Machine Learning-assisted Software Analysis

Abstract

Software engineering, fundamental to modern technological advancement, profoundly influences various aspects of society by enhancing efficiency, accessibility, and security. This discipline involves systematically applying engineering principles to software systems' design, development, testing, and maintenance. Innovations in software engineering have revolutionized industries such as communication, finance, healthcare, and education, democratizing access to information and connecting global communities. As software systems become increasingly complex, the need for efficient, secure, and reliable software analysis tools becomes paramount.

The thesis focuses on improving the actionability and scalability of software analysis by integrating machine learning (ML) techniques. Traditional static analysis tools often struggle with large codebases, leading to high false positive rates and high computational costs. Machine learning, particularly deep learning architectures like Transformers, offers a promising solution by capturing long-range dependencies in code and learning hierarchical representations. This capability enables ML models to automate tasks such as bug detection, source code summarization, and program repair, providing developers with actionable insights and improving overall productivity and code quality.

A significant contribution of this thesis is the development of ML-based techniques for type inference in Python and call graph pruning. An ML-based type inference approach, namely Type4Py, was proposed, which accurately predicts type annotations for Python code, enhancing code quality and reducing runtime errors. ML models with conservative pruning strategies were proposed for call graph pruning, which learns from dynamic traces obtained by executing programs to identify and eliminate false edges, thereby minimizing false positives and improving precision. Additionally, the thesis explores the application of call graphs in vulnerability analysis, demonstrating that granular assessments provide more accurate and actionable insights than more straightforward, dependency-level analyses.

In summary, this thesis advances the field of software analysis by harnessing machine learning to address two important issues related to the actionability and scalability of software analysis tools. The proposed ML-driven tools and techniques enhance the precision and reliability of software analysis and support developers in maintaining robust, secure, and maintainable software systems. These contributions pave the way for future research in applying ML techniques to various aspects of software engineering, promising further improvements in software development practices.