About

The SOTA (SOfTware & Ai) Research Laboratory investigates fundamental and applied problems in software engineering enhanced by artificial intelligence. Our research combines rigorous theoretical foundations with practical system building.

We are particularly interested in how machine learning and large language models can be leveraged to improve software development processes, from code generation to testing and maintenance. Our interdisciplinary approach brings together expertise in programming languages, machine learning, and systems research.

Research Areas

AI-Assisted Software Engineering

Developing machine learning techniques for automated code generation, bug detection, program repair, and software maintenance.

Program Analysis

Static and dynamic analysis techniques for improving software reliability, security, and performance.

Large Language Models for Code

Investigating the capabilities and limitations of LLMs for code understanding, generation, and transformation tasks.

Software Testing

Automated test generation, mutation testing, and intelligent fuzzing approaches for software quality assurance.

Intelligent Software Systems

Design and implementation of adaptive systems that learn from execution data and user behavior.

Selected Publications

Neural Code Synthesis: A Comprehensive Survey and Future Directions
J. Smith, J. Doe, A. Chen, B. Wilson
ACM Computing Surveys, 2025
Automated Bug Repair Using Reinforcement Learning
A. Chen, J. Smith
Proceedings of ICSE 2025, 2025
Scaling Test Generation with Large Language Models
B. Wilson, J. Doe, J. Smith
Proceedings of FSE 2024, 2024
Adaptive Software Systems: Design Patterns and Implementation
J. Doe, A. Chen
IEEE Transactions on Software Engineering, 2024

View complete publication list →