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].
An Iterative Approach to Synthesize Data Transformation Programs
Authors: Bo Wu, Craig A. Knoblock
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluated the approach with a variety of transformation scenarios. The results show that the approach significantly reduces the time used to generate the transformation programs, especially in complicated scenarios. |
| Researcher Affiliation | Academia | Bo Wu Computer Science Department University of Southern California Los Angeles, California EMAIL Craig A. Knoblock Information Science Institute University of Southern California Los Angeles, California EMAIL |
| Pseudocode | Yes | Algorithm 1: Program Adaptation |
| Open Source Code | Yes | Data and code are available at http://bit.ly/1Gt Z4Gc. The code is also available as the data transformation tool of Karma (http://www.isi.edu/integration/karma). |
| Open Datasets | Yes | Data and code are available at http://bit.ly/1Gt Z4Gc. |
| Dataset Splits | No | The paper mentions providing examples iteratively until programs transform all records correctly but does not specify any training/validation/test splits. |
| Hardware Specification | Yes | We performed the experiments on a laptop with 8G RAM and 2.66GHz CPU. |
| Software Dependencies | No | The paper mentions using Karma, Gulwani's approach, Metagol DF, and Flashfill, but does not specify version numbers for any key software dependencies. |
| Experiment Setup | No | The paper describes the comparison methodology and how time was measured for program generation, but it does not provide specific hyperparameters or system-level training settings like learning rates, batch sizes, or optimizer configurations, as might be found in typical machine learning setups. |