DirectDebug: A software package for the automated testing and debugging of feature models

Le, Viet Man; Felfernig, Alexander; Tran, Thi Ngoc Trang; Atas, Müslüm; Uta, Mathias; Benavides, David; Galindo, José

doi:10.1016/j.simpa.2021.100085

Cited by 5 publications

(4 citation statements)

References 11 publications

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“…Performance optimization can become an issue specifically in the context of constraint-based recommendation and related conflict detection and diagnosis problem solving (Reiter, 1987;Junker, 2004;Felfernig et al, 2012;Le et al, 2023). Improving the efficiency of constraint solving primarily depends on the ability to select optimal heuristics depending on the given search problem.…”

Section: Search Optimizationmentioning

confidence: 99%

Knowledge-based recommender systems: overview and research directions

Uta,

Felfernig,

et al. 2024

Front. Big Data

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Recommender systems are decision support systems that help users to identify items of relevance from a potentially large set of alternatives. In contrast to the mainstream recommendation approaches of collaborative filtering and content-based filtering, knowledge-based recommenders exploit semantic user preference knowledge, item knowledge, and recommendation knowledge, to identify user-relevant items which is of specific relevance when dealing with complex and high-involvement items. Such recommenders are primarily applied in scenarios where users specify (and revise) their preferences, and related recommendations are determined on the basis of constraints or attribute-level similarity metrics. In this article, we provide an overview of the existing state-of-the-art in knowledge-based recommender systems. Different related recommendation techniques are explained on the basis of a working example from the domain of survey software services. On the basis of our analysis, we outline different directions for future research.

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Section: Search Optimizationmentioning

confidence: 99%

Knowledge-based recommender systems: overview and research directions

Uta,

Felfernig,

et al. 2024

Front. Big Data

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“…DIRECTDEBUG requires four inputs: (1) δ that avoids redundant consistency checks, ( 2 For further details regarding the algorithm, we refer to [15,16].…”

Section: Feature Model Debuggingmentioning

confidence: 99%

FMTESTING: A FEATUREIDE Plug-in for Automated Feature Model Analysis and Diagnosis

Burgstaller,

Le,

Tran

et al. 2023

Frontiers in Artificial Intelligence and Applications

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The increasing size and complexity of feature models (FMs) can trigger anomalies or faults, challenging stakeholders in keeping FMs consistent with the domain requirements. Existing quality assurance tools do not provide advanced techniques to point out possibilities to adapt an FM for consistency recovery. In this paper, we present FMTESTING, which is a plug-in for FEATUREIDE, an ECLIPSE-based IDE supporting different phases of feature-oriented software development. FMTESTING is capable of automatically generating property-based test cases based on six different types of FM analysis operations. Furthermore, for violated test cases, diagnoses are provided to precisely indicate faulty FM elements (constraints) that should be adapted to restore consistency. Our tool provides user interfaces inside FEATUREIDE to ensure convenient use, even for users who are not domain experts.

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“…Choco-solver is used by the industry to solve many real-world problems, such as cryptanalysis (Delaune et al, 2021), construction planning (Cañizares et al, 2022), automated testing and debugging (Le et al, 2021), scheduling (Lorca et al, 2016), level design (Smith et al, 2011), placement service (Ait Salaht et al, 2019) and many others. In the Railway industry, Choco-solver is used to optimize the rail traffic of French train stations, on a daily basis.…”

Section: Industrial Usementioning

confidence: 99%

Choco-solver: A Java library for constraint programming

Prud’Homme¹,

Fages²

2022

JOSS

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Constraint Programming (CP) is a powerful programming paradigm for solving combinatorial search problems (Rossi et al., 2006). CP is at the intersection of artificial intelligence, computer science, operations research, and many other fields. One of the strengths of the paradigm is the wide variety of constraints it offers. CP is both a rich declarative language for describing combinatorial problems and a set of algorithms and techniques for solving them automatically.Choco-solver is Java library for constraint programming which was created in the early 2000s. Since then, the library has evolved a great deal, but ease of use has always been a guiding principle in its development. The Choco-solver API is designed to reduce entry points to a minimum and thus simplifies modelling for users. The wide variety of constraints available allows the user to describe his problem as naturally as possible. The black-box approach to solving allows everyone to focus on modelling. However, Choco-solver is also open and modifiable. The implementation of new constraints (Ouellet & Quimper, 2022) or strategies for exploring the search space (Fages & Prud'Homme, 2017;Li et al., 2021) is therefore possible.As a result, Choco-solver is used by the academics for teaching and research, on the other hand it is used by the industry to solve real-world problems.

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DirectDebug: A software package for the automated testing and debugging of feature models

Cited by 5 publications

References 11 publications

Knowledge-based recommender systems: overview and research directions

Knowledge-based recommender systems: overview and research directions

FMTESTING: A FEATUREIDE Plug-in for Automated Feature Model Analysis and Diagnosis

Choco-solver: A Java library for constraint programming

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