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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM67679
Volume:
10 |
10 |
Oct 2026
Pages:
1-5
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

Department of Computer Science & Engineering
Chaudhary Charan Singh University, Meerut, Uttar Pradesh, India

Department of Computer Science & Engineering
Chaudhary Charan Singh University, Meerut, Uttar Pradesh, India
Autonomous test oracles are emerging as a practical solution to the long-standing oracle problem in software testing. This condensed paper presents a short, implementation-oriented framework that integrates machine learning, large language models (LLMs), vision-language models (VLMs), self-healing automation, and explainable AI (XAI) into a unified oracle pipeline. The proposed framework organizes oracle intelligence into five layers: observation, representation, inference, assertion, and feedback. It further adopts an OODA-style operational loop (Observe–Orient–Decide–Act) for continuous oracle adaptation. Evaluation evidence from industrial and academic studies indicates approximately 30% improvement in defect detection, 25% increase in test coverage, 40% reduction in execution time, 20% higher user satisfaction, and 35% reduction in post-release defect costs [39], [42], [50]. The paper also discusses transparency, accountability, bias, scalability and explainability challenges [46]–[49]. The short format is intended for rapid dissemination and practitioner adoption while preserving full citation traceability.
KEYWORDS
This condensed paper presented a five-layer AI oracle pipeline and an OODA-style adaptation loop. It preserved full citation traceability to 50 sources. Future work should focus on long-term impact studies, ethical frameworks, XAI metrics, quantum machine learning, continuous learning systems, and human-AI collaboration [39], [42], [46], [49].
[1] Aleem, L. F. Capretz, F. Ahmed, “Benchmarking Machine Learning Techniques for Software Defect Detection,” Int. J. Softw. Eng. Appl., vol. 6, no. 3, pp. 11–23, 2015. [2] C. Jordan, F. Maurer, S. Lowenberg and J. Provost, “Framework for Flexible, Adaptive Support of Test Management by Means of Software Agents,” IEEE Robotics and Automation Letters, vol. 4, no. 3, pp. 2754–2761, 2019. [3] Dai, Wenbin, et al. “A Cloud-Based Decision Support System for Self-Healing in Distributed Automation Systems Using Fault Tree Analysis.” IEEE Transactions on Industrial Informatics, vol. 14, no. 3, pp. 989–1000, 2018. [4] Glenford J. Myers, Corey Sandler, Tom Badgett, The Art of Software Testing, 3rd Edition, 2015. [5] Huang, Rubing, et al. “Poster: An Experimental Analysis of Fault Detection Capabilities of Covering Array Constructors.” 2018 IEEE/ACM 40th International Conference on Software Engineering: Companion (ICSE-Companion), IEEE, 2018. [6] Jagdish Singh, Monika Sharma, “A Comprehensive Review of Web-based Automation Testing Tools,” International Journal of Innovative Research in Computer and Communication Engineering, vol. 3, no. 10, October 2015. [7] Mariani, L., Hao, D., Subramanyam, R., Zhu, H.: “The central role of test automation in software quality assurance,” Software Quality Journal, vol. 25, no. 3, pp. 797–802, 2017. [8] Narayan, Vaibhav, “The Role of AI in Software Engineering and Testing,” International Journal of Technical Research and Applications, 2018. [9] H. Li, Y. Wang, Y. Sun, “Fault Detection and Recovery in Self-Healing Networks Based on Artificial Neural Network,” 2021 5th International Conference on Electronic Information Technology and Computer Engineering (EITCE), Chengdu, China, 2021, pp. 96–100. [10] M. Liu, C. Hu, L. Zhai, “A Novel Fault Detection and Recovery Method for Self-healing Network Based on Artificial Intelligence,” 2020 IEEE International Conference on Intelligence and Security Informatics (ISI), Chengdu, China, 2020, pp. 1–5.
Publication Date
10/5/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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