Author name cluster

Viet-Man Le

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4 papers

2 author rows

AAAI Conference 2026 Conference Paper

Robust Lazy Conflict Detection via Multi-Conflict Extraction and Genetic Diversity Control

Viet-Man Le
Lukas André Feldgrill
Alexander Felfernig

Detecting minimal conflict sets is essential for providing meaningful feedback in knowledge-based configuration. While lazy conflict detection addresses runtime efficiency by predetermining conflict sets offline using a genetic algorithm, it suffers from low conflict coverage, stagnation, and instability. We propose a robust enhancement that integrates multi-conflict extraction and genetic diversity control to overcome these limitations. Our method extends conflict discovery per evaluation and introduces three diversity mechanisms: full population reproduction, weighted genetic operators, and adaptive extinction. Empirical evaluations on five real-world configuration knowledge bases show that our approach recovers up to 85% of conflict sets, reduces solver calls by up to 73%, and achieves higher result stability. These improvements demonstrate the scalability and reliability of enhanced lazy conflict detection for interactive configuration systems.

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AAAI Conference 2024 Conference Paper

INFORMEDQX: Informed Conflict Detection for Over-Constrained Problems

Viet-Man Le
Alexander Felfernig
Thi Ngoc Trang Tran
Mathias Uta

Conflict detection is relevant in various application scenarios, ranging from interactive decision-making to the diagnosis of faulty knowledge bases. Conflicts can be regarded as sets of constraints that cause an inconsistency. In many scenarios (e.g., constraint-based configuration), conflicts are repeatedly determined for the same or similar sets of constraints. This misses out on the valuable opportunity for leveraging knowledge reuse and related potential performance improvements, which are extremely important, specifically interactive constraint-based applications. In this paper, we show how to integrate knowledge reuse concepts into non-instructive conflict detection. We introduce the InformedQX algorithm, which is a reuse-aware variant of QuickXPlain. The results of a related performance analysis with the Linux-2.6.3.33 configuration knowledge base show significant improvements in terms of runtime performance compared to QuickXPlain.

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AAAI Conference 2023 Conference Paper

FASTDIAGP: An Algorithm for Parallelized Direct Diagnosis

Viet-Man Le
Cristian Vidal Silva
Alexander Felfernig
David Benavides
José Galindo
Thi Ngoc Trang Tran

Constraint-based applications attempt to identify a solution that meets all defined user requirements. If the requirements are inconsistent with the underlying constraint set, algorithms that compute diagnoses for inconsistent constraints should be implemented to help users resolve the “no solution could be found” dilemma. FastDiag is a typical direct diagnosis algorithm that supports diagnosis calculation without pre-determining conflicts. However, this approach faces runtime performance issues, especially when analyzing complex and large-scale knowledge bases. In this paper, we propose a novel algorithm, so-called FastDiagP, which is based on the idea of speculative programming. This algorithm extends FastDiag by integrating a parallelization mechanism that anticipates and pre-calculates consistency checks requested by FastDiag. This mechanism helps to provide consistency checks with fast answers and boosts the algorithm’s runtime performance. The performance improvements of our proposed algorithm have been shown through empirical results using the Linux-2.6.3.33 configuration knowledge base.

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ECAI Conference 2023 Conference Paper

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

Viet-Man Le
Thi Ngoc Trang Tran
Alexander Felfernig

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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