Calibrating Deep Learning-based Code Smell Detection using Human Feedback
Bibliographic record
Abstract
Code smells are inherently subjective in nature. Software developers may have different opinions and perspectives on smelly code. While many attempts have been made to use deep learning-based models for code smell detection, they fail to consider each developer’s subjective perspective while detecting smells. Ignoring this aspect defies the purpose of using deep learning-based smell detection methods because the models are not customized to the developer’s context. This paper proposes a method that considers human feedback to account for such subjectivity. Towards this, we created a plugin for IntelliJ IDEA and developed a container-based web-server to offer services of our baseline deep learning model. The setup allowed developers to see code smells within the IDE and provide feedback. Using this setup, we conducted a controlled experiment with 14 participants divided into experimental and control groups. In the first round of our experiment, we show code smells predicted using the baseline deep learning model and collect feedback from the participants. In the second round, we fine-tune the model based on the experimental group’s feedback and reevaluate its performance before and after adjustment. Our results show that using such calibration improves the performance of the smell detection model by 15.49% in F1 score on average across the participants of the experimental group. Our work carries implications for both researchers and practitioners. Practitioners can apply our approach to enhance the quality of their code in day-to-day development activities, aligning it with their own code smell definitions. Furthermore, software engineering researchers can leverage this study to adopt analogous approaches for addressing similar issues, including code review.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".