Reproducibility Challenges and Their Impacts on Technical Q&A Websites: The Practitioners’ Perspectives
Bibliographic record
Abstract
Software developers often submit questions to technical Q&A sites like Stack Overflow (SO) to resolve their code-level problems. Usually, they include example code segments with their questions to explain the programming issues. When users of SO attempt to answer the questions, they prefer to reproduce the issues reported in questions using the given code segments. However, such code segments could not always reproduce the issues due to several unmet challenges (e.g., too short code segment) that might prevent questions from receiving appropriate and prompt solutions. A previous study produced a catalog of potential challenges that hinder the reproducibility of issues reported at SO questions. However, it is unknown how the practitioners (i.e., developers) perceive the challenge catalog. Understanding the developers’ perspective is inevitable to introduce interactive tool support that promotes reproducibility. We thus attempt to understand developers’ perspectives by surveying 53 users of SO. In particular, we attempt to – (1) see developers’ viewpoints on the agreement to those challenges, (2) find the potential impact of those challenges, (3) see how developers address them, and (4) determine and prioritize tool support needs. Survey results show that about 90% of participants agree to the already exposed challenges. However, they report some additional challenges (e.g., error log missing) that might prevent reproducibility. According to the participants, too short code segment and absence of required Class/Interface/Method from code segments severely prevent reproducibility, followed by missing important part of code. To promote reproducibility, participants strongly recommend introducing tool support that interacts with question submitters with suggestions for improving the code segments if the given code segments fail to reproduce the issues.
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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 | 0.154 | 0.287 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.022 | 0.016 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".