Deep API Sequence Generation via Golden Solution Samples and API Seeds
Bibliographic record
Abstract
Automatic API recommendation can accelerate developers’ programming and has been studied for years. There are two orthogonal lines of approaches for this task, i.e., information retrieval-based (IR-based) approaches and sequence to sequence (seq2seq) model-based approaches. Although these approaches were reported to have remarkable performance, our observation finds two major drawbacks, i.e., IR-based approaches lack the consideration of relations among the recommended APIs, and seq2seq models do not model the API’s semantic meaning. To alleviate the above two problems, we propose APIGens, which is a retrieval-enhanced large language model (LLM)-based API recommendation approach to recommend an API sequence for a natural language query. The approach first retrieves similar programming questions in history based on the input natural language query, and then scores the results based on API documents via a scorer model. Finally, these results are used as samples for few-shot learning of LLM. To reduce the risk of encountering local optima, we also extract API seeds from the retrieved results to increase the search scope during the LLM generation process. The results show that our approach can achieve 48.41% ROUGE@10 on API sequence recommendation and the 82.61% MAP on API set recommendation, largely outperforming the state-of-the-art baselines.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".