Do Current Language Models Support Code Intelligence for R Programming Language?
Bibliographic record
Abstract
Recent advancements in developing Pre-trained Language Models for Code (Code-PLMs) have urged many areas of Software Engineering (SE) and brought breakthrough results for many SE tasks. Though these models have achieved the state-of-the-art performance for SE tasks for many popular programming languages, such as Java and Python, the Scientific Software and its related languages like R programming language have rarely benefited or even been evaluated with the Code-PLMs. Research has shown that R has many differences with other programming languages and requires specific techniques. In this study, we provide the first insights for code intelligence for R. For this purpose, we collect and open source an R dataset, and evaluate Code-PLMs for the two tasks of code summarization and method name prediction using several settings and strategies, including the differences in two R styles, Tidy-verse and Base R. Our results demonstrate that the studied models have experienced varying degrees of performance degradation when processing R programming language code, which is supported by human evaluation. Additionally, not all models show performance improvement in R-specific tasks even after multi-language fine-tuning. The dual syntax paradigms in R significantly impact the models’ performance, particularly in code summarization tasks. Furthermore, the project-specific context inherent in R codebases significantly impacts the performance when attempting cross-project training. Interestingly, even when Large Language Models like CodeLlama and StarCoder2 are used for code generation, the Pass@K ( \(K = 1,5,10\) ) results lag significantly behind Python scores. Our research shows that R as a low-resource language requires different techniques to collect a high-quality data. Specifically separating the two R styles has a great impact on the results and the separate dataset could increase the performance of the models. Our research sheds light on the capabilities of Code-PLMs and opens new research directions for researchers and practitioners for developing code intelligence tools and techniques for R. With R’s widespread use and popularity, the results of our study can potentially benefit a large community of R developers, both in research and industry.
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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 | 0.011 | 0.076 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.013 |
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".