Graph neural network-based long method and blob code smell detection
Bibliographic record
Abstract
• We propose a graph neural network-based model for long method and blob code smell detection. • The best strategies for the class imbalance of graph data and graph pooling are determined through experiments in our method. • During model design for abstract syntax tree of code, Euclidean space and non-Euclidean space are combined. • The experiments show that our proposed method outperforms machine learning methods and deep learning methods. The concept of code smell was first proposed in the late nineties, to refer to signals that code may need refactoring. While not necessarily affecting functionality, code smell can hinder understandability and future scalability of the program. As a result, the precise detection of code smell has become an important topic in coding research. However, current detection methods are limited by imbalanced and industrial-irrelevant datasets, a lack of sufficient structural and logical information on the code, and simple model architecture. Given these limitations, this paper utilized an industry-relevant and sufficient dataset and then developed a graph neural network to better detect code smell. First, we identified Long Method and Blob as our research subjects due to their frequent occurrence and impacts on the maintainability of software. We then designed modified fuzzy sampling with focalloss to address the issue of data imbalance. Second, to deal with the large volume of data, we proposed a global and local attention scoring mechanism to extract the key information from the code. Third, in order to design a graph neural network specifically for the abstract syntax tree of code, we combined Euclidean space and non-Euclidean space. Finally, we compared our method with other machine learning methods and deep learning methods. The results demonstrate that our method outperforms the other methods on Long Method and Blob, which indicates the effectiveness of our proposed method.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".