API usage templates via structural generalization
Bibliographic record
Abstract
APIs matter in software development, but determining how to use them can be challenging. Developers often refer to a small set of API usage examples, analyzing the information there to understand and adapt them to their own context. Generalization over many examples may aid in understanding commonalities and differences, reducing information overload while including greater variety. We propose ASGard, a novel approach that generates API usage templates from examples. Approximating the formal problem of E-generalization, ASGard generalizes all syntactic and some semantic information within the examples to arrive at pseudocode representations that retain the commonality of the usage examples but abstract the varying aspects. We evaluate the templates from our approach and the patterns generated from PAM and MUDetect (two existing tools for API data mining), using a total of 1,954 API usage examples across 59 different APIs. We measure the quality of the resulting templates: ASGard’s templates have superior completeness and compression. We perform a user study on ASGard with 12 participants to compare the use of these templates in solving programming tasks, compared to MUDetect. We find that participants solved the programming tasks in significantly less time with ASGard. Participants expressed a general preference for using ASGard templates.
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.
How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".