An Automated Approach Towards Code Comment Smell Detection and Repair
Hatice Kübra Çağlar
Master Student
(Supervisor: Assoc.Prof. Eray Tüzün) Computer Engineering Department
Bilkent University
Abstract: Code comments are essential for software comprehension and maintenance; however, low-quality or inconsistent comments, referred to as code comment smells, can mislead developers and reduce code quality. This thesis proposes a classification-based framework for the automatic detection and repair of inline code comment smells. An enhanced dataset of 2,211 labeled inline comments, extended with manually curated repairs, is used to evaluate four large language models (GPT-4o-mini, o3-mini, DeepSeek-V3, and Codestral-2501) under zero-shot and few-shot prompting strategies. Experimental results show that lightweight instruction-tuned models achieve competitive detection performance, while code-specialized models demonstrate stronger repair capability. The findings highlight challenges related to dataset imbalance, prompt sensitivity, and error propagation between detection and repair stages. To demonstrate practical feasibility, the proposed framework is implemented as a prototype tool, SmellSolver, integrated into Azure DevOps pull request workflows. Although not yet evaluated through user studies, the prototype illustrates the industrial applicability of automated inline comment smell detection and repair.
DATE: February 27, Friday @ 15:30 Place: EA 409