Studying and Automating Issue Resolution for Software Quality

• URL: http://arxiv.org/abs/2512.10238v1
• Date: Thu, 11 Dec 2025 02:44:40 GMT
• Title: Studying and Automating Issue Resolution for Software Quality
• Authors: Antu Saha,
• Abstract summary: This research aims to address challenges through three complementary directions.<n>First, we enhance issue report quality by proposing techniques that leverage LLM reasoning and application-specific information.<n>Second, we empirically characterize developer in both traditional and AI-augmented systems.<n>Third, we automate cognitively demanding resolution tasks, including buggy UI localization and solution identification.
• Score: 1.7767466724342065
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Effective issue resolution is crucial for maintaining software quality. Yet developers frequently encounter challenges such as low-quality issue reports, limited understanding of real-world workflows, and a lack of automated support. This research aims to address these challenges through three complementary directions. First, we enhance issue report quality by proposing techniques that leverage LLM reasoning and application-specific information. Second, we empirically characterize developer workflows in both traditional and AI-augmented systems. Third, we automate cognitively demanding resolution tasks, including buggy UI localization and solution identification, through ML, DL, and LLM-based approaches. Together, our work delivers empirical insights, practical tools, and automated methods to advance AI-driven issue resolution, supporting more maintainable and high-quality software systems.

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