MergeXplain: Fast Computation of Multiple Conflicts for Diagnosis

Authors: Kostyantyn Shchekotykhin, Dietmar Jannach, Thomas Schmitz

IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental An empirical evaluation on various benchmark problems shows that our method can lead to a significant reduction of the required diagnosis times.
Researcher Affiliation Academia Alpen-Adria University Klagenfurt, Austria e-mail: kostyantyn.shchekotykhin@aau.at 2TU Dortmund, Germany e-mail: {firstname.lastname}@tu-dortmund.de
Pseudocode Yes Algorithm 1: QUICKXPLAIN(B, C) and Algorithm 2: MERGEXPLAIN(B, C) are provided as pseudo-code blocks.
Open Source Code No The paper states 'We implemented all algorithms in a Java-based MBD framework...' but does not provide a link or explicit statement about the public availability of their source code.
Open Datasets Yes We used the five first systems of the DX Competition (DXC) 2011 Synthetic Track. In addition, we made experiments with a number of CSP problems from the CSP solver competition 2008 and several CSP encodings of real-world spreadsheets. The injection of faults was done in the same way as in [Jannach et al., 2015].
Dataset Splits No The paper does not explicitly specify training, validation, and test splits for its datasets.
Hardware Specification Yes The experiments were conducted on a laptop computer (Intel i7, 8GB RAM).
Software Dependencies No The paper mentions 'a Java-based MBD framework, which uses Choco as an underlying constraint solver' but does not specify version numbers for Java or Choco.
Experiment Setup Yes For the evaluation of MXP we used the most aggressive elimination strategy (2) as described in Section 3.4. The value of the simulated time quadratically increases with the number of constraints to be checked and is capped in the experiments at 10 milliseconds.