Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Distributed Spectrum-Based Fault Localization
Authors: Avraham Natan, Roni Stern, Meir Kalech
AAAI 2023 | Venue PDF | 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 EMAIL, EMAIL, EMAIL |
| 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. |