Automated Test Case Generation in a Real-World System Using a Customized AI Agent: An Experience Report
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Test case design is an essential activity in software quality assurance. However, when performed manually, it can be time-consuming, error-prone, and require substantial effort, particularly in complex applications. This experience report describes the development and application of an artificial intelligence agent, built using the ChatGPT platform, designed to automate the process of generating test cases for a real-world system and reduce the time required. The agent was configured to simulate the role of a QA analyst, using functional requirements, interface prototypes, and prompt engineering strategies to produce test scenarios with high coverage and accuracy. Experimentswere conducted on one of the modules of a component assembly control system, comparing manually created test cases with those generated by the agent. The results showed a reduction of over 50% in specification time while maintaining the quality and coverage of the scenarios. This paper details the agent’s configuration, the results achieved, the challenges encountered, and the lessons learned, contributing evidence for the practical use of generative AI in the context of software quality assurance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it