Citation :

Ijeoma Obi, Esther Umeh, 2026. "Self-Adaptive AI Frameworks for Predictive Test Generation and Software Defect Prevention" ESP International Journal of Science, Humanities & Management Studies(ESP-IJSHMS)  Volume 4, Issue 2: 1-12.

Abstract :

Software systems evolve in a more complex, distributed and injected manner at an ever-increasing rate which poses significant challenges to traditional software testing & defect prevention practices. As traditional testing techniques rely on manually created test cases, static rules and inflexible testing approach cannot keep up with dynamic nature of software environment. An emerging solution to your concern is self-adaptive Artificial Intelligence (AI) frameworks that learn from software artifacts, execution histories of systems at runtime, defect patterns in the production data produced previously by a system through instances code changes or testing outcomes and training. This research explores the use of self-adaptive AI frameworks for predictive generation of test cases and prevention software defects in advance. Specifically, our proposed testing lifecycle that combines best-of-breed industry and academic research: machine learning deep learning reinforcement learning software analytics automated test generation continuous feedback loops from production systems provides an integrated adaptive approach to testing.
This framework provides a defect-prone component prediction, testing activity prioritization and dynamic generation of prominent test cases based on newly available evidence for modifying the existing strategies. Notably, adaptive learning, defect prediction accuracy; test prioritization and code coverage; fault detection capability and computational efficiency have gotten the focus. The research also investigates how AI-powered testing mechanisms can be advantageous for continuous integration and delivery environments. The proposed framework incorporates predictive analytics with autonomous adaptation to efficiently reduce testing effort as well as improve defect detection, increase reliability of the software under test and enable faster delivery of software. This study shows how self-adaptive artificial intelligence transforms testing from a reactive verification activity to an ongoing, proactive defect-prevention process.

References :

[1] Arcuri, A. and Fraser, G. (2011) ‘On parameter tuning in search based software engineering’, Lecture Notes in Computer Science, 6957, pp. 33–47.

[2] Cadar, C., Dunbar, D. and Engler, D.R. (2008) ‘KLEE: Unassisted and automatic generation of high-coverage tests for complex systems programs’, Proceedings of the 8th USENIX Symposium on Operating Systems Design and Implementation, pp. 209–224.

[3] Catal, C. (2011) ‘Software fault prediction: A literature review’, Expert Systems with Applications, 38(4), pp. 4626–4636.

[4] Chen, J., Hu, K., Yu, Y., Xuan, Q., Filkov, V. and Liu, Y. (2020) ‘Software visualization and deep transfer learning for effective software defect prediction’, Proceedings of the 42nd International Conference on Software Engineering, pp. 578–589.

[5] Dam, H.K., Tran, T., Pham, T.M., Ghose, A., Grundy, J. and Kim, A. (2018) ‘Automatic feature learning for predicting vulnerable software components’, IEEE/ACM 40th International Conference on Software Engineering, pp. 247–258.

[6] Elish, M.O. and Elish, K.O. (2008) ‘Predicting defect-prone software modules using support vector machines’, Journal of Systems and Software, 81(5), pp. 649–660.

[7] Fontes, A. and Gay, G. (2023) ‘The integration of machine learning into automated test generation: A systematic mapping study’, Software Testing, Verification and Reliability, 33(4), e1845.

[8] Fraser, G. and Arcuri, A. (2011) ‘EvoSuite: Automatic test suite generation for object-oriented software’, Proceedings of the 19th ACM SIGSOFT Symposium on the Foundations of Software Engineering, pp. 416–419.

[9] Godefroid, P., Klarlund, N. and Sen, K. (2005) ‘DART: Directed automated random testing’, Proceedings of the ACM SIGPLAN Conference on Programming Language Design and Implementation, pp. 213–223.

[10] Hall, T., Beecham, S., Bowes, D., Gray, D. and Counsell, S. (2012) ‘A systematic literature review on fault prediction performance in software engineering’, IEEE Transactions on Software Engineering, 38(6), pp. 1276–1304.

[11] Harman, M., Jia, Y. and Zhang, Y. (2015) ‘Achievements, open problems and challenges for search based software testing’, IEEE Software, 32(4), pp. 58–66.

[12] Kamei, Y., Shihab, E., Adams, B., Hassan, A.E., Mockus, A., Sinha, A. and Ubayashi, N. (2013) ‘A large-scale empirical study of just-in-time quality assurance’, IEEE Transactions on Software Engineering, 39(6), pp. 757–773.

[13] Kephart, J.O. and Chess, D.M. (2003) ‘The vision of autonomic computing’, Computer, 36(1), pp. 41–50.

[14] Lessmann, S., Baesens, B., Mues, C. and Pietsch, S. (2008) ‘Benchmarking classification models for software defect prediction: A proposed framework and novel findings’, IEEE Transactions on Software Engineering, 34(4), pp. 485–496.

[15] McMinn, P. (2004) ‘Search-based software test data generation: A survey’, Software Testing, Verification and Reliability, 14(2), pp. 105-156.

[16] 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.

[17] Pacheco, C., Lahiri, S.K., Ernst, M.D. and Ball, T. (2007) ‘Feedback-directed random test generation’, Proceedings of the 29th International Conference on Software Engineering, pp. 75–84.

[18] Riccio, V., Jahangirova, G., Stocco, A., Humbatova, N., Weiss, M. and Tonella, P. (2020) ‘Testing machine learning based systems: A systematic mapping’, Empirical Software Engineering, 25, pp. 5193–5254.

[19] Salehie, M. and Tahvildari, L. (2009) ‘Self-adaptive software: Landscape and research challenges’, ACM Transactions on Autonomous and Adaptive Systems, 4(2), Article 14.

[20] Saputri, T.R.D. and Lee, S.-W. (2020) ‘The application of machine learning in self-adaptive systems: A systematic literature review’, IEEE Access, 8, pp. 205948–205967.

[21] Sen, K., Marinov, D. and Agha, G. (2005) ‘CUTE: A concolic unit testing engine for C’, Proceedings of the 10th European Software Engineering Conference, pp. 263–272.

[22] Shepperd, M., Bowes, D. and Hall, T. (2014) ‘Researcher bias: The use of machine learning in software defect prediction’, IEEE Transactions on Software Engineering, 40(6), pp. 603–616.

[23] Tantithamthavorn, C., McIntosh, S., Hassan, A.E. and Matsumoto, K. (2016) ‘Automated parameter optimization of classification techniques for defect prediction models’, Proceedings of the 38th International Conference on Software Engineering, pp. 321–332.

[24] Tonella, P. (2004) ‘Evolutionary testing of classes’, Proceedings of the ACM SIGSOFT International Symposium on Software Testing and Analysis, pp. 119–128.

[25] Weyns, D., Malek, S. and de Lemos, R. (2012) ‘Self-adaptive software systems’, IEEE Software, 29(6), pp. 2–5.

[26] Wang, S., Liu, T. and Tan, L. (2016) ‘Automatically learning semantic features for defect prediction’, Proceedings of the 38th International Conference on Software Engineering, pp. 297–308.

[27] Yoo, S. and Harman, M. (2012) ‘Regression testing minimization, selection and prioritization: A survey’, Software Testing, Verification and Reliability, 22(2), pp. 67–120.

[28] Zhang, J., Hu, X., Xia, X., Cheung, S.-C. and Li, S. (2026) ‘Automated unit test generation via chain-of-thought prompt and reinforcement learning from coverage feedback’, ACM Transactions on Software Engineering and Methodology, 35(4), Article 92.

[29] Gheibi, O., Weyns, D. and Quin, F. (2021) ‘Applying machine learning in self-adaptive systems: A systematic literature review’, ACM Transactions on Autonomous and Adaptive Systems, 16(3–4), pp. 1–37.

[30] Omri, S. and Sinz, C. (2021) ‘Machine learning techniques for software quality assurance: A survey’, arXiv preprint, arXiv:2104.14056.

Keywords :

Predictive Testing for Self-Adaptive AI: A Review of Machine Learning-Based Test Automation, Software Quality, and Defect Prediction Techniques.