JTestMigBench and JTestMigTax: A benchmark and taxonomy for unit test migration
Bibliographic record
Abstract
Unit tests play a critical role in improving software quality. However, writing effective unit tests from scratch is difficult and tedious. One way to reduce this difficulty is to recommend existing tests of semantically similar functions. However, modifying the recommended tests manually might still be difficult and tedious. For example, developers have to understand various code elements in the recommended tests to accurately replace them with semantically similar code elements from the target application. One way to mitigate the issue is by developing a test migration or reuse technique that could automatically transform the code elements in the recommended tests and migrate them to the target application. However, to develop such a technique, we first need to identify what types of code transformations are required to successfully migrate the recommended tests. Therefore, in this paper, we first recruit two external participants to create JTestMigBench, a benchmark of 510 manually migrated JUnit tests for 186 methods from five popular libraries. We then analyze the code changes in the migrated tests to create JTestMigTax, a taxonomy of test code transformation patterns. Our contributions provide the necessary foundations to develop automated unit test migration or reuse techniques.
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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 | 0.009 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.003 |
| 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".