Date of Defense
31-3-2026 3:00 PM
Location
E1-1041
Document Type
Thesis Defense
Degree Name
Master of Science in Software Engineering
College
College of Information Technology
Department
Computer Science and Software Engineering
First Advisor
Yasir Mahmood
Keywords
Coding Agent, Pull Requests, Large Language Models (LLM), Human-Computer Interaction (HCI), Prompt Engineering, Software Engineering
Abstract
This thesis investigates the real-world behavior of LLM-driven coding agents that generate code changes and submit pull requests (PRs) to public software repositories. As these tools evolve from autocomplete-style assistants into more autonomous agents, their contributions increasingly interact with socio-technical review processes (human reviewers, bots, CI/CD gates, and project norms). This thesis focuses on understanding why agent-generated PRs are accepted or rejected and what these outcomes reveal about current agent limitations in practical development workflows. The main objective of this thesis is to systematically characterize rejection patterns and failure modes of agent-generated pull requests in real repositories. Specifically, the thesis aims to quantify acceptance vs. rejection trends across agents and time, derive a structured taxonomy of rejection reasons grounded in reviewer comments and repository signals, and translate the empirical findings into actionable recommendations for practitioners and for designers of more reliable, reflexive coding agents. The research employs an empirical software engineering methodology combining large-scale data mining, quantitative analysis, and qualitative labeling.
Included in
Software Engineering in the Age of Coding Agents: Failure Modes and Rejection Patterns
E1-1041
This thesis investigates the real-world behavior of LLM-driven coding agents that generate code changes and submit pull requests (PRs) to public software repositories. As these tools evolve from autocomplete-style assistants into more autonomous agents, their contributions increasingly interact with socio-technical review processes (human reviewers, bots, CI/CD gates, and project norms). This thesis focuses on understanding why agent-generated PRs are accepted or rejected and what these outcomes reveal about current agent limitations in practical development workflows. The main objective of this thesis is to systematically characterize rejection patterns and failure modes of agent-generated pull requests in real repositories. Specifically, the thesis aims to quantify acceptance vs. rejection trends across agents and time, derive a structured taxonomy of rejection reasons grounded in reviewer comments and repository signals, and translate the empirical findings into actionable recommendations for practitioners and for designers of more reliable, reflexive coding agents. The research employs an empirical software engineering methodology combining large-scale data mining, quantitative analysis, and qualitative labeling.