Identification and Optimization of Redundant Code Using Large Language Models
Mon 28 Apr 2025 16:00 - 16:20 at 212 - Doctoral Symposium 3 (Detailed Presentation)
Redundant code is a persistent challenge in software development that makes systems harder to maintain, scale, and update. It adds unnecessary complexity, hinders bug fixes, and increases technical debt. Despite their impact, removing redundant code manually is risky and error-prone, often introducing new bugs or missing dependencies. While studies highlight the prevalence and negative impact of redundant code, little focus has been given to Artificial Intelligence (AI) system codebases and the common patterns that cause redundancy. Additionally, the reasons behind developers unintentionally introducing redundant code remain largely unexplored. This research addresses these gaps by leveraging large language models (LLMs) to automatically detect and optimize redundant code in AI projects. Our research aims to identify recurring patterns of redundancy and analyze their underlying causes, such as outdated practices or insufficient awareness of best coding principles. Additionally, we plan to propose an LLM agent that will facilitate the detection and refactoring of redundancies on a large scale while preserving original functionality. This work advances the application of AI in identifying and optimizing redundant code, ultimately helping developers maintain cleaner, more readable, and scalable codebases.
Sun 27 AprDisplayed time zone: Eastern Time (US & Canada) change
Mon 28 AprDisplayed time zone: Eastern Time (US & Canada) change
16:00 - 17:30 | |||
16:00 20mTalk | Identification and Optimization of Redundant Code Using Large Language Models Doctoral Symposium Shamse Tasnim Cynthia University of Saskatchewan | ||
16:20 20mTalk | Systematic Testing of Security-Related Defects in LLM-Based Applications Doctoral Symposium Hasan Kaplan Jheronimus Academy of Data Science, Tilburg University | ||
16:40 20mTalk | Model-Based Verification for AI-Enabled Cyber-Physical Systems through Guided Falsification of Temporal Logic Properties Doctoral Symposium Hadiza Yusuf University of Michigan - Dearborn |