# Conjunctive Query Based Constraint Solving For Feature Model Configuration

arXiv: https://arxiv.org/abs/2304.13422 · HTML: https://mlsanity.com/paper/2304.13422/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 25 references that are in the map.
- First posted on arXiv 26 Apr 2023.

## Builds on (most cited first, 3 of 3)

- [An Efficient Diagnosis Algorithm for Inconsistent Constraint Sets](https://mlsanity.com/paper/2102.09005/) (2021)
- [DirectDebug: Automated Testing and Debugging of Feature Models](https://mlsanity.com/paper/2102.05949/) (2021)
- [An Overview of Recommender Systems and Machine Learning in Feature Modeling and Configuration](https://mlsanity.com/paper/2102.06634/) (2021)

## Abstract (arXiv)

Feature model configuration can be supported on the basis of various types of reasoning approaches. Examples thereof are SAT solving, constraint solving, and answer set programming (ASP). Using these approaches requires technical expertise of how to define and solve the underlying configuration problem. In this paper, we show how to apply conjunctive queries typically supported by today's relational database systems to solve constraint satisfaction problems (CSP) and -- more specifically -- feature model configuration tasks. This approach allows the application of a wide-spread database technology to solve configuration tasks and also allows for new algorithmic approaches when it comes to the identification and resolution of inconsistencies.