Which API is Faster: Mining Fine-grained Performance Opinion from Online Discussions
Bibliographic record
Abstract
Inefficient API usage is one of the main reasons for software performance issues. Current practice of API documentation mainly provides its functionalities, while the performance related information are seldom covered in the official documentation. Meanwhile, the online discussions brings various pieces of information about the efficiency of API, yet buried in massive messages. Existing approaches would derive API opinion with pattern-based techniques, and typically result in inaccurate and coarse-grained result. This paper proposes a relation-aware approach RAMiner for the fine-grained API-related performance opinion mining from online discussions. It leverages pre-trained Large Language Model (LLM), thus can better capture the semantics of the text and API tokens. Besides, it disentangles the task into subtasks to cope with the situation of limited labeled data for fine-tuning the model, and incorporates relation-aware design for capturing the fine-grained opinion of each mentioned API. The experimental results show that, RAMiner can correctly predict 70% opinions, which largely outperforms the baselines. We also demonstrate its potential usage in promoting the code generation models in recommending more efficient code snippets. This approach can also be utilized to extract other non-functional opinions, e.g., security, compatibility.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".