Preetha Chatterjee

Preetha Chatterjee is an Assistant Professor in the Department of Computer Science at Drexel University, where she leads the SOftware Engineering and Analytics Research (SOAR) Lab. Her research lies at the intersection of software engineering, machine learning, and natural language processing, with a focus on improving developer tools and environments through data mining and empirical studies.

• PhD and MS in Computer Science, University of Delaware
• Former Software Engineer with over 5 years of industry experience

Her research interests include software analytics, mining software repositories, empirical software engineering, emotion and toxicity detection in developer communications, and enhancing LLM-assisted software development. She investigates how data-driven techniques can extract actionable knowledge from software artifacts and improve developer productivity and collaboration.

Her recent publications focus on leveraging large language models for bug resolution, detecting security risks in LLM-generated code, mining emotions and moral foundations in open-source communications, and extracting valuable information from developer chat platforms like Slack and Discord. Her work combines empirical studies with state-of-the-art NLP and ML techniques.

Notable scientific recognitions include the Distinguished Reviewer Award at FSE 2023 and research excellence awards for her lab members. She has served on the program committees of top conferences including ICSE, FSE, ASE, and MSR, and on the editorial board of the Journal of Systems and Software (JSS).

She advises multiple PhD, MS, and undergraduate students. She co-leads the Drexel Programming Systems Seminar and actively mentors students in research. Her lab has received recognition for research excellence and leadership. She teaches courses in software engineering and software analytics at both undergraduate and graduate levels at Drexel University.

She maintains active research collaborations and has led the creation of several public datasets and tools for the software engineering community, including datasets on disentangled Slack chats, Discord conversations, and annotated GitHub issue threads.