[Journal First] Analyzing the Effects of Test Driven Development in GitHub
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 recommend Test Driven Development (TDD) as a key practice for reducing costs and improving code quality. The objective of this work is to perform a cost-benefit analysis of this practice. Previous work by Fucci et al. engaged in laboratory studies of developers actively engaged in test-driven development practices. Fucci et al. found little difference between test-first behaviour of TDD and test-later behaviour. To that end, we opted to conduct a study about TDD behaviours in the wild rather than in the laboratory. Thus we have conducted a comparative analysis of GitHub repositories that adopts TDD to a lesser or greater extent, in order to determine how TDD affects software development productivity and software quality. We classified GitHub repositories archived in 2015 in terms of how rigorously they practiced TDD, thus creating a TDD spectrum. We then matched and compared various subsets of these repositories on this TDD spectrum with control sets of equal size. The control sets were samples from all GitHub repositories that matched certain characteristics, and that contained at least one test file. We compared how the TDD sets differed from the control sets on the following characteristics: number of test files, average commit velocity, number of bug-referencing commits, number of issues recorded, usage of continuous integration, number of pull requests, and distribution of commits per author. We found that Java TDD projects were relatively rare. In addition, there were very few significant differences in any of the metrics we used to compare TDD-like and non-TDD projects; therefore, our results do not reveal any observable benefits from using TDD.
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 | 0.006 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".