Demystifying the Combination of Dynamic Slicing and Spectrum-based Fault Localization
Authors: Sofia Reis, Rui Abreu, Marcelo d'Amorim
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This paper reports on a comprehensive study to reassess the effects of combining DS with SFL. With this combination, components that are often involved in failing but seldom in passing test runs could be located and their suspiciousness reduced. Results show that the DS-SFL combination, coined as Tandem-FL, improves the diagnostic accuracy up to 73.7% (13.4% on average). We conducted a comprehensive study involving 260 faults from 5 different programs from the Defects4J benchmark [Just et al., 2014], which is frequently used to evaluate fault localization research. |
| Researcher Affiliation | Academia | Sofia Reis1,2 , Rui Abreu1,2 and Marcelo d Amorim3 1IST, University of Lisbon, Portugal 2INESC-ID, Portugal 3Federal University of Pernambuco, Brazil |
| Pseudocode | No | The workflow of Tandem-FL is described in numbered steps but not in a structured pseudocode or algorithm block. |
| Open Source Code | Yes | A tool1, dubbed Tandem-FL, implementing the DS-SFL combination. 1Tool and dataset available at/through https://github.com/ damorim/lithium-slicer (Accessed July 4, 2019) |
| Open Datasets | Yes | We used subject programs from the Defects4J benchmark [Just et al., 2014] in our evaluation. 1Tool and dataset available at/through https://github.com/ damorim/lithium-slicer (Accessed July 4, 2019) |
| Dataset Splits | No | The paper mentions using test cases from the Defects4J benchmark but does not specify explicit training, validation, or test dataset splits (e.g., percentages or sample counts) for reproducing the experiment. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions tools like 'Mozilla Lithium' and 'SFL diagnosis tool' but does not provide specific version numbers for these or any other software dependencies needed to replicate the experiment. |
| Experiment Setup | Yes | We control the scope of analysis through the variable k, denoting the number of most-suspicious files that will be analyzed... In this study, we used 5 and 10 as the values of k, following the same choice as in previous fault localization studies [Ang et al., 2017]. |