5 papers
A Framework for Identifying, Categorizing, and Explaining Bias in AI-Generated Code
Manaal Basha, Aimee M. Ribeiro, Gema Rodriguez-Perez
As Large Language Models (LLMs) become integrated into software development workflows, concerns regarding unintentional biases in AI-generated code. Although evidence suggests thes…
Do Influence Tactics Matter? Investigating Prompt Framing Effects in LLM Code Generation
Alex Deaconu, Anubhav Gupta, Manaal Basha +2
Large Language Models (LLMs) are increasingly integrated into software engineering workflows, helping developers write, debug, test, and maintain code. While prompt wording and str…
CodeWatcher: IDE Telemetry Data Extraction Tool for Understanding Coding Interactions with LLMs
Manaal Basha, Aimeê M. Ribeiro, Jeena Javahar +2
Understanding how developers interact with code generation tools (CGTs) requires detailed, real-time data on programming behavior which is often difficult to collect without disrup…
Cracking CodeWhisperer: Analyzing Developers' Interactions and Patterns During Programming Tasks
Jeena Javahar, Tanya Budhrani, Manaal Basha +3
The use of AI code-generation tools is becoming increasingly common, making it important to understand how software developers are adopting these tools. In this study, we investiga…
Toward Inclusive AI-Driven Development: Exploring Gender Differences in Code Generation Tool Interactions
Manaal Basha, Ivan Beschastnikh, Gema Rodriguez-Perez +1
The increasing reliance on Code Generation Tools (CGTs), such as Claude Code and GitHub Copilot, is revamping programming workflows and raising critical questions about fairness an…