Self-Admitted Technical Debt (SATD) refers to sub-optimal solutions deliberately introduced to accelerate the software development process, often at the expense of software maintainability and sustainability. Therefore, timely identification and repayment of the SATD is critical for the software system. As exploration deepens, it is found that effectively prioritizing the repayment of SATD with more significant impacts on software quality requires not only identifying SATD but also further classifying it. However, existing SATD identification and classification approaches face the following challenges: (1) SATDs originate from diverse sources. Code comments are a widespread source, but recent research has revealed that SATDs can originate from other sources, such as pull requests, issues, and commit messages. Nonetheless, existing approaches primarily target code comments, lacking the capability to analyze SATDs from other sources effectively. (2) SATDs fall into diverse categories. Nonetheless, existing SATD classification approaches fail to address all SATD categories comprehensively and show inadequate performance. (3) Imbalance of existing SATD datasets. Real-world SATD data are scarce, making dataset collection challenging. Moreover, SATD distribution across different sources is uneven, further complicating the construction of high-quality datasets. To alleviate these challenges, this article presents an SATD identification and classification framework named IMPACT . First, IMPACT employs ChatGPT to construct an augmented dataset. Subsequently, it utilizes a pipeline with two fine-tuned language models of different parameter sizes to identify and classify SATD separately. To evaluate the effectiveness of IMPACT, we compare it with three state-of-the-art SATD classification methods and its two foundation models. Experimental results demonstrate that IMPACT outperforms state-of-the-art methods by a large margin, and even surpasses its foundation model GLM-4-9B-Chat. It achieves the optimal average F1 score of 0.697 on the source of pull requests, the most challenging data source. Moreover, experiments on the cross-project test set show that IMPACT demonstrates strong generalizability on unseen project data.

In recent years, pre-trained language models have seen significant success in natural language processing and have been increasingly applied to code-related tasks. Code intelligence tasks have shown promising performance with the support of code pre-trained language models. Pre-processing code simplification methods have been introduced to prune code tokens from the model’s input while maintaining task effectiveness. These methods improve the efficiency of code intelligence tasks while reducing computational costs. Post-prediction code simplification methods provide explanations for code intelligence task outcomes, enhancing the reliability and interpretability of model predictions. However, comprehensive evaluations of these methods across diverse code pre-trained model architectures and code intelligence tasks are lacking. To assess the effectiveness of code simplification methods, we conduct an empirical study integrating these code simplification methods with various pre-trained code models across multiple code intelligence tasks. Our empirical findings suggest that developing task-specific code simplification methods would be beneficial. Then, we recommend leveraging post-prediction methods to summarize prior knowledge, which can pre-process code simplification strategies. Moreover, establishing more evaluation mechanisms for code simplification is crucial. Finally, we propose incorporating code simplification methods into the pre-training phase of code pre-trained models to enhance their program comprehension and code representation capabilities.

Pre-trained models (PTMs) have succeeded in various software engineering (SE) tasks following the “pre-train then fine-tune” paradigm. As fully fine-tuning all parameters of PTMs can be computationally expensive, a potential solution is parameter-efficient fine-tuning (PEFT), which freezes PTMs while introducing extra parameters. Although PEFT methods have been applied to SE tasks, researchers often focus on specific scenarios and lack a comprehensive comparison of PTMs from different aspects such as field, size, and architecture. To fill this gap, we have conducted an empirical study on six PEFT methods, eight PTMs, and four SE tasks. The experimental results reveal several noteworthy findings. For example, model architecture has little impact on PTM performance when using PEFT methods. Additionally, we provide a comprehensive discussion of PEFT methods from three perspectives. First, we analyze the effectiveness and efficiency of PEFT methods. Second, we explore the impact of the scaling factor hyperparameter. Finally, we investigate the application of PEFT methods on the latest open source large language model, Llama 3.2. These findings provide valuable insights to guide future researchers in effectively applying PEFT methods to SE tasks.

Zhiwei Fei, Xiaoyu Shen, Dawei Zhu, Fengzhe Zhou, Zhuo Han, Alan Huang, Songyang Zhang, Kai Chen, Zhixin Yin, Zongwen Shen, Jidong Ge, Vincent Ng. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. 2024.

Code changes are crucial because all code repositories can be viewed as composed of a series of code changes. Recent works on code changes prefer to use pre-trained models (PTMs) to capture the code change representations and have achieved remarkable success. However, these works usually compromise the original code representations of PTMs and ignore the relation of different code-change-related tasks. To boost the existing solutions to code-change-related tasks, we propose a new two-stage Code Change representation learning method using AdapterFusion, which is called CCAF. The first stage is knowledge extraction, where we freeze the parameters of the PTM and fine-tune additional parameters known as adapters. Each adapter acquires knowledge from a specific code-change-related task. The second stage, knowledge composition, employs AdapterFusion to compose the knowledge from all adapters, enhancing the PTM’s performance on a specific code-change-related task. To assess the effectiveness of CCAF, we employ CodeT5 as the base PTM, with its parameters frozen, and apply CCAF to three code-change-related tasks: commit message generation, automated patch correctness assessment, and just-in-time defect prediction. The experimental results indicate that CCAF not only outperforms a fully fine-tuned CodeT5 but also performs comparably to the state-of-the-art method, CCRep.