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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM67679

Toward Autonomous Test Oracles: A Condensed AI-Driven Framework for Intelligent Software Testing

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM67679

Volume:

10 |

10 |

Oct 2026

Pages:

1-5

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM67679

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.

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Authors

Monu Kumar

Monu Kumar

Department of Computer Science & Engineering

Chaudhary Charan Singh University, Meerut, Uttar Pradesh, India

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Dr. Amit Sharma

Dr. Amit Sharma

Department of Computer Science & Engineering

Chaudhary Charan Singh University, Meerut, Uttar Pradesh, India

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Abstract

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

Autonomous Test Oracles AI-Driven Testing Large Language Models Vision-Language Models Self-Healing Tests Explainable AI Software Quality

Cite This Article

Monu Kumar, Dr. Amit Sharma, "Toward Autonomous Test Oracles: A Condensed AI-Driven Framework for Intelligent Software Testing," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 10, pp. 1-5, Oct 2026. https://doi.org/10.55041/IJSREM67679

Full Article/Conclusion

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].

References

[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.

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Publication Date

10/5/2026

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