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].
Anticipatory Troubleshooting
Authors: Netantel Hasidi, Roni Stern, Meir Kalech, Shulamit Reches
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To evaluate the proposed algorithms, we performed two sets of experiments: one-shot experiments, in which a single TP is solved (evaluates the method in Section 4.1), and long-term experiments, in which troubleshooting costs are accumulated (evaluates the method in Section 5.2). |
| Researcher Affiliation | Academia | Ben Gurion University of the Negev Be er Sheva, Israel EMAIL Shulamit Reches Jerusalem College of Technology Jerusalem, Israel EMAIL |
| Pseudocode | No | The paper describes algorithms and decision rules (DR1) conceptually but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | The second system, denoted S2, is the CAR DIAGNOSIS 2 network from the library of benchmark BN made available by Norsys (www.norsys.com/netlib/Car Diagnosis2.dnet). |
| Dataset Splits | No | The paper describes the generation of random TPs for experiments but does not provide specific details on train/validation/test splits, percentages, or sample counts for the data used in the evaluation. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments, such as CPU/GPU models, memory, or cloud instance types. |
| Software Dependencies | No | The paper mentions using Bayesian networks and an MBD algorithm, but it does not list any specific software components or libraries with their version numbers (e.g., Python, PyTorch, CPLEX version). |
| Experiment Setup | Yes | We set the age of each component to be Ageinit plus a random number between zero and Agediff, where Ageinit is a constant set arbitrarily to 0.3 and Agediff is a parameter we varied in our experiments. ... The punish-factor parameter P controls the difference between the after-fix and the regular survival function. ... We experimented with a range of values for these two parameters and studied their impact on the long-term costs. ... All results are averaged over 50 instances. |