Exploring the Integration of Generative AI Tools in Software Testing Education: A Case Study on ChatGPT and Copilot for Preparatory Testing Artifacts in Postgraduate Learning
Bibliographic record
Abstract
Software testing education is important for building qualified testing professionals. To ensure that software testing graduates are ready for real-world challenges, it is necessary to integrate modern tools and technologies into the curriculum. With the emergence of Large Language Models (LLMs), their potential use in software engineering has become a focus, but their application in software testing education remains largely unexplored. This study, conducted in the Capstone Project course of a postgraduate software testing program, was carried out over two semesters with two distinct groups of students. A custom-built Travel Application limited to a web platform was used in the first semester. In the second semester, a new set of students worked with an open-source application, offering a larger-scale, multi-platform experience across web, desktop, and mobile platforms. Students initially created preparatory testing artifacts manually as a group deliverable. Following this, they were assigned an individual assignment to generate the same artifacts using LLM tools such as ChatGPT 3.5 in the first semester and Microsoft Copilot in the second. This process directly compared manually created artifacts and those generated using LLMs, leveraging AI for faster outputs. After completion, they responded to a set of assigned questions. The students’ responses were assessed using an integrated methodology, including quantitative and qualitative assessments, sentiment analysis to understand emotions, and a thematic approach to extract deeper insights. The findings revealed that while LLMs can assist and augment manual testing efforts, they cannot entirely replace the need for manual testing. By incorporating innovative technology into the curriculum, this study highlights how Generative AI can support active learning, connect theoretical concepts with practical applications, and align educational practices with industry needs.
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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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".