Analyzing test driven development based on GitHub evidence
Bibliographic record
Abstract
Testing is an integral part of the software development lifecycle, approached with varying degrees of rigor by different process models. Agile process models advocate Test Driven Development (TDD) as one among their key practices for reducing costs and improving code quality. In this paper we comparatively analyze GitHub repositories that adopt TDD against repositories that do not, in order to determine how TDD affects a number of variables related to productivity and developer satisfaction, two aspects that should be considered in a cost-benefit analysis of the paradigm. In this study, we searched through GitHub and found that a relatively small subset of Java-based repositories can be seen to adopt TDD, and an even smaller subset can be confidently identified as rigorously adhering to TDD. For comparison purposes, we created two same-size control sets of repositories. We then compared the repositories in these two sets in terms of number of test files, average commit velocity, number of commits that reference bugs, number of issues recorded, whether they use continuous integration, and the sentiment of their developers’ commits. We found some interesting and significant differences between the two sets, including higher commit velocity and increased likelihood of continuous integration for TDD repositories.
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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 | 0.267 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.024 | 0.027 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".