Distributed Spectrum-Based Fault Localization

Authors: Avraham Natan, Roni Stern, Meir Kalech

AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental We analyze these algorithms theoretically and empirically. Our analysis shows that the distributed SFL algorithms we developed output identical diagnoses to centralized SFL while preserving privacy.
Researcher Affiliation Academia Ben-Gurion University of the Negev, Israel avinat123@gmail.com, roni.stern@gmail.com, kalech@bgu.ac.il
Pseudocode Yes Algorithm 1: DSFLA-SINGLE
Open Source Code Yes This causes the components to discover less conflicts and by that less hidden cells in their respective spectra (https://github.com/avi-natan/DDIFMAS).
Open Datasets No The paper states: 'We experimented on samples inspired by the domain of Internet Delay Diagnosis (Stern and Kalech 2014).', and describes generating problems synthetically. However, no specific link, DOI, repository name, or citation with author/year for a publicly available dataset is provided.
Dataset Splits No The paper does not provide explicit training, validation, or test dataset splits, percentages, or sample counts.
Hardware Specification No The paper does not explicitly describe the specific hardware used to run its experiments (e.g., specific GPU/CPU models, memory details).
Software Dependencies No The paper does not provide specific software dependencies, such as library or solver names with version numbers, required to replicate the experiment.
Experiment Setup Yes We generated problems with varying number of components x {6, ..., 12, 13}, faulty components f {1, 2, 3, 4, 5}, fault probability values p {0.1, 0.2, ..., 0.9} and number of runs y {10, 20, ..., 50}. We conducted 30 examples for each combination.