research-article

A genetic algorithm for goal-conflict identification

Authors:

Renzo Degiovanni,

Facundo Molina,

Nazareno AguirreAuthors Info & Claims

ASE '18: Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering

Pages 520 - 531

https://doi.org/10.1145/3238147.3238220

Published: 03 September 2018 Publication History

Abstract

Goal-conflict analysis has been widely used as an abstraction for risk analysis in goal-oriented requirements engineering approaches. In this context, where the expected behaviour of the system-to-be is captured in terms of domain properties and goals, identifying combinations of circumstances that may make the goals diverge, i.e., not to be satisfied as a whole, is of most importance.

Various approaches have been proposed in order to automatically identify boundary conditions, i.e., formulas capturing goal-divergent situations, but they either apply only to some specific goal expressions, or are affected by scalability issues that make them applicable only to relatively small specifications. In this paper, we present a novel approach to automatically identify boundary conditions, using evolutionary computation. More precisely, we develop a genetic algorithm that, given the LTL formulation of the domain properties and the goals, it searches for formulas that capture divergences in the specification. We exploit a modern LTL satisfiability checker to successfully guide our genetic algorithm to the solutions. We assess our technique on a set of case studies, and show that our genetic algorithm is able to find boundary conditions that cannot be generated by related approaches, and is able to efficiently scale to LTL specifications that other approaches are unable to deal with.

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Cited By

Fazelnia MMirakhorli MBagheri HFilkov VRay BZhou M(2024)Translation Titans, Reasoning Challenges: Satisfiability-Aided Language Models for Detecting Conflicting RequirementsProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695302(2294-2298)Online publication date: 27-Oct-2024
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Luo WLiang PQiu JChen PWan HDu JFang WChristakis MPradel M(2024)Learning to Check LTL Satisfiability and to Generate Traces via Differentiable Trace CheckingProceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3650212.3680337(996-1008)Online publication date: 11-Sep-2024
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Brizzio MRoychoudhury APaiva AAbreu RStorey M(2024)Resolving Goal-Conflicts and Scaling Synthesis through Mode-Based DecompositionProceedings of the 2024 IEEE/ACM 46th International Conference on Software Engineering: Companion Proceedings10.1145/3639478.3639801(207-211)Online publication date: 14-Apr-2024
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Index Terms

A genetic algorithm for goal-conflict identification
1. Software and its engineering
  1. Software creation and management
2. Theory of computation
  1. Logic
    1. Modal and temporal logics

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cover image ACM Conferences

ASE '18: Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering

September 2018

955 pages

ISBN:9781450359375

DOI:10.1145/3238147

General Chair:
Marianne Huchard
University of Montpellier, France
,
Program Chairs:
Christian Kästner
Carnegie Mellon University, USA
,
Gordon Fraser
University of Passau, Germany

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Published: 03 September 2018

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ASE '18: 33rd ACM/IEEE International Conference on Automated Software Engineering

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Cited By

Fazelnia MMirakhorli MBagheri HFilkov VRay BZhou M(2024)Translation Titans, Reasoning Challenges: Satisfiability-Aided Language Models for Detecting Conflicting RequirementsProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695302(2294-2298)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695302
Luo WLiang PQiu JChen PWan HDu JFang WChristakis MPradel M(2024)Learning to Check LTL Satisfiability and to Generate Traces via Differentiable Trace CheckingProceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3650212.3680337(996-1008)Online publication date: 11-Sep-2024
https://dl.acm.org/doi/10.1145/3650212.3680337
Brizzio MRoychoudhury APaiva AAbreu RStorey M(2024)Resolving Goal-Conflicts and Scaling Synthesis through Mode-Based DecompositionProceedings of the 2024 IEEE/ACM 46th International Conference on Software Engineering: Companion Proceedings10.1145/3639478.3639801(207-211)Online publication date: 14-Apr-2024
https://dl.acm.org/doi/10.1145/3639478.3639801
Carvalho LDegiovanni RCordy MAguirre NLe Traon YPapadakis MRoychoudhury APaiva AAbreu RStorey M(2024)SpecBCFuzz: Fuzzing LTL Solvers with Boundary ConditionsProceedings of the IEEE/ACM 46th International Conference on Software Engineering10.1145/3597503.3639087(1-13)Online publication date: 20-May-2024
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Chen XJin ZZhang MMallet FLiu XZhou T(2024)A Scalable Approach to Detecting Safety Requirements Inconsistencies for Railway SystemsIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.341886425:8(8375-8386)Online publication date: Aug-2024
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Brizzio MCordy MPapadakis MSánchez CAguirre NDegiovanni RSilva SPaquete L(2023)Automated Repair of Unrealisable LTL Specifications Guided by Model CountingProceedings of the Genetic and Evolutionary Computation Conference10.1145/3583131.3590454(1499-1507)Online publication date: 15-Jul-2023
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Luo WWan HZhang DDu JSu H(2023)Checking LTL Satisfiability via End-to-end Learning37th IEEE/ACM International Conference on Automated Software Engineering10.1145/3551349.3561163(1-13)Online publication date: 5-Jan-2023
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Luo WZheng YYe RWan HDu JLiang PChen P(2023)SAT-Verifiable LTL Satisfiability Checking via Graph Representation Learning2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE)10.1109/ASE56229.2023.00173(1761-1765)Online publication date: 11-Sep-2023
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Niu TXiao SZhang XLi JHuang YShi J(2023)Computing minimal unsatisfiable core for LTL over finite tracesJournal of Logic and Computation10.1093/logcom/exad049Online publication date: 4-Aug-2023
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