A Simple, Efficient, Context‐sensitive Approach for Code Completion
Bibliographic record
Abstract
Abstract Code completion helps developers use application programming interfaces (APIs) and frees them from remembering every detail. In this paper, we first describe a novel technique called Context‐sensitive Code Completion (CSCC) for improving the performance of API method call completion. CSCC is context sensitive in that it uses new sources of information as the context of a target method call. CSCC indexes method calls in code examples by their context. To recommend completion proposals, CSCC ranks candidate methods by the similarities between their contexts and the context of the target call. Evaluation using a set of subject systems and five popular state‐of‐the‐art techniques suggests that CSCC performs better than existing type or example‐based code completion systems. We conduct experiments to find how different contextual elements of the target call benefit CSCC. Next, we investigate the adaptability of the technique to support another form of code completion, i.e., field completion. Evaluation with eight different subject systems suggests that CSCC can easily support field completion with high accuracy. Finally, we compare CSCC with four popular statistical language models that support code completion. Results of statistical tests from our study suggest that CSCC not only outperforms those techniques that are based on token level language models, but also in most cases performs better or equally well with GraLan, the state‐of‐the‐art graph‐based language model. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".