Model and Data Engineering

doi:10.1007/978-3-319-23781-7

Lecture Notes in Computer Science

2015

DOI: 10.1007/978-3-319-23781-7

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Model and Data Engineering

Abstract: Abstract. This paper presents a technique to diagnose probabilistic counterexamples that are generated when model checking probabilistic systems against probabilistic properties. In probabilistic model checking (PMC), a counterexample is a set of paths in which a path formula holds, and their cumulative probability mass violates the probability bound. The diagnosis is to repair errors in probabilistic PRISM programs using the probabilistic abduction reasoning on Independent Choice Logic (ICL) programs describi… Show more

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Improving Variabilty Analysis through Scenario-Based Incompatibility Detection

2022

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Software Product Line (SPL) developments include Variability Management (VA) as a core activity aiming at minimizing the inherent complexity in commonality and variability manipulation. Particularly, the (automated) analysis of variability models refers to the activities, methods and techniques involved in the definition, design, and instantiation of variabilities modeled during SPL development. Steps of this analysis are defined as a variability analysis process (VA process), which is focused on assisting variability model designers in avoiding anomalies and/or inconsistencies, and minimizing problems when products are implemented and derived. Previously, we have proposed an approach for analyzing variability models through a well-defined VA process (named SeVaTax). This process includes a comprehensive set of scenarios, which allows a designer to detect (and even correct in some cases) different incompatibilities. In this work, we extend SeVaTax by classifying the scenarios according to their dependencies, and by assessing the use of these scenarios. This assessment introduces two experiments to evaluate accuracy and coverage. The former addresses responses when variability models are analyzed, and the latter the completeness of our process with respect to other proposals. Findings show that a more extensive set of scenarios might improve the possibilities of current practices in variability analysis.

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Improving Variabilty Analysis through Scenario-Based Incompatibility Detection

2022

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Model and Data Engineering

Cited by 1 publication

References 3 publications

Improving Variabilty Analysis through Scenario-Based Incompatibility Detection

Improving Variabilty Analysis through Scenario-Based Incompatibility Detection

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