Prioritizing the creation of unit tests in legacy software systems
Bibliographic record
Abstract
SUMMARY Test‐driven development (TDD) is a software development practice that prescribes writing unit tests before writing implementation code. Recent studies have shown that TDD practices can significantly reduce the number of pre‐release defects. However, most TDD research thus far has focused on new development. We investigate the adaptation of TDD‐like practices for already‐implemented code, in particular legacy systems. We call such an adaptation ‘Test‐driven maintenance’ (TDM). In this paper, we present a TDM approach that assists software development and testing managers to use the limited resources they have for testing legacy systems efficiently. The approach leverages the development history of a project to generate a prioritized list of functions that managers should focus their unit test writing resources on. The list is updated dynamically as the development of the legacy system progresses. We evaluate our approach on two large software systems: a large commercial system and the Eclipse Open Source Software system. For both systems, our findings suggest that heuristics based on the function size, modification frequency and bug fixing frequency should be used to prioritize the unit test writing efforts for legacy systems. Copyright © 2011 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 | 0.008 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".