Comparing model checkers for timed UML activity diagrams

Daw, Zamira; Cleaveland, Rance

doi:10.1016/j.scico.2015.05.008

Cited by 15 publications

(10 citation statements)

References 22 publications

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“…The equation to calculate Fitness is shown in Eq. (7). The Fitness value is obtained by dividing the number of variation in the activity records that is shown on model with the number of variation in activity records.…”

Section: Quality Aspectsmentioning

confidence: 99%

“…The equation to calculate Precision is shown in Eq. (7). The Precision value is obtained by dividing the number of tp in a model with the addition of tp and tn in a model.…”

Section: Quality Aspectsmentioning

confidence: 99%

“…The deterministic model depicts all activity records. There are several deterministic process models, such as Data Flow Diagram (DFD) [3], Business Process Modeling Notation (BPMN) [4,5], Activity Diagram [6,7], and Linear Temporal Logic (LTL) [8,9]. The probabilistic model removes relationships of activity that have small occurrences in its model.…”

Section: Introductionmentioning

confidence: 99%

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Heuristic Linear Temporal Logic Pattern Algorithm in Business Process Model

Sungkono¹,

Rochmah²,

Sarno³

2019

IJIES

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Process discovery obtains a process model of activity records. There are two representations of process model, i.e. a probabilistic model and a deterministic model. A deterministic model takes all of activity records to depict a process model, however, the probabilistic model chooses several activity records that satisfy a threshold. Determination of the right threshold leads the emergence of many discovery algorithms of probabilistic models, such as Heuristic Miner, Fodina, Modified Heuristic Miner, and Modified Time-Based Heuristic Miners. Those algorithms determine a threshold based on users or an average of probabilities of activity records, so the quality of the model depends on user proficiency or frequent activities. This paper proposes a new algorithm of probabilistic model discovery, i.e. Heuristic Linear Temporal Logic (HLTL), which determines the threshold based on four quality aspects, i.e. Fitness, Precision, Generalization, and Simplicity. HLTL utilizes Linear Temporal Logic to create a formal representation of process model and store the weight of relationships used for the threshold formation. The result shows that the process model constructing by HLTL has better quality aspects than the process model constructing by Modified Heuristic Miners and Modified Time-Based Heuristic Miners. The generalization value of HLTL is 0.8422 and the generalization value of Modified Heuristic Miner and Modified Time-Based Heuristic Miners are 0.8421.

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Section: Quality Aspectsmentioning

confidence: 99%

“…The equation to calculate Precision is shown in Eq. (7). The Precision value is obtained by dividing the number of tp in a model with the addition of tp and tn in a model.…”

Section: Quality Aspectsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Heuristic Linear Temporal Logic Pattern Algorithm in Business Process Model

Sungkono¹,

Rochmah²,

Sarno³

2019

IJIES

View full text Add to dashboard Cite

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“…Most of the works make use of UML models, often enriched with OCL constraints, and only a part of them is fully supported by a tool implementing the model transformations and the verification process. A number of other works have used a model-driven approach for the formal verification of behavioral models (see, e.g., [7,10]), without addressing the specificities of DIAs. To the best of our knowledge, few works try to address the problem of the verification of DIAs, none of them adopting the MDE approach.…”

Section: Related Workmentioning

confidence: 99%

Formal verification of storm topologies through D-VerT

Marconi

Bersani

Rossi

2017

Proceedings of the Symposium on Applied Computing

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Data-intensive applications (DIAs) based on so-called Big Data technologies are nowadays a common solution adopted by IT companies to face their growing computational needs. The need for highly reliable applications able to handle huge amounts of data and the availability of infrastructures for distributed computing rapidly led industries to develop frameworks for streaming and big-data processing, like Apache Storm and Spark. The definition of methodologies and principles for good software design is, therefore, fundamental to support the development of DIAs. This paper presents an approach for non-functional analysis of DIAs through DVerT, a tool for the architectural assessment of Storm applications. The verification is based on a translation of Storm topologies into the CLTLoc metric temporal logic. It allows the designer of a Storm application to check for the existence of components that cannot process their workload in a timely manner, typically due to an incorrect design of the topology. CCS Concepts•Theory of computation → Verification by model checking; •Software and its engineering → Model-driven software engineering;

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“…verification of behavioral models (see, e.g., [9,12]), without addressing the specificities of DIAs. To the best of our knowledge, few works try to address the problem of the verification of DIAs, none of them adopting the MDE approach.…”

Section: Related Workmentioning

confidence: 99%