Automatic Generation of Text Descriptive Comments for Code Blocks
Authors: Yuding Liang, Kenny Zhu
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our evaluation comes in two parts. In the first part, we evaluate Code-RNN model s ability to classify different source code blocks into k known categories. In the second part, we show the effectiveness of our comment generation model by comparing with several state-of-the-art approaches in both quantitative and qualitative assessments. the overall framework achieves remarkable accuracy (Rouge-2 value) in the task of generating descriptive comments for Java methods, compared to state-of-the-art approaches. |
| Researcher Affiliation | Academia | Yuding Liang, Kenny Q. Zhu liangyuding@sjtu.edu.cn, kzhu@cs.sjtu.edu.cn Department of Computer Science and Engineering Shanghai Jiao Tong University 800 Dongchuan Road, Shanghai, China 200240 |
| Pseudocode | No | The paper includes equations and diagrams describing the models (Code-RNN, Code-GRU) but does not contain explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | The source code of our approach as well as all data set is available at https://adapt.seiee.sjtu.edu.cn/CodeComment/. |
| Open Datasets | Yes | The source code of our approach as well as all data set is available at https://adapt.seiee.sjtu.edu.cn/CodeComment/. Our data set comes from the Google Code Jam contest (2008 – 2016), which there are multiple problems... We use the solutions (10,724 methods) of 6 problems as training set and the solutions (30 methods) of the other 6 problems as the test set. For one project, we separate the commented methods into three parts: training set, validation set and test set. |
| Dataset Splits | Yes | For one project, we separate the commented methods into three parts: training set, validation set and test set. We tune these two parameters on the validation set to determine which values to use. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions tools like 'Java Parser' and discusses baselines like 'Moses' and 'CODE-NN', but does not provide specific version numbers for the key software dependencies or libraries (e.g., Python, deep learning frameworks like TensorFlow/PyTorch) used to replicate the experiments. |
| Experiment Setup | Yes | Seq2Seq, Basic RNN and our model run 800 epochs during training time. We tune these two parameters on the validation set to determine which values to use. Our tuning ranges are: beam size: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] weight for the length penalty: [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0] |