How Do Software Developers Use ChatGPT? An Exploratory Study on GitHub Pull Requests
Bibliographic record
Abstract
Nowadays, Large Language Models (LLMs) play a pivotal role in software engineering. Developers can use LLMs to address software development-related tasks such as documentation, code refactoring, debugging, and testing. ChatGPT, released by OpenAI, has become the most prominent LLM. In particular, ChatGPT is a cutting-edge tool for providing recommendations and solutions for developers in their pull requests (PRs). However, little is known about the characteristics of PRs that incorporate ChatGPT compared to those without it and what developers usually use it for. To this end, we quantitatively analyzed 243 PRs that listed at least one ChatGPT prompt against a representative sample of 384 PRs without any ChatGPT prompts. Our findings show that developers use ChatGPT in larger, time-consuming pull requests that are five times slower to be closed than PRs that do not use ChatGPT. Furthermore, we perform a qualitative analysis to build a taxonomy of the topics developers primarily address in their prompts. Our analysis results in a taxonomy comprising 8 topics and 32 sub-topics. Our findings highlight that ChatGPT is often used in review-intensive pull requests. Moreover, our taxonomy enriches our understanding of the developer's current applications of ChatGPT.
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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 | 0.018 | 0.144 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".