Bibliographic record
Abstract
Test cases use assertions to check program behaviour. While these assertions may not be complex, they are themselves code and must be written correctly in order to distinguish between passing and failing test cases. I assert that test assertions are relatively repetitive and straight-forward, making their construction well suited to automation; and that tools can reduce developer effort (and simultaneously improve the quality of the assertions in their test suites) by automatically generating assertions that the tester can choose to accept, modify, delete, or augment. Such a tool can fit into a developer workflow where tests are frequently written alongside runnable source code. I examined 33,873 assertions from 105 projects and identified twelve high-level categories that account for the vast majority of developer-written test assertions, confirming that test assertions are fairly simple in practice. To assess the utility of my human-in-the-loop assertion generation thesis, I developed the AutoAssert framework, which generates typical assertions for test cases written for JavaScript code. AutoAssert uses dynamic analysis to determine both which assertions to generate and what values they should verify. The developer can choose to accept, modify, delete, or add to the set of generated assertions. I compared assertions generated by AutoAssert to those written by developers and found that it generates the same kind of assertions as written by developers 84\% of the time in a sample of over 1,000 assertions. Additionally I validated the utility of AutoAssert-generated assertions with 17 developers; these developers found that the majority of generated assertions were useful and expressed considerable interest in using such a tool/approach for their own projects.
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.
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 | 0.008 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".