Implementing Troubleshooting with Batch Repair

Authors: Roni Stern, Meir Kalech, Hilla Shinitzky

AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental Experimentally, we show the benefit of these algorithms over repairing components one at a time.
Researcher Affiliation Academia Roni Stern, Meir Kalech, Hilla Shinitzky Ben Gurion University of the Negev Be er Sheva, Israel
Pseudocode No The paper describes algorithms conceptually and mathematically but does not include formal pseudocode or algorithm blocks.
Open Source Code No The paper does not provide any statement about releasing source code or links to a code repository.
Open Datasets Yes The standard Boolean circuits we used in our experiments are presented in Table 1. The systems 74XXX (Hansen, Yalcin, and Hayes 1999) are described in the first three rows, and additional three systems of ISCAS-85 (Brglez, Bryan, and Kozminski 1989) are described in the following three rows. Observations were selected randomly from Feldman et al. s (2010) known benchmark.
Dataset Splits No The paper mentions using 'benchmark systems' and 'problem instances' but does not explicitly provide details about training, validation, or test dataset splits in terms of percentages or sample counts.
Hardware Specification No The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running the experiments.
Software Dependencies No The paper mentions using a 'simple MBDE based on exhaustive search' but does not specify any software names with version numbers.
Experiment Setup Yes The component repair cost was set to 5, and we experimented with repair overhead (costrepair) costs of 10, 15, 20, and 25. All batch repair algorithms used a simple MBDE based on exhaustive search to generate diagnoses. Diagnoses were generated in order of increasing cardinality, and halted after either all subset minimal diagnoses were found or a timeout of 15 minutes was reached.