Pre-trained Model Based Feature Envy Detection

Ma, Wenhao; Yu, Yaoxiang; Ruan, Xiaoming; Cai, Bo

doi:10.1109/msr59073.2023.00065

Cited by 2 publications

(2 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Pan et al [25] encoded Java source code files using a pre-trained CodeBERT model to aid software defect prediction. Kovačević et al [46] conducted experiments with the Code2Vec, Code2Seq, and CuBERT models to represent Java methods or classes as code embeddings, facilitating machine-learning-based detection of two code smells, i.e., long method and god class, while Ma et al [26] leveraged the CodeT5, CodeGPT, and CodeBERT models to detect the feature envy code smell. To compare software systems, Karakatič et al [47] utilized a pre-trained Code2Vec model to embed Java methods.…”

Section: Pre-trained Models In Code-related Tasksmentioning

confidence: 99%

“…To achieve this, we evaluated the proposed method using two BERT-based code models, i.e., CodeBERT and GraphCodeBERT. These two code models were selected based on existing studies indicating their success in capturing code syntax and semantics, making them suitable for tasks requiring source code understanding [25][26][27][28][29][30][31][32]. To further train the pre-trained models of CodeBERT and GraphCodeBERT in a self-supervised manner, i.e., to perform task-adaptive pre-training, we constructed a dataset of more than 45,000 files affected by commit operations.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Commit-Level Software Change Intent Classification Using a Pre-Trained Transformer-Based Code Model

Heričko,

Šumak,

Karakatič

2024

Mathematics

View full text Add to dashboard Cite

Software evolution is driven by changes made during software development and maintenance. While source control systems effectively manage these changes at the commit level, the intent behind them are often inadequately documented, making understanding their rationale challenging. Existing commit intent classification approaches, largely reliant on commit messages, only partially capture the underlying intent, predominantly due to the messages’ inadequate content and neglect of the semantic nuances in code changes. This paper presents a novel method for extracting semantic features from commits based on modifications in the source code, where each commit is represented by one or more fine-grained conjoint code changes, e.g., file-level or hunk-level changes. To address the unstructured nature of code, the method leverages a pre-trained transformer-based code model, further trained through task-adaptive pre-training and fine-tuning on the downstream task of intent classification. This fine-tuned task-adapted pre-trained code model is then utilized to embed fine-grained conjoint changes in a commit, which are aggregated into a unified commit-level vector representation. The proposed method was evaluated using two BERT-based code models, i.e., CodeBERT and GraphCodeBERT, and various aggregation techniques on data from open-source Java software projects. The results show that the proposed method can be used to effectively extract commit embeddings as features for commit intent classification and outperform current state-of-the-art methods of code commit representation for intent categorization in terms of software maintenance activities undertaken by commits.

show abstract