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Qianxiang Wang

3 accepted papers

2025

CodeV: Issue Resolving with Visual Data

ACL 2025finding

Large Language Models (LLMs) have advanced rapidly in recent years, with their applications in software engineering expanding to more complex repository-level tasks. GitHub issue resolving is a key challenge among these tasks. While recent approaches have made progress on this task, they focus on te…

2024

CodeM: Less Data Yields More Versatility via Ability Matrix

ACL 2024findings

In the era of code large language models (code LLMs), data engineering plays a pivotal role during the instruction fine-tuning phase. To train a versatile model, previous efforts devote tremendous efforts into crafting instruction data covering all the downstream scenarios. Nonetheless, this will in…

2024

VulLibGen: Generating Names of Vulnerability-Affected Packages via a Large Language Model

ACL 2024long

Security practitioners maintain vulnerability reports (e.g., GitHub Advisory) to help developers mitigate security risks. An important task for these databases is automatically extracting structured information mentioned in the report, e.g., the affected software packages, to accelerate the defense…