Inconsistency detection methods for statecharts and sequence diagrams: a systematic literature review

During model-based systems engineering or software engineering activities, diagrams representing use cases (sequence diagrams) and diagrams representing object behaviors (state machine diagrams or statecharts) can conflict with each other in what is called an inconsistency. Detecting these inconsistencies is crucial to check if a given specification is realizable through the behavior that was conceived to meet it. This paper provides a systematic literature review of inconsistency detection methods for UML state machine diagrams and sequence diagrams. The selection process is aided by an open-source machine-learning tool, and resulted in the qualitative synthesis of 27 works. The included publications offer methods to tackle the detection of horizontal-semantic behavior inconsistencies.

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Publication Details

Journal
Aplicações Operacionais em Áreas de Defesa
Published
2025-09-23
DOI
https://doi.org/10.55972/spectrum.v26i1.427
Primary Topic
Data Quality and Management
Type
article
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article

Inconsistency detection methods for statecharts and sequence diagrams: a systematic literature review

Emília Villani, Christopher Shneider Cerqueira, Matheus Cogo, Carline Muenchen
Aplicações Operacionais em Áreas de Defesa
Data Quality and Management
article

Inconsistency detection methods for statecharts and sequence diagrams: a systematic literature review

Emília Villani, Christopher Shneider Cerqueira, Matheus Cogo, Carline Muenchen
article en

Abstract

During model-based systems engineering or software engineering activities, diagrams representing use cases (sequence diagrams) and diagrams representing object behaviors (state machine diagrams or statecharts) can conflict with each other in what is called an inconsistency. Detecting these inconsistencies is crucial to check if a given specification is realizable through the behavior that was conceived to meet it. This paper provides a systematic literature review of inconsistency detection methods for UML state machine diagrams and sequence diagrams. The selection process is aided by an open-source machine-learning tool, and resulted in the qualitative synthesis of 27 works. The included publications offer methods to tackle the detection of horizontal-semantic behavior inconsistencies.

Aplicações Operacionais em Áreas de DefesaVol. 26(1)
Instituto Tecnológico de Aeronáutica (BR)
Openalex Percentile: Top 68%
Data Quality and Management
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