Advancing Language Models for Code-related Tasks
By: Zhao Tian
Potential Business Impact:
Helps computers write better computer code.
Recent advances in language models (LMs) have driven significant progress in various software engineering tasks. However, existing LMs still struggle with complex programming scenarios due to limitations in data quality, model architecture, and reasoning capability. This research systematically addresses these challenges through three complementary directions: (1) improving code data quality with a code difference-guided adversarial augmentation technique (CODA) and a code denoising technique (CodeDenoise); (2) enhancing model architecture via syntax-guided code LMs (LEAM and LEAM++); and (3) advancing model reasoning with a prompting technique (muFiX) and an agent-based technique (Specine). These techniques aim to promote the practical adoption of LMs in software development and further advance intelligent software engineering.
Similar Papers
Language Models for Code Optimization: Survey, Challenges and Future Directions
Software Engineering
Makes computer programs run much faster.
From Code Foundation Models to Agents and Applications: A Practical Guide to Code Intelligence
Software Engineering
Helps computers write computer programs from words.
From Code Foundation Models to Agents and Applications: A Practical Guide to Code Intelligence
Software Engineering
Makes computers write computer programs from your words.