Pyramid Attention For Source Code Summarization
Authors: Lei Chai, Ming LI
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluated it on two source code summarization benchmarks where it surpasses the prior works and achieves new state-of-the-art results. And ablation studies are conducted to show the efficiency of the proposed method. |
| Researcher Affiliation | Academia | Lei Chai and Ming Li National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China {chail, lim}@lamda.nju.edu.cn |
| Pseudocode | No | The paper describes its methods in text and uses diagrams, but it does not include any clearly labeled 'Pseudocode' or 'Algorithm' blocks or figures. |
| Open Source Code | Yes | Our code and data are available at https://github.com/leichainju/pa-former. |
| Open Datasets | Yes | To demonstrate the effectiveness of the proposed method, we conduct experiments on two widely-used and well-developed java datasets: EMSE-Deep Com2 [11] which is collected from Git Hubs Java repositories and Fun Com3 [14] which has 2 million java methodcomment pairs. 2https://github.com/xing-hu/EMSE-Deep Com 3http://leclair.tech/data/funcom/ |
| Dataset Splits | No | Table 1 provides '#train' and '#test' statistics for the datasets, but there is no explicit mention of a 'validation' dataset split with specific numbers or percentages. |
| Hardware Specification | Yes | All models are trained using NVIDIA Tesla A100 GPUs with a batch size of 64. |
| Software Dependencies | No | The paper mentions using the 'Tree-sitter' tool and a 'Py Torch-based' framework, but it does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | For fair comparisons, all the Transformer-based models use the default Transformer configurations with embedding dimension as 512, feedforward dimension as 2048, head number as 8, and layer number for encoder/decoder as 6 and all RNN-based models use the hidden dimension with 512... All models are trained using NVIDIA Tesla A100 GPUs with a batch size of 64. We train all baselines including our models using Adam W optimizer with a multi_step learning rate scheduler, and set the initial learning rate to 0.0002 and 0.003 for Transformer-based and RNNbased models, respectively. |