Studying and Automating Issue Resolution for Software Quality

TL;DR

This paper presents new methods leveraging AI, machine learning, and large language models to improve issue report quality, understand developer workflows, and automate complex issue resolution tasks, thereby enhancing software quality.

Contribution

It introduces novel AI-driven techniques for issue report enhancement, empirical analysis of developer workflows, and automation of resolution tasks, advancing the state-of-the-art in software issue management.

Findings

Enhanced issue report quality using LLM reasoning.

Characterized developer workflows in AI-augmented systems.

Automated bug localization and solution identification.

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.