Bilkent University
Department of Computer Engineering
CS 590/690 SEMINAR
Automated Classification of Software Bugs: Leveraging Large Language Models to Distinguish Bohrbugs and Mandelbugs
Mehmet Taha Demircan
Master Student
(Supervisor:Assoc.Prof.Eray Tüzün)
Computer Engineering Department
Bilkent University
Abstract: Research on the categorization of software bugs into Bohrbugs and Mandelbugs often hinges on the ability to interpret complex, non-deterministic failure patterns. Recent advancements in reasoning-oriented LLMs offer a new paradigm for analyzing bug reports and system logs with higher-level semantic understanding. This study investigates the efficacy of reasoning LLMs in automated bug classification, specifically distinguishing between Bohrbugs and Mandelbugs. The research further extends into the sub-categorization of Mandelbugs into aging-related and non-aging-related types, as well as more granular groups based on specific system behaviors and failure mechanisms. By leveraging the inherent causal inference capabilities of these models, the proposed approach aims to capture the underlying root causes of software faults more effectively than static analysis or traditional classification techniques. The findings suggest that reasoning LLMs not only enhance classification performance across these complex taxonomies but also provide improved interpretability, representing a significant shift toward more sophisticated, automated debugging systems.
DATE: March 09, Monday @ 16:30 Place: EA 502