--Pre-trained code models are essential for various code intelligence tasks. Y et, their effectiveness is heavily influenced by the quality of the pre-training dataset, particularly human-written reference comments, which usually serve as a bridge between the programming language and natural language. One significant challenge is that such comments could become inconsistent with the corresponding code as the software evolves, leading to suboptimal model performance. Large language models (LLMs) have demonstrated superior capabilities in generating high-quality code comments. This work investigates whether substituting original human-written comments with LLM-generated ones can improve pre-training datasets for more effective pre-trained code models. As existing reference-based metrics cannot evaluate the quality of human-written reference comments themselves, to enable direct comparison between LLM-generated and human reference comments, we introduce two auxiliary tasks as novel reference-free metrics, including code-comment inconsistency detection and semantic code search. Experimental results show that LLM-generated comments exhibit superior semantic consistency with the code compared to human-written reference comments. Our manual evaluation also corroborates this conclusion, which indicates the potential of utilizing LLMs to enhance the quality of the pre-training dataset. Based on this finding, we rebuilt the CodeSearchNet dataset with LLM-generated comments and re-pre-trained the CodeT5 model. Evaluations on multiple code intelligence tasks demonstrate that models pre-trained by LLM-enhanced data outperform their counterparts (pre-trained by original human reference comments data) on code summarization, code generation, and code translation tasks. This research validates the feasibility of rebuilding the pre-training dataset by LLMs to advance code intelligence tasks. It advocates rethinking the reliance on human reference comments for code-related tasks. I NTRODUCTION In the realm of code intelligence, pre-trained code models have significantly enhanced a spectrum of tasks such as code This research was supported by the the National Key Research and Development Program of China (Grant No.2023YFB4503802), the National Natural Science Foundation of China (Grant No.62172426,62302515) and the NUDT Research Project for Student(No.ZC525Z042402). Xinjun Mao and Shangwen Wang are the corresponding authors. Pre-trained source code models such as CodeBERT [11], GraphCodeBERT [12], CodeT5 [13] and UniXcoder [14] have achieved remarkable results on various software engineering tasks and even outperform large language models when fine-tuned with domain-specific data [15]. The comments of the corresponding code snippets serve as a crucial bridge between the programming language (PL) and natural language (NL), providing contextual understanding pivots that are vital for pre-training the above models.

Code intelligence is an emerging domain in software engineering, aiming to improve the effectiveness and efficiency of various code-related tasks. Recent research suggests that incorporating contextual information beyond the basic original task inputs (i.e., source code) can substantially enhance model performance. Such contextual signals may be obtained directly or indirectly from sources such as API documentation or intermediate representations like abstract syntax trees can significantly improve the effectiveness of code intelligence. Despite growing academic interest, there is a lack of systematic analysis of context in code intelligence. To address this gap, we conduct an extensive literature review of 146 relevant studies published between September 2007 and August 2024. Our investigation yields four main contributions. (1) A quantitative analysis of the research landscape, including publication trends, venues, and the explored domains; (2) A novel taxonomy of context types used in code intelligence; (3) A task-oriented analysis investigating context integration strategies across diverse code intelligence tasks; (4) A critical evaluation of evaluation methodologies for context-aware methods. Based on these findings, we identify fundamental challenges in context utilization in current code intelligence systems and propose a research roadmap that outlines key opportunities for future research.

As Pre-trained Language Models (PLMs), a popular approach for code intelligence, continue to grow in size, the computational cost of their usage has become prohibitively expensive. Prompt learning, a recent development in the field of natural language processing, emerges as a potential solution to address this challenge. In this paper, we investigate the effectiveness of prompt learning in code intelligence tasks. We unveil its reliance on manually designed prompts, which often require significant human effort and expertise. Moreover, we discover existing automatic prompt design methods are very limited to code intelligence tasks due to factors including gradient dependence, high computational demands, and limited applicability. To effectively address both issues, we propose Genetic Auto Prompt (GenAP), which utilizes an elaborate genetic algorithm to automatically design prompts. With GenAP, non-experts can effortlessly generate superior prompts compared to meticulously manual-designed ones. GenAP operates without the need for gradients or additional computational costs, rendering it gradient-free and cost-effective. Moreover, GenAP supports both understanding and generation types of code intelligence tasks, exhibiting great applicability. We conduct GenAP on three popular code intelligence PLMs with three canonical code intelligence tasks including defect prediction, code summarization, and code translation. The results suggest that GenAP can effectively automate the process of designing prompts. Specifically, GenAP outperforms all other methods across all three tasks (e.g., improving accuracy by an average of 2.13% for defect prediction). To the best of our knowledge, GenAP is the first work to automatically design prompts for code intelligence PLMs.

Code intelligence plays a key role in transforming modern software engineering. Recently, deep learning-based models, especially Transformer-based large language models (LLMs), have demonstrated remarkable potential in tackling these tasks by leveraging massive open-source code data and programming language features. However, the development and deployment of such models often require expertise in both machine learning and software engineering, creating a barrier for the model adoption. In this paper, we present CodeTF, an open-source Transformer-based library for state-of-the-art Code LLMs and code intelligence. Following the principles of modular design and extensible framework, we design CodeTF with a unified interface to enable rapid access and development across different types of models, datasets and tasks. Our library supports a collection of pretrained Code LLM models and popular code benchmarks, including a standardized interface to train and serve code LLMs efficiently, and data features such as language-specific parsers and utility functions for extracting code attributes. In this paper, we describe the design principles, the architecture, key modules and components, and compare with other related library tools. Finally, we hope CodeTF is able to bridge the gap between machine learning/generative AI and software engineering, providing a comprehensive open-source solution for developers, researchers, and practitioners.

Pre-trained models have been shown effective in many code intelligence tasks. These models are pre-trained on large-scale unlabeled corpus and then fine-tuned in downstream tasks. However, as the inputs to pre-training and downstream tasks are in different forms, it is hard to fully explore the knowledge of pre-trained models. Besides, the performance of fine-tuning strongly relies on the amount of downstream data, while in practice, the scenarios with scarce data are common. Recent studies in the natural language processing (NLP) field show that prompt tuning, a new paradigm for tuning, alleviates the above issues and achieves promising results in various NLP tasks. In prompt tuning, the prompts inserted during tuning provide task-specific knowledge, which is especially beneficial for tasks with relatively scarce data. In this paper, we empirically evaluate the usage and effect of prompt tuning in code intelligence tasks. We conduct prompt tuning on popular pre-trained models CodeBERT and CodeT5 and experiment with three code intelligence tasks including defect prediction, code summarization, and code translation. Our experimental results show that prompt tuning consistently outperforms fine-tuning in all three tasks. In addition, prompt tuning shows great potential in low-resource scenarios, e.g., improving the BLEU scores of fine-tuning by more than 26\% on average for code summarization. Our results suggest that instead of fine-tuning, we could adapt prompt tuning for code intelligence tasks to achieve better performance, especially when lacking task-specific data.