451.
58. Ladapo, O.O., Dosunmu, A.A., Jooda, D., et al. (2022a) ‘Human-in-the-loop machine learning: a state of the art’, Journal of Frontiers in Multidisciplinary Research, 3(1), pp. 656-669. https://doi.org/10.54660/JFMR.2022.3.1.656-669
59. Ladapo, O.O., Dosunmu, A.A., Jooda, D., et al. (2025) ‘Migration of applications and information systems to cloud computing infrastructure: lessons from a South African retail bank’, International Journal of Multidisciplinary Research and Growth Evaluation, 6(6), pp. 1361-1375. https://doi.org/10.54660/IJMRGE.2025.6.6.1361-1375
60. Ladapo, O.O., Jooda, D., Dosunmu, A.A., et al. (2022b) ‘Navigating digital transformation: best practices for cloud migration strategies in the enterprise’, Journal of Frontiers in Multidisciplinary Research, 3(1), pp. 643-655. https://doi.org/10.54660/JFMR.2022.3.1.643-655
61. Ladapo, O.O., Jooda, D., Dosunmu, A.A., et al. (2024) ‘Keeping humans in the loop: human-centered automated annotation with generative AI’, International Journal of Multidisciplinary Futuristic Development, 5(1), pp. 81-95. https://doi.org/10.54660/IJMFD.2024.5.1.81-95
62. Ladisa, P., Plate, H., Martinez, M., et al. (2023) ‘SoK: taxonomy of attacks on open-source software supply chains’. Proceedings of the 2023 IEEE Symposium on Security and Privacy (SP 2023). San Francisco, USA, pp. 1509-1526. https://doi.org/10.1109/SP46215.2023.10179304
63. Le Goues, C., Nguyen, T., Forrest, S., et al. (2012) ‘GenProg: a generic method for automatic software repair’, IEEE Transactions on Software Engineering, 38(1), pp. 54-72. https://doi.org/10.1109/TSE.2011.104
64. Lemieux, C., Inala, J.P., Lahiri, S.K., et al. (2023) ‘CodaMosa: escaping coverage plateaus in test generation with pre-trained large language models’. Proceedings of the 45^th IEEE/ACM International Conference on Software Engineering (ICSE 2023). Melbourne, Australia, pp. 919-931. https://doi.org/10.1109/ICSE48619.2023.00085
65. Li, Y., Choi, D., Chung, J., et al. (2022) ‘Competition-level code generation with AlphaCode’, Science, 378(6624), pp. 1092-1097. https://doi.org/10.1126/science.abq1158
66. Li, Z., Lu, S., Guo, D., et al. (2022) ‘Automating code review activities by large-scale pre-training’. Proceedings of the 30^th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2022). Singapore, pp. 1035-1047. https://doi.org/10.1145/3540250.3549081
67. Li, Z., Zou, D., Xu, S., et al. (2018) ‘VulDeePecker: a deep learning-based system for vulnerability detection’. Proceedings of the 25^th Annual Network and Distributed System Security Symposium (NDSS 2018). San Diego, USA. https://doi.org/10.14722/ndss.2018.23158
68. Liang, J.T., Yang, C. and Myers, B.A. (2024) ‘A large-scale survey on the usability of AI programming assistants: successes and challenges’. Proceedings of the 46^th IEEE/ACM International Conference on Software Engineering (ICSE 2024). Lisbon, Portugal, 52. https://doi.org/10.1145/3597503.3608128
69. Liu, J., Xia, C.S., Wang, Y., et al. (2023) ‘Is your code generated by ChatGPT really correct? Rigorous evaluation of large language models for code generation’. Advances in Neural Information Processing Systems 36 (NeurIPS 2023). New Orleans, USA.
70. Lukasczyk, S. and Fraser, G. (2022) ‘Pynguin: automated unit test generation for Python’. Proceedings of the 44^th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion 2022). Pittsburgh, USA, pp. 168-172. https://doi.org/10.1145/3510454.3516829
71. Mayo, W., Ogbole, J.I., Okoruwa, P.O., et al. (2021) ‘Designing an AI-predictive maintenance model for e-commerce systems using machine learning and cloud analytics’, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 7(5), pp. 416-440.
72. Mbonu, I.S., Aliliele, C., Iwuanyanwu, U., et al. (2020a) ‘A review of identity and access management integration strategies in hybrid and multi-cloud environments’, International Journal of Multidisciplinary Research and Growth Evaluation, 1(5), pp. 795-810. https://doi.org/10.54660/IJMRGE.2020.1.5.795-810
73. Mbonu, I.S., Aliliele, C., Iwuanyanwu, U., et al. (2021) ‘Advances in artificial intelligence techniques for secure software testing and automated regression control mechanisms’, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 7(5), pp. 468-496. https://doi.org/10.32628/CSEIT217565
74. Mbonu, I.S., Aliliele, C., Iwuanyanwu, U., et al. (2022a) ‘A conceptual framework for AI enabled IT general controls and SOX audit automation processes’, Gyansharyam, International Scientific Refereed Research Journal, 5(5), pp. 384-414. https://doi.org/10.32628/GISRRJ2256239
75. Mbonu, I.S., Iwuanyanwu, U., Aliliele, C., et al. (2020b) ‘Advances in infrastructure as code governance for secure Terraform based enterprise cloud deployments’, International Journal of Multidisciplinary Research and Growth Evaluation, 1(5), pp. 811-828. https://doi.org/10.54660/IJMRGE.2020.1.5.811-828
76. Mbonu, I.S., Iwuanyanwu, U., Aliliele, C., et al. (2022b) ‘Advances in cloud identity and access governance optimization in large scale AWS enterprise environments’, Shodhsharyam, International Scientific Refereed Research Journal, 5(3), pp. 403-438. https://doi.org/10.32628/SHISRRJ225490
77. Menzies, T., Greenwald, J. and Frank, A. (2007) ‘Data mining static code attributes to learn defect predictors’, IEEE Transactions on Software Engineering, 33(1), pp. 2-13. https://doi.org/10.1109/TSE.2007.256941
78. Monperrus, M. (2018) ‘Automatic software repair: a bibliography’, ACM Computing Surveys, 51(1), 17. https://doi.org/10.1145/3105906
79. Mozannar, H., Bansal, G., Fourney, A., et al. (2024) ‘Reading between the lines: modeling user behavior and costs in AI-assisted programming’. Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems (CHI 2024). Honolulu, USA, 142. https://doi.org/10.1145/3613904.3641936
80. Nguyen, N. and Nadi, S. (2022) ‘An empirical evaluation of GitHub Copilot’s code suggestions’. Proceedings of the 19^th International Conference on Mining Software Repositories (MSR 2022). Pittsburgh, USA, pp. 1-5. https://doi.org/10.1145/3524842.3528470
81. NIST (2022) Secure Software Development Framework (SSDF) Version 1.1: Recommendations for Mitigating the Risk of Software Vulnerabilities. NIST Special Publication 800-218. Gaithersburg, MD: National Institute of Standards and Technology. https://doi.org/10.6028/NIST.SP.800-218
82. NIST (2023) Artificial Intelligence Risk Management Framework (AI RMF 1.0). NIST AI 100-1. Gaithersburg, MD: National Institute of Standards and Technology. https://doi.org/10.6028/NIST.AI.100-1
83. Notaro, P., Cardoso, J. and Gerndt, M. (2021) ‘A survey of AIOps methods for failure management’, ACM Transactions on Intelligent Systems and Technology, 12(6), 81. https://doi.org/10.1145/3483424
84. Nwakamma, S., Ojuku, J. and Oyesiji, S.O. (2024a) ‘LLM-as-a-judge for automated model governance: a review of methods, biases, and release-gating practices’, World Journal of Innovation and Modern Technology, 8(6), pp. 185-216. https://doi.org/10.56201/wjimt.v8.no6.2024.pg185.216
85. Nwakamma, S., Ojuku, J. and Oyesiji, S.O. (2024b) ‘Securing agentic AI enterprise workflows against prompt injection, tool poisoning, memory manipulation, and excessive agency threats’, World Journal of Innovation and Modern Technology, 8(6), pp. 217-248. https://doi.org/10.56201/wjimt.v8.no6.2024.pg217.248
86. Odejobi, O.D., Okonkwo, C.S., Ahiaeke Patrick, M.C., et al. (2025) ‘AI-augmented secure software engineering: leveraging deep learning for autonomous threat detection and mitigation’, International Journal of Engineering and Modern Technology, 11(12), pp. 101-121. https://doi.org/10.56201/ijemt.vol.11.no12.2025.pg101.121
87. Ogbole, J.I., Okoruwa, P.O., Fadayomi, O., et al. (2021) ‘Conceptual model for identity-centric zero trust architecture in enterprise security governance’, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 7(5), pp. 393-415.
88. Ojuku, J., Oyesiji, S.O. and Nwakamma, S. (2025) ‘WebAssembly component model and WASI for portable cloud-to-edge software: language interoperability, security, performance, and container comparisons’, International Journal of Computer Science and Mathematical Theory, 11(12), pp. 232-263. https://doi.org/10.56201/ijcsmt.vol.11.no12.2025.pg232.263
89. Okoruwa, P.O., Fadayomi, O., Abolaji, T.O., et al. (2024) ‘Enterprise cybersecurity trends and threat evolution, advances and emerging research directions’, Global Multidisciplinary Perspectives Journal, 1(6), pp. 194-204. https://doi.org/10.54660/GMPJ.2024.1.6.194-204
90. Okoruwa, P.O., Fadayomi, O., Akeju, B., et al. (2020) ‘Conceptual model for privacy-centric security engineering in digital and cloud computing systems’, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 7(5), pp. 371-389.
91. Olagunju, E.O., Edivri, J. and Oteri, O. (2022) ‘Predictive capacity planning and resource utilization forecasting models for multi program and multi stakeholder IT portfolios’, Shodhshauryam, International Scientific Refereed Research Journal, 5(3), pp. 336-356.
92. Olagunju, E.O., Edivri, J. and Oteri, O. (2023) ‘A conceptual governance model for change, incident, and problem management in mission-critical enterprise IT environments’, Gyanshauryam, International Scientific Refereed Research Journal, 6(6).
93. Olagunju, E.O., Edivri, J. and Oteri, O. (2024) ‘Applied performance optimization frameworks for managing high-demand enterprise technology programs and system rollouts’, International Journal of Advanced Multidisciplinary Research and Studies, 4(6), pp. 3059-3070. https://doi.org/10.62225/2583049X.2024.4.6.5915
94. Olagunju, E.O., Oteri, O. and Edivri, J. (2025) ‘An operational reliability and service assurance framework for enterprise IT systems supporting large user populations’, International Journal of Advanced Multidisciplinary Research and Studies, 5(6), pp. 2117-2128. https://doi.org/10.62225/2583049X.2025.5.6.5916
95. Ominyi, M. and Anichukwueze, C.C. (2024) ‘Conceptualizing responsible AI governance structures for automated decision systems in regulated industries’, International Journal of Social Sciences and Management Research, 10(11), pp. 403-430. https://doi.org/10.56201/ijssmr.v10.no11.2024.pg403.430
96. Ominyi, M., Anichukwueze, C.C. and Uzougbo, N.S. (2024) ‘A systematic review of organizational readiness for the EU Artificial Intelligence Act in multinational enterprises’, World Journal of Innovation and Modern Technology, 8(6), pp. 185-203. https://doi.org/10.56201/wjimt.v8.no6.2024.pg185.203
97. Ominyi, M., Anichukwueze, C.C. and Uzougbo, N.S. (2025) ‘Reviewing legal privilege and confidentiality challenges in enterprise deployment of large language models’, Journal of Law and Global Policy, 10(3), pp. 150-176. https://doi.org/10.56201/jlgp.vol.10.no3.2025.pg150.176
98. Oshoba, T.O., Hammed, N.I. and Odejobi, O.D. (2020) ‘Blockchain-enabled compliance and audit trail model for cloud configuration management’, Journal of Frontiers in Multidisciplinary Research, 1(1), pp. 193-201.
99. Oteri, O. and Chibwaye, I.P. (2022) ‘Econometric and conceptual models for predicting early technology adoption in emerging and transitional economies’, Shodhshauryam, International Scientific Refereed Research Journal, 5(1), pp. 451-483.
100. Oteri, O. and Chibwaye, I.P. (2025) ‘Advances and conceptual models in enterprise level change management analytics for large scale organizational system transitions’, Gyanshauryam, International Scientific Refereed Research Journal, 8(5), pp. 177-211.
101. Oteri, O., Olagunju, E.O. and Edivri, J. (2025a) ‘An operational reliability engineering framework for sustained high availability in 5G core networks serving millions’, International Journal of Advanced Multidisciplinary Research and Studies, 5(6), pp. 2129-2147.
102. Oteri, O., Olagunju, E.O. and Edivri, J. (2025b) ‘Conceptual and architectural models for cloud native 5G packet core orchestration using Kubernetes and OpenStack’, Gyanshauryam, International Scientific Refereed Research Journal, 8(5), pp. 52-92.
103. Oteri, O., Olagunju, E.O. and Edivri, J. (2025c) ‘Conceptual models for cloud-based IT service management architecture and orchestration using ServiceNow platforms’, Shodhshauryam, International Scientific Refereed Research Journal, 8(6), pp. 185-203.
104. Pearce, H., Ahmad, B., Tan, B., et al. (2022) ‘Asleep at the keyboard? Assessing the security of GitHub Copilot’s code contributions’. Proceedings of the 2022 IEEE Symposium on Security and Privacy (SP 2022). San Francisco, USA, pp. 754-768. https://doi.org/10.1109/SP46214.2022.9833571
105. Perry, N., Srivastava, M., Kumar, D., et al. (2023) ‘Do users write more insecure code with AI assistants?’. Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security (CCS 2023). Copenhagen, Denmark, pp. 2785-2799. https://doi.org/10.1145/3576915.3623157
106. Ray, B., Hellendoorn, V., Godhane, S., et al. (2016) ‘On the “naturalness” of buggy code’. Proceedings of the 38^th International Conference on Software Engineering (ICSE 2016). Austin, USA, pp. 428-439. https://doi.org/10.1145/2884781.2884848
107. Ronanki, K., Berger, C. and Horkoff, J. (2023) ‘Investigating ChatGPT’s potential to assist in requirements elicitation processes’. Proceedings of the 49^th Euromicro Conference on Software Engineering and Advanced Applications (SEAA 2023). Durres, Albania, pp. 354-361. https://doi.org/10.1109/SEAA60479.2023.00061
108. Russell, R., Kim, L., Hamilton, L., et al. (2018) ‘Automated vulnerability detection in source code using deep representation learning’. Proceedings of the 17^th IEEE International Conference on Machine Learning and Applications (ICMLA 2018). Orlando, USA, pp. 757-762.
109. Sallou, J., Durieux, T. and Panichella, A. (2024) ‘Breaking the silence: the threats of using LLMs in software engineering’. Proceedings of the 2024 ACM/IEEE 44th International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER 2024). Lisbon, Portugal, pp. 102-106. https://doi.org/10.1145/3639476.3639764
110. Sandoval, G., Pearce, H., Nys, T., et al. (2023) ‘Lost at C: a user study on the security implications of large language model code assistants’. Proceedings of the 32nd USENIX Security Symposium. Anaheim, USA, pp. 2205-2222.
111. Schäfer, M., Nadi, S., Eghbali, A., et al. (2024) ‘An empirical evaluation of using large language models for automated unit test generation’, IEEE Transactions on Software Engineering, 50(1), pp. 85-105. https://doi.org/10.1109/TSE.2023.3334955
112. Sculley, D., Holt, G., Golovin, D., et al. (2015) ‘Hidden technical debt in machine learning systems’. Advances in Neural Information Processing Systems 28 (NIPS 2015). Montreal, Canada, pp. 2503-2511.
113. Spracklen, J., Wijewickrama, R., Sakib, A.H.M.N., et al. (2025) ‘We have a package for you! A comprehensive analysis of package hallucinations by code generating LLMs’. Proceedings of the 34^th USENIX Security Symposium. Seattle, USA.
114. Steenhoek, B., Rahman, M.M., Jiles, R., et al. (2023) ‘An empirical study of deep learning models for vulnerability detection’. Proceedings of the 45^th IEEE/ACM International Conference on Software Engineering (ICSE 2023). Melbourne, Australia, pp. 2237-2248. https://doi.org/10.1109/ICSE48619.2023.00188
115. Tufano, R., Masiero, S., Mastropaolo, A., et al. (2022) ‘Using pre-trained models to boost code review automation’. Proceedings of the 44^th International Conference on Software Engineering (ICSE 2022). Pittsburgh, USA, pp. 2291-2302. https://doi.org/10.1145/3510003.3510621
116. Uchechi, N., Ominyi, M., Anichukwueze, C.C., et al. (2022) ‘Real-time compliance pipelines: a review of automated risk monitoring and enterprise data governance’, International Journal of Multidisciplinary Futuristic Development, 3(2), pp. 66-94. https://doi.org/10.54660/IJMFD.2022.3.2.66-94
117. Upreti, M. and Oteri, O. (2023) ‘Analytical model for optimizing cloud resource allocation through rightsizing and long-term usage forecasting’, Gyansharyam, International Scientific Refereed Research Journal, 6(1), pp. 400-424.
118. Upreti, M. and Oteri, O. (2025) ‘Security design model for resilient network architectures in large scale cloud computing platforms’, Shodhsharyam, International Scientific Refereed Research Journal, 8(5), pp. 84-104.
119. Vaithilingam, P., Zhang, T. and Glassman, E.L. (2022) ‘Expectation vs. experience: evaluating the usability of code generation tools powered by large language models’. Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA 2022). New Orleans, USA, Article 332. https://doi.org/10.1145/3491101.3519665
120. Vaswani, A., Shazeer, N., Parmar, N., et al. (2017) ‘Attention is all you need’. Advances in Neural Information Processing Systems 30 (NIPS 2017). Long Beach, USA, pp. 5998-6008.
121. Wang, J., Huang, Y., Chen, C., et al. (2024) ‘Software testing with large language models: survey, landscape, and vision’, IEEE Transactions on Software Engineering, 50(4), pp. 911-936. https://doi.org/10.1109/TSE.2024.3368208
122. Xia, C.S., Paltenghi, M., Tian, J.L., et al. (2024) ‘Fuzz4All: universal fuzzing with large language models’. Proceedings of the 46^th IEEE/ACM International Conference on Software Engineering (ICSE 2024). Lisbon, Portugal, Article 126. https://doi.org/10.1145/3597503.3639121
123. Xia, C.S., Wei, Y. and Zhang, L. (2023) ‘Automated program repair in the era of large pre-trained language models’. Proceedings of the 45^th IEEE/ACM International Conference on Software Engineering (ICSE 2023). Melbourne, Australia, pp. 1482-1494. https://doi.org/10.1109/ICSE48619.2023.00129
124. Yu, Z., Wu, Y., Zhang, N., et al. (2023) ‘CodeIPPrompt: intellectual property infringement assessment of code language models’. Proceedings of the 40^th International Conference on Machine Learning (ICML 2023). Honolulu, USA, PMLR 202, pp. 40373-40389.
125. Zhou, Y., Liu, S., Siow, J., et al. (2019) ‘Devign: effective vulnerability identification by learning comprehensive program semantics via graph neural networks’. Advances in Neural Information Processing Systems 32 (NeurIPS 2019). Vancouver, Canada, pp. 10197-10207.
126. Ziegler, A., Kalliamvakou, E., Li, X.A., et al. (2024) ‘Measuring GitHub Copilot’s impact on productivity’, Communications of the ACM, 67(3), pp. 54-63. https://doi.org/10.1145/3633453