Using Structure-Based Recommendations to Facilitate Discoverability in APIs
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
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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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it