The Java2CSP Debugging Tool Utilizing Constraint Solving and Model-Based Diagnosis Principles

Franz Wotawa*, Vlad Andrei Dumitru

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

Abstract

Localizing faults in programs and repairing them is considered a difficult, time-consuming, but necessary activity of software engineering to assure programs fulfilling their expected behavior during operation. In this paper, we introduce the Java2CSP debugging tool implementing the principles of model-based diagnosis for fault localization, which can be accessed over the internet using an ordinary web browser. Java2CSP makes use of a constraint representation of a program together with a failing test case for reporting debugging candidates. The tool supports a non-object-oriented subset of the programming language Java. Java2CSP is not supposed to be used in any production environment. Instead, the tool has been developed for providing a prototypical implementation of a debugger using constraints. We present the underlying foundations behind Java2CSP, discuss some preliminary results, and show how the tool can also be used for test case generation and other applications.

Original languageEnglish
Title of host publicationAdvances and Trends in Artificial Intelligence. Theory and Practices in Artificial Intelligence - 35th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2022, Proceedings
EditorsHamido Fujita, Philippe Fournier-Viger, Moonis Ali, Yinglin Wang
Place of PublicationCham
PublisherSpringer
Pages543-554
Number of pages12
ISBN (Electronic)978-3-031-08530-7
ISBN (Print)9783031085291
DOIs
Publication statusPublished - 2022
Event35th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: IEA/AIE 2022 - Kitakyushu, Japan
Duration: 19 Jul 202222 Jul 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13343 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference35th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems
Abbreviated titleIEA/AIE 2022
Country/TerritoryJapan
CityKitakyushu
Period19/07/2222/07/22

Keywords

  • Application of constraint solving
  • Automated debugging
  • Debugging research web tool

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

Cite this