Validating pragmatic reuse tasks by leveraging existing test suites
Bibliographic record
Abstract
SUMMARY Traditional industrial practice often involves the ad hoc reuse of source code that was not designed for that reuse. Suchpragmatic reusetasks play an important role indisciplinedsoftware development. Pragmatic reuse has been seen as problematic due to a lack of systematic support, and an inability to validate that the reused code continues to operate correctly within the target system. Although recent work has successfully systematized support for pragmatic reuse tasks, the issue of validation remains unaddressed. In this paper, we present a novel approach and tool to semi‐automatically reuse and transform relevant portions of the test suite associated with pragmatically reused code, as a means to validate that the relevant constraints from the originating system continue to hold, while minimizing the burden on the developer. We conduct a formal experiment with experienced developers, to compare the application of our approach versus the use of a standard IDE (the ‘manual approach’). We find that, relative to the manual approach, our approach: reduces task completion time; improves instruction coverage by the reused test cases; and improves the correctness of the reused test cases. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.024 | 0.146 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".