Lifts

Fortz, Sophie

doi:10.1145/3461002.3473066

Cited by 6 publications

(2 citation statements)

References 59 publications

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“…However, this approach becomes impractical when dealing with a large number of variants. Another approach, instead of considering variants individually, would be to consider learning the VIS in a family-based fashion Fortz (2021Fortz ( , 2023. In both cases, it is essential to relate Angluin's queries and counterexamples to configurations.…”

Section: Motivation: Behaviour-driven Vis Reverse-engineering Via Bla...mentioning

confidence: 99%

VaryMinions: leveraging RNNs to identify variants in variability-intensive systems’ logs

Fortz,

Temple,

Devroey

et al. 2024

Empir Software Eng

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From business processes to course management, variability-intensive software systems (VIS) are now ubiquitous. One can configure these systems’ behaviour by activating options, e.g., to derive variants handling building permits across municipalities or implementing different functionalities (quizzes, forums) for a given course. These customisation facilities allow VIS to support distinct relevant customer requirements while taking advantage of reuse for common parts. Customisation thus allows realising both scope and scale economies. Behavioural differences amongst variants manifest themselves in event logs. To re-engineer this kind of system, one must know which variant(s) have produced which behaviour. Since variant information is barely present in logs, this paper supports this task by employing machine learning techniques to classify behaviours (event sequences) among variants. Specifically, we train Long Short Term Memory (LSTMs) and Gated Recurrent Units (GRUs) recurrent neural networks to relate event sequences with the variants they belong to on six different datasets issued from the configurable process and VIS domains. After having evaluated 20 different architectures of LSTM/GRU, our results demonstrate that it is possible to effectively learn the trace-to-variant mapping with high accuracy (at least $$80\%$$ 80 % and up to $$99\%$$ 99 % ) and at scale, i.e., identifying 50 variants using 5000+ traces for each variant.

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Section: Motivation: Behaviour-driven Vis Reverse-engineering Via Bla...mentioning

confidence: 99%

VaryMinions: leveraging RNNs to identify variants in variability-intensive systems’ logs

Fortz,

Temple,

Devroey

et al. 2024

Empir Software Eng

View full text Add to dashboard Cite

show abstract

“…Variability is a system's capacity to be expanded, altered, customized, or configured for use in unique settings [1]. We can tailor or adapt variability intensive systems (VIS) to particular needs in different domains [2,3]. As Wang et al [4] highlights, variability has become a key in software systems for the steadily increasing demand for highly customizable products.…”

Section: Introductionmentioning

confidence: 99%

Reviewing Automated Analysis of Feature Model Solutions for the Product Configuration

et al. 2022

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Feature models (FMs) appeared more than 30 years ago, and they are valuable tools for modeling the functional variability of systems. The automated analysis of feature models (AAFM) is currently a thriving, motivating, and active research area. The product configuration of FMs is a relevant and helpful operation, a crucial activity overall with large-scale feature models. The minimal conflict detection, the diagnosis of in-conflict configuration, and the product completion of consistent partial configuration are significant operations for obtaining consistent and well-defined products. Overall, configuring products for large-scale variability intensive systems (VIS) asks for efficient automated solutions for minimal conflict, diagnosis, and product configuration. Given the relevance of minimal conflict, diagnosis, and product configuration, and the current application of large-scale configuration and FMs for representing those systems and products, the main goals of this research paper are to establish the fundaments of the product configuration of feature models and systematically review existing solutions for the conflict detection, diagnosis, and product completion in FMs from 2010 to 2019. We can perceive that even though modern computing approaches exist for AAFM operations, no solutions exist for assisting the product configurations before 2020. This article reports that in 2020, new solutions appear regarding applying parallel computing for those goals. This research highlights research opportunities for developing new and more efficient solutions for conflict detection, diagnosis, and product completion of large-scale configurations.

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Efficient construction of family-based behavioral models from adaptively learned models

Tavassoli,

Khosravi

2024

Softw Syst Model

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Lifts

Cited by 6 publications

References 59 publications

VaryMinions: leveraging RNNs to identify variants in variability-intensive systems’ logs

VaryMinions: leveraging RNNs to identify variants in variability-intensive systems’ logs

Reviewing Automated Analysis of Feature Model Solutions for the Product Configuration

Efficient construction of family-based behavioral models from adaptively learned models

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