SEA Lab
Software Engineering Analytics — making software maintenance smarter through automated bug analysis, refactoring intelligence, and program comprehension.
Mission
We focus on the human side of software engineering — understanding how developers interact with software artifacts and building tools that amplify their ability to maintain and improve code. Our work has been recognized with four ACM SIGSOFT Distinguished Paper Awards, reflecting the impact of our contributions to the field.
By the Numbers
Research Areas
The SEA Lab focuses on four interconnected research areas that address core challenges in software maintenance:
Bug Report Analysis
Automated analysis and classification of bug reports to improve defect resolution. Extracting structured information from natural language descriptions to help developers triage and fix bugs faster.
Software Refactoring
Intelligent guidance for code refactoring decisions. Identifying when, where, and how to refactor code to improve quality, reduce technical debt, and enhance maintainability.
Program Comprehension
Understanding how developers read, navigate, and make sense of source code. Building empirically grounded tools that support code understanding during maintenance tasks.
Maintenance Intelligence
AI-powered techniques for predicting maintenance effort, identifying problematic code regions, and recommending evolution strategies for long-lived software systems.
Key Publications
Selected papers from the SEA Lab recognized for their contribution to software engineering research:
Tools & Artifacts
Open-source tools and datasets developed by the SEA Lab:
BugReporter
An automated bug report classification and quality assessment tool. Analyzes issue reports to identify missing information and suggest improvements for faster resolution.
RefactorGuide
An intelligent refactoring recommendation engine that identifies code smells, suggests appropriate refactoring operations, and estimates the impact on code quality metrics.
Bug Report Corpus
A curated, annotated dataset of thousands of bug reports from open-source projects, labeled for quality, completeness, and defect type classification.
ComprehensionKit
An experimental framework for conducting program comprehension studies, including eye-tracking integration and developer interaction logging.
Team
Meet the researchers behind the SEA Lab: