Official repository for the paper "GN-Transformer: Fusing AST and Source Code information in Graph Networks".

Related tags

Deep Learning GN-Transformer-AST
Overview

GN-Transformer AST

This is the official repository for the paper "GN-Transformer: Fusing AST and Source Code information in Graph Networks".

Data Preparing

Preprocess the dataset by yourself

The code we used to preprocess the Java and Python datasets are under in ./preprocess, please read README.md in /Java and /Python respectively to see how to preprocess the corpus.

The original corpus we used are from here:

Java corpus: https://github.com/xing-hu/TL-CodeSum

Python corpus: https://github.com/EdinburghNLP/code-docstring-corpus

Directly use our preprocessed dataset

You can directly download our preprocessed dataset:

Java: https://drive.google.com/file/d/1hVJaA2JA377Iz3bstHLIGaffUh_ogVnG/view?usp=sharing

Python: https://drive.google.com/file/d/1lQhczrERskISdBcWeS6VWLwCMpBAh-YF/view?usp=sharing

Or you can run the data_prepare.sh in ./data to prepare the dataset.

Training

Enter the script folders and run the gntransformer.sh, the training and testing will start.

#GPU: gpu device ids

#NAME: name of the model

Java:

cd ./scripts/java

bash gntransformer.sh #GPU #NAME

Python:

cd ./scripts/python

bash gntransformer.sh #GPU #NAME

Examples:

bash gntransformer.sh 0 some_name # one gpu

bash gntransformer.sh 0,1 some_name # two gpus

...

Trained models

You can download our trained models here:

Java: https://drive.google.com/file/d/1vnIuGLBNGU_AHDwL7yZIkoaByWiLKYxb/view?usp=sharing

Python: https://drive.google.com/file/d/1tk3Wc4YpSo_oLKCi6h3Kitvsux3vWFUO/view?usp=sharing

Or directly run download_models.sh in ./models to download the trained models.

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Comments
  • Bump fastjson from 1.2.70 to 1.2.83 in /preprocess/Java/JavaExtractor/JPredict

    Bump fastjson from 1.2.70 to 1.2.83 in /preprocess/Java/JavaExtractor/JPredict

    Bumps fastjson from 1.2.70 to 1.2.83.

    Release notes

    Sourced from fastjson's releases.

    FASTJSON 1.2.83版本发布（安全修复）

    这是一个安全修复版本，修复最近收到在特定场景下可以绕过autoType关闭限制的漏洞，建议fastjson用户尽快采取安全措施保障系统安全。

    安全修复方案 ：https://github.com/alibaba/fastjson/wiki/security_update_20220523

    FASTJSON2已经发布并且提供兼容包，性能更好也更安全，升级指南 https://github.com/alibaba/fastjson2/wiki/fastjson_1_upgrade_cn

    Issues

    1. 安全加固
    2. 修复JDK17下setAccessible报错的问题 #4077

    fastjson 1.2.79版本发布，BUG修复

    这又是一个bug fixed的版本，大家按需升级

    Issues

    1. 修复引入MethodInheritanceComparator导致某些场景序列化报错的问题
    2. 增强JDK 9兼容
    3. 修复JSONArray/JSONObject的equals方法在内部对象map/list相同时不直接返回true的问题

    相关链接

    fastjson 1.2.76版本发布，BUG修复增强兼容

    这又是一个bug fixed的版本，大家按需升级

    Issues

    1. 修复一些直接抛RuntimeException的问题 #3631
    2. parser自动识别gzip bytes #3614
    3. 修复Throwable继承类属性不支持自动类型转换问题 #3217
    4. 修复PrettyFormat情况下引用计算不对的问题 #3672
    5. 修复AutoType不兼容LinkedHashMap的问题
    6. 增强对Enum类型的自定类型转换
    7. 修复deserializeUsing在泛型某些场景不能正常工作的问题 #3693
    8. 提升JSONReader性能，减少小对象创建 #3627
    9. 增强对JSONPath对filter的支持 #3629
    10. JSONPath支持忽略NullValue的选项 #3607
    11. 增强对定制化enum的支持 #3601
    12. 增强对java.time.Instant和org.joda.time.Instant的支持 #3539
    13. 修复Parser某些场景不能识别引用的问题

    相关链接

    ... (truncated)

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  • Bump jackson-databind from 2.9.10.4 to 2.12.6.1 in /preprocess/Java/JavaExtractor/JPredict

    Bump jackson-databind from 2.9.10.4 to 2.12.6.1 in /preprocess/Java/JavaExtractor/JPredict

    Bumps jackson-databind from 2.9.10.4 to 2.12.6.1.

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