A Review of Large Language Models for Automated Test Case Generation
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
Automated test case generation aims to improve software testing by reducing the manual effort required to create test cases. Recent advancements in large language models (LLMs), with their ability to understand natural language and generate code, have identified new opportunities to enhance this process. In this review, the focus is on the use of LLMs in test case generation to identify the effectiveness of the proposed methods compared with existing tools and potential directions for future research. A literature search was conducted using online resources, filtering the studies based on the defined inclusion and exclusion criteria. This paper presents the findings from the selected studies according to the three research questions and further categorizes the findings based on the common themes. These findings highlight the opportunities and challenges associated with the use of LLMs in this domain. Although improvements were observed in metrics such as test coverage, usability, and correctness, limitations such as inconsistent performance and compilation errors were highlighted. This provides a state-of-the-art review of LLM-based test case generation, emphasizing the potential of LLMs to improve automated testing while identifying areas for further advancements.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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