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Main Author:	Tian, Zhao
Format:	Preprint
Published:	2026
Subjects:	Software Engineering Artificial Intelligence Computation and Language
Online Access:	https://arxiv.org/abs/2601.04526
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author	Tian, Zhao
author_facet	Tian, Zhao
contents	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.
format	Preprint
id	arxiv_https___arxiv_org_abs_2601_04526
institution	arXiv
publishDate	2026
record_format	arxiv
spellingShingle	Advancing Language Models for Code-related Tasks Tian, Zhao Software Engineering Artificial Intelligence Computation and Language 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.
title	Advancing Language Models for Code-related Tasks
topic	Software Engineering Artificial Intelligence Computation and Language
url	https://arxiv.org/abs/2601.04526

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