Using Structure-Based Recommendations to Facilitate Discoverability in APIs
Bibliographic record
Abstract
Abstract. Empirical evidence indicates that developers face significant hurdles when the API elements necessary to implement a task are not accessible from the types they are working with. We propose an approach that leverages the structural relationships between API elements to make API methods or types not accessible from a given API type more discoverable. We implemented our approach as an extension to the content assist feature of the Eclipse IDE, in a tool called API Explorer. API Explorer facilitates discoverability in APIs by recommending methods or types, which although not directly reachable from the type a developer is currently working with, may be relevant to solving a programming task. In a case study evaluation, participants experienced little difficulty selecting relevant API elements from the recommendations made by API Explorer, and found the assistance provided by API Explorer helpful in surmounting discoverability hurdles in multiple tasks and various contexts. The results provide evidence that relevant API elements not accessible from the type a developer is working with could be efficiently located through guidance based on structural relationships. 1
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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 | 0.005 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".