Recommending Posts concerning API Issues in Developer Q&A Sites
Bibliographic record
Abstract
API design is known to be a challenging craft, as API designers must balance their elegant ideals against "real-world" concerns, such as utility, performance, backwards compatibility, and unforeseen emergent uses. However, to date, there is no principled method to collect or analyze API usability information that incorporates input from typical developers. In practice, developers often turn to Q&A websites such as stackoverflow.com (SO) when seeking expert advice on API use, the popularity of such sites has thus led to a very large volume of unstructured information that can be searched with diligence for answers to specific questions. The collected wisdom within such sites could, in principle, be of great help to API designers to better support developer needs, if only it could be collected, analyzed, and distilled for practical use. In this paper, we present a methodology that combines several techniques, including social network analysis and topic mining, to recommend SO posts that are likely to concern API design-related issues. To establish a comparison baseline, we introduce two more recommendation approaches: a reputation-based recommender and a random recommender. We have found that when applied to Q&A discussion of two popular mobile platforms, Android and iOS, our methodology achieves up to 93% accuracy and is more stable with its recommendations when compared to the two baseline techniques.
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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 | 0.006 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".