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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Improving Neural Program Synthesis with Inferred Execution Traces
Authors: Eui Chul Shin, Illia Polosukhin, Dawn Song
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical results show that this modification leads to state-of-the-art results on the Karel [Pattis, 1981] program synthesis task, improving upon Bunel et al. [2018] from 77.12% to 81.3% accuracy. |
| Researcher Affiliation | Collaboration | Richard Shin UC Berkeley EMAIL Illia Polosukhin NEAR Protocol EMAIL Dawn Song UC Berkeley EMAIL |
| Pseudocode | No | No pseudocode or clearly labeled algorithm blocks were found. |
| Open Source Code | No | The paper links to a dataset: "To train and test our models, we used the same dataset as Bunel et al. [2018], from https://bit.ly/karel-dataset." However, there is no explicit statement or link providing the open-source code for the methodology described in the paper. |
| Open Datasets | Yes | To train and test our models, we used the same dataset as Bunel et al. [2018], from https://bit.ly/karel-dataset. |
| Dataset Splits | Yes | The training dataset consists of 1,116,854 entries, and the test dataset contains 2,500 entries. Each entry in the dataset contains a Karel program and 6 input/output pairs which satisfy that program. For training the I/O TRACE model, we used all 6 input/output pairs within each entry for a total of 6,701,124 training traces. For training the TRACE CODE model (and our reimplementation of the I/O CODE model from Bunel et al. [2018]), we randomly sample 5 out of the 6 input/output examples (and corresponding traces) each time we sample an entry from the training data. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments were provided in the paper. |
| Software Dependencies | No | The paper mentions deep learning components and training methods (e.g., "convolutional neural network", "two-layer LSTM decoder", "SGD with gradient clipping", "Adam") but does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | SGD with gradient clipping worked better for training the models than Adam. For all of the evaluations of TRACE CODE we used beam search with size 50. |