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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM66297
Volume:
10 |
7 |
Jul 2026
Pages:
1-4
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.
Dept. of Artificial Intelligence and Machine Learning
Sri Krishna Institute of Technology, Bangalore.
Computer Science and Technology
RRIT (VTU Autonomous), Bangalore.
Computer science & Technology
RRIT (VTU Autonomous), Bangalore.
Agile teams are always short on time for testing, which is exactly when good test cases matter most. Traditionally, testers write these by hand using their understanding of the requirements, but AI tools that can now read a user story or a chunk of code and spit out a working test suite in seconds are changing that equation. This paper compares manual and AI-assisted test case generation in an agile setting, first through a literature-based comparison of speed, coverage, defect detection, adaptability, and reliability, and then through a small hands-on case study where both approaches were applied to the same module of a sample application. The results line up with what recent studies suggest: AI-assisted generation wins on speed and raw coverage, but manual testing still catches things AI tends to miss, particularly usability and context-dependent defects. Neither approach came out ahead on every count, which points toward a hybrid workflow rather than picking one over the other.
KEYWORDS
Manual and AI-assisted test case generation each have a clear place in agile testing, and this paper's comparison — both from the literature and from our own small case study — backs that up. AI-assisted tools are the faster option and cover more ground on paper, but they still need a human reviewer to catch the noise and the domain-specific gaps. Manual testing is slower but brings judgement that AI, at least for now, doesn't reliably replicate. For agile teams working under sprint pressure, the practical takeaway is to let AI handle the repetitive groundwork and keep testers focused on the scenarios that actually need a human eye.
[1] M. Olsthoorn, "More effective test case generation with multiple tribes of AI," in Proc. ACM/IEEE 44th Int. Conf. Software Engineering: Companion Proceedings, May 2022, pp. 286-290. [2] E. Dinella, G. Ryan, T. Mytkowicz, and S. K. Lahiri, "TOGA: A neural method for test oracle generation," in Proc. 44th Int. Conf. Software Engineering (ICSE), 2022, pp. 2130-2141. [3] V. Guilherme and A. Vincenzi, "An initial investigation of ChatGPT unit test generation capability," in Proc. 8th Brazilian Symp. Systematic and Automated Software Testing, 2023, pp. 15-24. [4] J. Shao and W. Zhang, "The role of AI in enhancing software testing efficiency and effectiveness," Software: Practice and Experience, vol. 52, no. 3, pp. 662-686, 2022. [5] S. Ghosh and A. Mukherjee, "Bias in AI algorithms: Implications for software testing and validation," ACM Trans. Softw. Eng. Methodol., vol. 31, no. 2, pp. 1-25, 2022. [6] A. Aleti, "Software testing of generative AI systems: Challenges and opportunities," in Proc. IEEE/ACM 45th Int. Conf. Software Engineering: New Ideas and Emerging Results (ICSE-NIER), May 2023. [7] S. Yarram and S. R. Bittla, "Predictive test automation: Shaping the future of quality engineering in enterprise platforms," SSRN Electronic Journal, 2023. [8] M. Gupta et al., "Comparative study of software testing technique using manually and automated way," Int. J. Scientific Research in Science and Technology, vol. 9, no. 6, pp. 384-394, Nov.-Dec. 2022. [9] K. S. Thant and H. H. K. Tin, "The impact of manual and automatic testing on software testing efficiency and effectiveness," Indian J. Science and Research, vol. 3, no. 3, pp. 88-93, 2023. [10] H. Liu, L. Liu, C. Yue, Y. Wang, and B. Deng, "AutoTestGPT: A system for the automated generation of software test cases," 2023.
Publication Date
7/22/2026
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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