Automata Models for Effective Bug Pattern Description

TL;DR

This paper introduces automata-based techniques to generate concise, informative bug descriptions by focusing on essential failure patterns, thereby improving bug detection and understanding in complex systems.

Contribution

It presents a novel automata learning approach that extracts meaningful bug pattern summaries using Failure Explanations, Eventual Failure Explanations, and Early Detection concepts.

Findings

Effective bug pattern summarization demonstrated on real-world benchmarks

Automata models improve bug detection accuracy

Approach reduces complexity of bug descriptions

Abstract

Debugging complex systems is a crucial yet time-consuming task. This paper presents the use of automata learning and testing techniques to obtain concise and informative bug descriptions. We introduce the concepts of Failure Explanations (FE), Eventual Failure Explanations (EFE), and Early Detection (ED) to provide meaningful summaries of failing behavior patterns. By factoring out irrelevant information and focusing on essential test patterns, our approach aims to enhance bug detection and understanding. We evaluate our methods using various test patterns and real-world benchmarks, demonstrating their effectiveness in producing compact and informative bug descriptions.