Minimizing test-point allocation to improve diagnosability in business process models

Borrego, Diana; Gómez-López, María Teresa; Gasca, Rafael M.

doi:10.1016/j.jss.2013.05.105

Cited by 3 publications

(1 citation statement)

References 26 publications

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“…Frequently, probability and cost are included in sorting the list of possible causes, although the number of possibilities can be extremely high. One option for the reduction of the number of candidates involves improving the observation of the system by developing a set of new readings of signals in the system [3], [4]. Troubleshooting is the reasoning process for the determination of what part of a system is causing a malfunction in accordance with a set of actions that guide the diagnoser to isolate the origin of misbehaviour.…”

Section: Introductionmentioning

confidence: 99%

Self-Adaptative Troubleshooting for to Guide Resolution of Malfunctions in Aircraft Manufacturing

et al. 2021

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The increasing complexity of systems and the heterogeneous origin of the possible malfunctions bring about the necessity of redefining the troubleshooting processes. Troubleshooting comprises the set of steps for the systematic analysis of the symptoms after the detection of a malfunction. The complexity of certain systems, such as aircraft, means the origin of that malfunction can be any of several reasons, where diagnosis techniques support engineers in determining the reason for the unexpected behaviour. However, derived from the high number of components involved in an aircraft, the list of possible fault origins can be extremely long, and the analysis of every element on the list, until the element responsible is found, can be very time-consuming and error-prone. As an alternative, certain input/output signals can be read to prevent the substitution of a correctly functioning component, by validating its behaviour in an indirect way. In order to optimise the actions to perform, we have identified the relevant parts of the model to propose a troubleshooting process to ascertain the signals to read and the components to substitute, while striving to minimise the action cost in accordance with a combination of structural analysis, the probability of malfunction associated to the components, and the cost associated to each extra signal read and component substituted. The proposal has been validated in a system taken from a real scenario obtained in collaboration with the Airbus Defence and Space company. A statistical analysis of the degree of improvement of the troubleshooting process has also been included. INDEX TERMS Decision-making process, Model-based Diagnosis, multi-objective function, Troubleshooting.

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Section: Introductionmentioning

confidence: 99%

Self-Adaptative Troubleshooting for to Guide Resolution of Malfunctions in Aircraft Manufacturing

et al. 2021

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A Genetic Algorithm for Automatic Business Process Test Case Selection

Böhmer

Rinderle-Ma

2015

Lecture Notes in Computer Science

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Automatic Business Process Test Case Selection: Coverage Metrics, Algorithms, and Performance Optimizations

Böhmer

Rinderle-Ma

2016

Int. J. Coop. Info. Syst.

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Business processes describe and implement the business logic of companies, control human interaction, and invoke heterogeneous services during runtime. Therefore, ensuring the correct execution of processes is crucial. Existing work is addressing this challenge through process verification. However, the highly dynamic aspects of the current processes and the deep integration and frequent invocation of third party services limit the use of static verification approaches. Today, one frequently utilized approach to address this limitation is to apply process tests. However, the complexity of process models is steadily increasing. So, more and more test cases are required to assure process model correctness and stability during design and maintenance. But executing hundreds or even thousands of process model test cases lead to excessive test suite execution times and, therefore, high costs. Hence, this paper presents novel coverage metrics along with a genetic test case selection algorithm. Both enable the incorporation of user-driven test case selection requirements and the integration of different knowledge sources. In addition, techniques for test case selection computation performance optimization are provided and evaluated. The effectiveness of the presented genetic test case selection algorithm is evaluated against five alternative test case selection algorithms.

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Minimizing test-point allocation to improve diagnosability in business process models

Cited by 3 publications

References 26 publications

Self-Adaptative Troubleshooting for to Guide Resolution of Malfunctions in Aircraft Manufacturing

Self-Adaptative Troubleshooting for to Guide Resolution of Malfunctions in Aircraft Manufacturing

A Genetic Algorithm for Automatic Business Process Test Case Selection

Automatic Business Process Test Case Selection: Coverage Metrics, Algorithms, and Performance Optimizations

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