Automatic Unit Test Generation for Machine Learning Libraries: How Far Are We?
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
Automatic unit test generation that explores the input space and produces effective test cases for given programs have been studied for decades. Many unit test generation tools that can help generate unit test cases with high structural coverage over a program have been examined. However, the fact that existing test generation tools are mainly evaluated on general software programs calls into question about its practical effectiveness and usefulness for machine learning libraries, which are statistically orientated and have fundamentally different nature and construction from general software projects. In this paper, we set out to investigate the effectiveness of existing unit test generation techniques on machine learning libraries. To investigate this issue, we conducted an empirical study on five widely used machine learning libraries with two popular unit testcase generation tools, i.e., EVOSUITE and Randoop. We find that (1) most of the machine learning libraries do not maintain a high-quality unit test suite regarding commonly applied quality metrics such as code coverage (on average is 34.1%) and mutation score (on average is 21.3%), (2) unit test case generation tools, i.e., EVOSUITE and Randoop, lead to clear improvements in code coverage and mutation score, however, the improvement is limited, and (3) there exist common patterns in the uncovered code across the five machine learning libraries that can be used to improve unit test case generation tasks.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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