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Lecture

Learning-aided Program Reasoning

Description

This lecture by the instructor covers the fundamental challenges in program reasoning, such as bug-finding and verification, using examples like the Heartbleed bug. It explores the use of static analysis tools like Clang Static Analyzer and the application of differential Bayesian reasoning to prioritize true alarms over false alarms. The lecture also delves into the automation and accuracy challenges in program verification, showcasing research contributions leveraging machine learning. The talk outlines differential Bayesian reasoning frameworks and deep reinforcement learning frameworks for program verification, emphasizing the importance of learning-aided approaches in addressing these challenges.

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Machine learning

Machine learning (ML) is an umbrella term for solving problems for which development of algorithms by human programmers would be cost-prohibitive, and instead the problems are solved by helping machines 'discover' their 'own' algorithms, without needing to be explicitly told what to do by any human-developed algorithms. Recently, generative artificial neural networks have been able to surpass results of many previous approaches.

Static program analysis

In computer science, static program analysis (or static analysis) is the analysis of computer programs performed without executing them, in contrast with dynamic program analysis, which is performed on programs during their execution. The term is usually applied to analysis performed by an automated tool, with human analysis typically being called "program understanding", program comprehension, or code review. In the last of these, software inspection and software walkthroughs are also used.

Program analysis

In computer science, program analysis is the process of automatically analyzing the behavior of computer programs regarding a property such as correctness, robustness, safety and liveness. Program analysis focuses on two major areas: program optimization and program correctness. The first focuses on improving the program’s performance while reducing the resource usage while the latter focuses on ensuring that the program does what it is supposed to do.

Deep learning

Deep learning is part of a broader family of machine learning methods, which is based on artificial neural networks with representation learning. The adjective "deep" in deep learning refers to the use of multiple layers in the network. Methods used can be either supervised, semi-supervised or unsupervised.

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