What Do Client Developers Concern When Using Web APIs? An Empirical Study on Developer Forums and Stack Overflow
Bibliographic record
Abstract
Popularity of service-oriented computing makes more and more companies and organizations provide their services through Web Application Program Interfaces (Web APIs). The Web APIs are considered to offer a convenient way to integrate web services to client applications. However, the integration process is often challenging. For example, updated Web APIs may be no longer compatible with the current version of client applications, thus break the client applications. To help the integration process, it is of significant interest to understand the challenges that are encountered by client developers. Developer forums and Stack Overflow are commonly used by client developers to seek help from fellow peers. In this paper, we mine both developer forums and Stack Overflow to find the common challenges encountered by client developers. We perform an empirical study on 32 Web APIs with a total of 92,471 discussions. To extract topics from all discussions, we apply a topic modeling technique called Latent Dirichlet Allocation (LDA). The results show that on average five dominant topics can cover at least 50% of questions regarding each Web API. We further investigate how topics evolve across Web APIs, and find five patterns. As a summary, our findings highlight a list of dominant concerns and persistent concerns for each Web API that Web API providers should pay more attention to.
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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 | 0.017 | 0.111 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".