Yash Mahesh Burshe

ELASTIC: Evaluation of Large Language Models and Abstract Syntax Tree Parsing for Identification of Code Smell Detection

Advised by Dr. Joydeep Mitra

Northeastern University

Building on SmeLLM, this approach uses Static Analysis (particularly Abstract Syntax Tree parsing) to create rules to detect code smells.

Poster

SmeLLM

Advised by Dr. Joydeep Mitra

Northeastern University

A code smell is defined as an anti-pattern when writing code. Presence of code smells can make code harder to read, more difficult to maintain and generally harder to debug or make changes to. The increasing usage of LLMs in the field of Software Engineering lead us to examine the effectiveness of popular LLMs in identifying these code smells and developing a tool that can help evaluate the effectiveness of these LLMs and provide the user feedback about their code.

Enhanced Location Entity Recognition and Extraction

University of Auckland

Enhancing the CLAVIN-NERD model to achieve more accurate location entity recognition and extraction capabilities. Exploring different approaches, such as retraining, fine-tuning and post processing-procedures to improve the model's performance.