On Adequacy of Assertions in Automated Test Suites: An Empirical Investigation
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
An integral part of test case is the verification phase (also called `test oracle'), which verifies program's state, output or behavior. In automated testing, the verification phase is often implemented using test assertions which are usually developed manually by testers. More precisely, assertions are used for checking the unit or the system's behavior (or output) which is reflected by the changes in the data fields of the class under test, or the output of the function under test. Originated from human (testers') error, test suites are prone to having inadequate assertions. The paper reports an empirical study on the Inadequate-Assertion (IA) problem in the context of automated test suites developed for open-source projects. In this study, test suites of three active open-source projects have been chosen. To investigate IA problem occurrence among the sampled test suites, we performed mutation analysis and coverage analysis. The results indicate that: (1) the IA problem is common among the sampled open-source projects, and the occurrence varies from project to project and from package to package, and (2) the occurrence rate of the IA problem is positively co-related with the complexity of test code.
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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 | 0.001 |
| 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