Scalable Model-Based Diagnosis with FastDiag: A Dataset and Parallel Benchmark Framework

Delia Isabel Carrión León, Cristian Vidal-Silva () and Nicolás Márquez ()
Additional contact information
Delia Isabel Carrión León: Facultad de Ciencias e Ingenierías, Universidad Estatal de Milagro, Cdla. Universitaria Dr. Rómulo Minchala Murillo km 1.5 vía Milagro—Virgen de Fátima, Milagro 091050, Guayas, Ecuador
Cristian Vidal-Silva: Facultad de Ingeniería y Negocios, Universidad de Las Américas, Manuel Montt 948, Providencia, Santiago 7500975, Chile
Nicolás Márquez: Escuela de Ingeniería Comercial, Facultad de Economía y Negocios, Universidad Santo Tomás, Talca 3460000, Chile

Data, 2025, vol. 10, issue 9, 1-13

Abstract: FastDiag is a widely used algorithm for model-based diagnosis, computing minimal subsets of constraints whose removal restores consistency in knowledge-based systems. As applications grow in complexity, researchers have proposed parallel extensions such as Java-version FastDiagP and FastDiagP++ to accelerate diagnosis through speculative and multiprocessing strategies. This paper presents a reproducible and extensible framework for evaluating FastDiag and its parallel variants across a benchmark suite of feature models and ontology-like constraints. We analyze each variant in terms of recursion structure, runtime performance, and diagnostic correctness. Tracking mechanisms and structured logs enable the fine-grained comparison of recursive behavior and branching strategies. Technical validation confirms that parallel execution preserves minimality and structural soundness, while benchmark results show runtime improvements of up to 4× with FastDiagP++. The accompanying dataset, available as open source, supports educational use, algorithmic benchmarking, and integration into interactive configuration environments. The framework is primarily intended for reproducible benchmarking and teaching with open-source implementations that facilitate analysis and extension.

Keywords: model-based diagnosis; constraint satisfaction; FastDiag; speculative parallelism; multiprocessing; reproducible benchmarking; knowledge-based configuration; feature models; ontology debugging; Python implementation (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2306-5729/10/9/141/pdf (application/pdf)
https://www.mdpi.com/2306-5729/10/9/141/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jdataj:v:10:y:2025:i:9:p:141-:d:1740916

Access Statistics for this article

Data is currently edited by Ms. Becky Zhang

More articles in Data from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().