Recommending Posts concerning API Issues in Developer Q&A Sites
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it