<<<<<<< HEAD

从命令行创建一个新的仓库

touch README.md
git init
git add README.md
git commit -m "first commit"
git remote add origin https://git.trustie.net/MingzhuShen/CoDas4CG.git
git push -u origin master

从命令行推送已经创建的仓库

git remote add origin https://git.trustie.net/MingzhuShen/CoDas4CG.git
git push -u origin master

=======

CoDas4CG

Contests based Dataset for Code Generation

If you are using the dataset, please cite the following paper: H. Liu, M. Shen, J. Zhu, N. Niu, G. Li and L. Zhang, “Deep Learning Based Program Generation from Requirements Text: Are We There Yet?,” in IEEE Transactions on Software Engineering, doi: 10.1109/TSE.2020.3018481. Available at: https://ieeexplore.ieee.org/document/9173704

There are Seven folders: AssistantTools , DatasetInSQL, Dataset, TestCases, Tools, CodeOfApproaches and GeneratedPrograms.

/Dataset contains the programming tasks and their corresponding implementations in different programming languages. Each subfolder under /Datset corresponds to a single programming task. Notably, we do not include the commercial script to crawl data.

/TestCases contains the test cases for the programming tasks in folder /Dataset. The names of the subfolders in /TestCases specify the names of the programming tasks. According to such names you may find the corresponding tasks under /Dataset. Notably, such test cases are collected from programming contest websites, and we do not leverage test case generation tools.

/Tools contains the source code of our tool kit.

/GeneratedPrograms contains the programs generated by each approach.

/CodeOfApproaches Implementation of evaluated approaches.

/DatasetInSQL: This folder is composed of a database that contains the whole dataset (Python programs only).

In case your approach is specially designed for Python, this database is strongly suggested for usage (compated to the /Dataset folder).

How to use the database:

Retrieving original data (without preprocessing)

1. RetrieveTasks(): Returns the description (requirements) of all tasks, each requirement is a text string

   SQL: select question from question

2. RetrieveTask(ID): Return the task description of the specified ID

   SQL: select question from question where numId = ID

3. RetreiveImplementations()：Return all codes, and each code corresponds to a python file.

   SQL：select code from code

4. RetreiveImplementations（ID）：Return all codes corresponding to the specified topic id, and each code corresponds to a python file. s

   SQL: select code from code where numId = ID

Retrieving processed data (preprocessing includes word segmentation and standarlization, et al.)

1. RetrieveTasks(): Returns the description (requirements) of all tasks, each requirement is a text string

   SQL: select question from process_question

2. RetrieveTask(ID): Return the task description of the specified ID

   SQL: select question from process_question where numId = ID

3. RetreiveImplementations()：Return all codes, and each code corresponds to a python file.

   SQL：select code from process_code

4. RetreiveImplementations（ID）：Return all codes corresponding to the specified topic id, and each code corresponds to a python file.

   SQL: select code from process_code where numId = ID

5. RetreiveTestCasess（ID）：Returns the test case corresponding to the specified question id.

   SQL:select input,output from testcase where numId= ID

/AssistantTools: This folder contains tools to calculate the BLEU, and to detect compilation errors in generated programs.

1.ComputeBLEU（pred, refer）: Retrun BLEU between the generated code pred and the reference code refer

2.ComputeBLEU2（pred,refers）:Retrun BLEU of code pred according to a series of refer

3.hasCompilerErrors（File name）:Check whether the code has static and dynamic compilation errors

4.PreProcessALL(File requirements， File implements): Preprocess related requirements and programs

5.PreProcessReq(File requirements， File implements): Preprocess requirements (tasks)

6.PreProcessImp(File requirements， File implements): Preprocess the program

first commit