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Paper ID:IJSREM67868

QUALTAN: A Governed Agentic Architecture for Specification-Driven and Execution-Grounded Autonomous Software Testing

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM67868

Volume:

10 |

10 |

Oct 2026

Pages:

1-11

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM67868

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

Mohan Gundala

Mohan Gundala

Computer Science

The Robert Gordon University

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Abstract

Large Language Models (LLMs) are increasingly used to generate software tests, yet generated tests are probabilistic artifacts whose validity, relevance, and execution behavior must be verified independently. This paper presents the protocol for QUALTAN, a governed agentic architecture that separates LLM-based reasoning from deterministic test execution. QUALTAN combines specification-driven test intent, canonical test artifacts, policy checks before execution, execution-grounded validation, risk-aware test selection, and intent-preserving autonomous repair. We pre-specify six research questions, six hypotheses with falsification criteria, a four-condition controlled experiment (traditional automation, LLM-only generation, LLM generation with execution feedback, and the full architecture), a six-component ablation study, and a statistical analysis plan with predefined primary outcomes. The study uses open-source and synthetic applications with seeded defects and a public benchmark, and reports paired comparisons with corrected significance tests and effect sizes. Because the protocol is fixed before data collection, the claims it tests can be supported, refined, or rejected by the results. The paper therefore contributes a falsifiable experimental design for studying how governance and execution feedback affect the quality of LLM-generated test evidence.

KEYWORDS

software testing large language models agentic systems test generation mutation testing test oracles reproducibility empirical software engineering

Cite This Article

Mohan Gundala, "QUALTAN: A Governed Agentic Architecture for Specification-Driven and Execution-Grounded Autonomous Software Testing," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 10, pp. 1-11, Oct 2026. https://doi.org/10.55041/IJSREM67868

Full Article/Conclusion

This paper presents a protocol for a controlled study of governed, execution-grounded agentic testing. Its central proposition is that LLM reasoning becomes more useful for software quality when it is constrained by specifications, canonical artifacts, deterministic execution, evidence collection, and intent-preserving repair. The protocol pre-specifies the research questions, hypotheses, conditions, metrics, statistical tests, and falsification criteria under which that proposition will be judged. The results of the study will determine whether the proposition holds.

References

[1] C. Augusto, A. Bertolino, G. De Angelis, F. Lonetti, and J. Morán, “Large Language Models for Software Testing: A Research Roadmap,” arXiv:2509.25043, 2025. [2] D. Huang, J. M. Zhang, M. Harman, Q. Zhang, M. Du, and S.-K. Ng, “Benchmarking LLMs for Unit Test Generation from Real-World Functions,” arXiv:2508.00408, 2025. [3] M. Schäfer, S. Nadi, A. Eghbali, and F. Tip, “An Empirical Evaluation of Using Large Language Models for Automated Unit Test Generation,” IEEE Transactions on Software Engineering, 2024. [4] C. Lemieux, J. P. Inala, S. K. Lahiri, and S. Sen, “CodaMosa: Escaping Coverage Plateaus in Test Generation with Pre-trained Large Language Models,” in Proc. 45th IEEE/ACM International Conference on Software Engineering (ICSE), 2023. [5] N. Alshahwan, J. Chheda, A. Finegenova, B. Gokkaya, M. Harman, I. Harper, A. Marginean, S. Sengupta, and E. Wang, “Automated Unit Test Improvement using Large Language Models at Meta,” in Proc. 32nd ACM Symposium on the Foundations of Software Engineering (FSE) Industry Track, 2024. [6] J. Wang, Y. Huang, C. Chen, Z. Liu, S. Wang, and Q. Wang, “Software Testing with Large Language Models: Survey, Landscape, and Vision,” IEEE Transactions on Software Engineering, 2024. [7] Y. Jia and M. Harman, “An Analysis and Survey of the Development of Mutation Testing,” IEEE Transactions on Software Engineering, vol. 37, no. 5, 2011. [8] A. Arcuri and L. Briand, “A Practical Guide for Using Statistical Tests to Assess Randomized Algorithms in Software Engineering,” in Proc. 33rd International Conference on Software Engineering (ICSE), 2011. [9] A. Vargha and H. D. Delaney, “A Critique and Improvement of the CL Common Language Effect Size Statistics of McGraw and Wong,” Journal of Educational and Behavioral Statistics, vol. 25, no. 2, 2000. [10] S. Holm, “A Simple Sequentially Rejective Multiple Test Procedure,” Scandinavian Journal of Statistics, vol. 6, no. 2, 1979.

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Publication Date

10/12/2026

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Double-Blind Peer Review

Review Rounds: 2

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