Automata Models for Effective Bug Description

2025-07-15

AI Summary

This paper addresses the challenges of verbose, low-readability defect behavior descriptions and inefficient diagnosis in complex systems. We propose a defect pattern induction method integrating automata learning with formal testing. Our approach collects failing execution traces via black-box testing, applies automata learning to extract salient failure sequences, and introduces three abstract models—“failure explanation,” “final failure explanation,” and “early detection”—to enable precise behavioral generalization and proactive defect identification. Unlike conventional log analysis or full-path backtracking, our method automatically filters out irrelevant behaviors, yielding compact (average compression rate >68%), accurate, and causally interpretable defect characterizations. Evaluated on real-world benchmarks including Redis and SQLite, the method improves defect localization efficiency by 2.3× and achieves a diagnosis accuracy of 91.4%, significantly enhancing debugging comprehensibility and automation support.

Technology Category

• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Search and Optimization: Learning to Search
• Machine Learning: Evaluation and Analysis

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

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.

Problem

• Automata models for concise bug descriptions
• Learning techniques to identify failure patterns
• Enhancing bug detection by filtering irrelevant data

Innovation

• Automata learning for concise bug descriptions
• Failure Explanations to summarize behavior patterns
• Factoring out irrelevant test pattern information