Can Generative AI Produce Test Cases? An Experience from the Automotive Domain
Bibliographic record
Abstract
Engineers need automated support for software testing. Generative AI is a novel technology for generating new content; however, its applicability for test case generation is still unclear. This work considers the following question: Can generative AI produce test cases in industrial software applications? We framed our question in the automotive domain. We performed our evaluation in collaboration with a large automotive manufacturer to assess to what extent generative AI can produce test cases (a.k.a. test scripts) from informal test case specifications. We considered 1) informal test case specifications defined in Rational Quality Manager, an industrial test management tool from IBM, and 2) executable test scripts specified as ecu.test packages supported by the ecu.test tool from Tracetronic. We used generative AI to produce the test scripts from the informal test case descriptions. Our results show that generative AI can produce correct or near-correct test scripts in a reasonable number of cases. We also analyzed the effects of prompt design, choice of generative AI model, and context accuracy on the effectiveness of our solution and reflected on our results.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".