Colloquium - Pengyu Nie, The University of Texas at Austin, Execution-Guided Learning for Software Development, Testing, and Maintenance

Pengyu Nie
Monday, January 30, 2023 - 4:25pm to 5:25pm
Event Type: 

Machine Learning (ML) techniques have been increasing adopted for Software Engineering (SE) tasks, such as code completion and code summarization. However, existing ML models provide limited value for SE tasks, because these models do not take into account the key characteristics of software: software is executable and software constantly evolves. In this talk, I will present my insights and work on developing execution-guided and evolutionaware ML models for several SE tasks targeting important domains, including software testing, verification, and maintenance.

First, I will present my techniques to help developers write tests and formal proofs. My work has direct impact on software correctness and everyone that depends on software. I will present TeCo: the first ML model for test completion/generation, and Roosterize: the first model for lemma name generation. In order to achieve good performance, these two tasks require reasoning about code execution, which existing ML models are not capable of. To tackle this problem, I designed and develop ML models that integrate execution data and use such data to validate generation results. Next, I will present my techniques to help developers maintain software. Specifically, I will present my work on comment updating, i.e., automatically updating comments when associated code changes. I proposed the first edit ML model for SE to solve this task, which learns to perform developer-like edits instead of generating comments from scratch. I will also describe the way I generalized this model for general-purpose software editing, including tasks such as bug fixing and automated code review. All my code and data are open-sourced, evaluated on real-world software, and shown to outperform existing ML models by large margins. My contributions lay the foundation for the development of accurate, robust, and interpretable ML models for SE.