Efficient Sequential Model-Based Fault-Localization with Partial Diagnoses
Authors: Kostyantyn Shchekotykhin, Thomas Schmitz, Dietmar Jannach
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | An experimental evaluation on different benchmark problems shows that our sequential diagnosis approach needs considerably less computation time when compared with an existing domain-independent approach. |
| Researcher Affiliation | Academia | Kostyantyn Shchekotykhin,1 Thomas Schmitz,2 and Dietmar Jannach2 1Alpen-Adria University Klagenfurt, Austria e-mail: kostyantyn.shchekotykhin@aau.at 2TU Dortmund, Germany e-mail: {firstname.lastname}@tu-dortmund.de |
| Pseudocode | Yes | Algorithm 1 summarizes our approach, which in contrast to previous works can operate on the basis of partial diagnoses. |
| Open Source Code | No | The paper states that 'The algorithms were implemented in Java' but does not provide any link, repository, or explicit statement of code availability for the methodology described. |
| Open Datasets | Yes | We evaluated our method on two sets of benchmark problems: (a) the ontologies of the OAEI Conference benchmark as used in [Shchekotykhin et al., 2014], (b) the systems of the DX Competition (DXC) 2011 Synthetic Track. |
| Dataset Splits | No | The paper describes running experiments multiple times with randomly selected preferred diagnoses or scenarios, but it does not specify explicit training, validation, and testing dataset splits. |
| Hardware Specification | No | All tests were performed on a modern laptop computer. This description is too vague and does not provide specific hardware details (e.g., CPU, GPU, memory). |
| Software Dependencies | No | The algorithms were implemented in Java. Choco was used as a constraint solver and Hermi T as Description Logic reasoner. However, specific version numbers for Java, Choco, or Hermi T are not provided. |
| Experiment Setup | Yes | For both strategies, we set the number of diagnoses n that are used to determine the optimal query to 9 as done in [Shchekotykhin et al., 2014], and used the best-performing Entropy strategy for query selection (GETQUERY). |