3 citations · 7 across the 12 of their papers we have counts for
12 papers
Mitigating Implicit Inconsistencies in Patch Porting
Shengyi Pan, Zhongxin Liu, Jiayuan Zhou +3
Promptly porting patches from a source codebase to its variants (e.g., forks and branches) is essential for mitigating propagated defects and vulnerabilities. Recent studies have e…
Forecasting Developer Environments with GenAI: A Research Perspective
Raula Gaikovina Kula, Christoph Treude, Xing Hu +28
Generative Artificial Intelligence (GenAI) models are achieving remarkable performance in various tasks, including code generation, testing, code review, and program repair. The ab…
Intention Chain-of-Thought Prompting with Dynamic Routing for Code Generation
Shen Li, Li Huang, Shaoxiong Zhan +4
Large language models (LLMs) exhibit strong generative capabilities and have shown great potential in code generation. Existing chain-of-thought (CoT) prompting methods enhance mod…
B4: Towards Optimal Assessment of Plausible Code Solutions with Plausible Tests
Mouxiang Chen, Zhongxin Liu, He Tao +4
Selecting the best code solution from multiple generated ones is an essential task in code generation, which can be achieved by using some reliable validators (e.g., developer-writ…
Exploring the Capabilities of LLMs for Code Change Related Tasks
Lishui Fan, Jiakun Liu, Zhongxin Liu +3
Developers deal with code-change-related tasks daily, e.g., reviewing code. Pre-trained code and code-change-oriented models have been adapted to help developers with such tasks. R…
Automating Zero-Shot Patch Porting for Hard Forks
Shengyi Pan, You Wang, Zhongxin Liu +3
Forking is a typical way of code reuse, which provides a simple way for developers to create a variant software (denoted as hard fork) by copying and modifying an existing codebase…
Pre-training by Predicting Program Dependencies for Vulnerability Analysis Tasks
Zhongxin Liu, Zhijie Tang, Junwei Zhang +2
Vulnerability analysis is crucial for software security. This work focuses on using pre-training techniques to enhance the understanding of vulnerable code and boost vulnerability…
Towards More Realistic Evaluation for Neural Test Oracle Generation
Zhongxin Liu, Kui Liu, Xin Xia +1
Effective unit tests can help guard and improve software quality but require a substantial amount of time and effort to write and maintain. A unit test consists of a test prefix an…
CCT5: A Code-Change-Oriented Pre-Trained Model
Bo Lin, Shangwen Wang, Zhongxin Liu +3
Software is constantly changing, requiring developers to perform several derived tasks in a timely manner, such as writing a description for the intention of the code change, or id…
CCRep: Learning Code Change Representations via Pre-Trained Code Model and Query Back
Zhongxin Liu, Zhijie Tang, Xin Xia +1
Representing code changes as numeric feature vectors, i.e., code change representations, is usually an essential step to automate many software engineering tasks related to code ch…
Plot2API: Recommending Graphic API from Plot via Semantic Parsing Guided Neural Network
Zeyu Wang, Sheng Huang, Zhongxin Liu +4
Plot-based Graphic API recommendation (Plot2API) is an unstudied but meaningful issue, which has several important applications in the context of software engineering and data visu…
Automatic Generation of Pull Request Descriptions
Zhongxin Liu, Xin Xia, Christoph Treude +2
Enabled by the pull-based development model, developers can easily contribute to a project through pull requests (PRs). When creating a PR, developers can add a free-form descripti…