Promises and perils of using Transformer-based models for SE research
Bibliographic record
Abstract
Many Transformer-based pre-trained models for code have been developed and applied to code-related tasks. In this paper, we analyze 519 papers published on this topic during 2017-2023, examine the suitability of model architectures for different tasks, summarize their resource consumption, and look at the generalization ability of models on different datasets. We examine three representative pre-trained models for code: CodeBERT, CodeGPT, and CodeT5, and conduct experiments on the four topmost targeted software engineering tasks from the literature: Bug Fixing, Bug Detection, Code Summarization, and Code Search. We make four important empirical contributions to the field. First, we demonstrate that encoder-only models (CodeBERT) can outperform encoder-decoder models for general-purpose coding tasks, and showcase the capability of decoder-only models (CodeGPT) for certain generation tasks. Second, we study the most frequently used model-task combinations in the literature and find that less popular models can provide higher performance. Third, we find that CodeBERT is efficient in understanding tasks while CodeT5's efficiency is unreliable on generation tasks due to its high resource consumption. Fourth, we report on poor model generalization for the most popular benchmarks and datasets on Bug Fixing and Code Summarization tasks. We frame our contributions in terms of promises and perils, and document the numerous practical issues in advancing future research on transformer-based models for code-related tasks.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".