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README.md

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+---
+tags:
+- code
+pretty_name: CoCoNuT-Python(2010)
+---
+
+# Dataset Card for CoCoNuT-Python(2010)
+
+## Dataset Description
+
+- **Homepage:** [CoCoNuT training data](https://github.com/lin-tan/CoCoNut-Artifact/releases/tag/training_data_1.0.0)
+- **Repository:** [CoCoNuT repository](https://github.com/lin-tan/CoCoNut-Artifact)
+- **Paper:** [CoCoNuT: Combining Context-Aware Neural Translation Models using Ensemble for Program Repair](https://dl.acm.org/doi/abs/10.1145/3395363.3397369)
+
+### Dataset Summary
+
+Part of the data used to train the models in the "CoCoNuT: Combining Context-Aware Neural Translation Models using Ensemble for Program Repair" paper.
+These datasets contain raw data extracted from GitHub, GitLab, and Bitbucket, and have neither been shuffled nor tokenized.
+The year in the dataset’s name is the cutting year that shows the year of the newest commit in the dataset.
+
+### Languages
+
+- Python
+
+## Dataset Structure
+
+### Data Fields
+
+The dataset consists of 4 columns: `add`, `rem`, `context`, and `meta`.
+These match the original dataset files: `add.txt`, `rem.txt`, `context.txt`, and `meta.txt`.
+
+### Data Instances
+
+There is a mapping between the 4 columns for each instance.
+For example:
+
+5 first rows of `rem` (i.e., the buggy line/hunk):
+
+```
+1 public synchronized StringBuffer append(char ch)
+2 ensureCapacity_unsynchronized(count + 1); value[count++] = ch; return this;
+3 public String substring(int beginIndex, int endIndex)
+4 if (beginIndex < 0 || endIndex > count || beginIndex > endIndex) throw new StringIndexOutOfBoundsException(); if (beginIndex == 0 && endIndex == count) return this; int len = endIndex - beginIndex;  return new String(value, beginIndex + offset, len, (len << 2) >= value.length);
+5 public Object next() {
+```
+
+5 first rows of add (i.e., the fixed line/hunk):
+
+```
+1 public StringBuffer append(Object obj)
+2 return append(obj == null ? "null" : obj.toString());
+3 public String substring(int begin)
+4 return substring(begin, count);
+5 public FSEntry next() {
+```
+
+These map to the 5 instances:
+
+```diff
+- public synchronized StringBuffer append(char ch)
++ public StringBuffer append(Object obj)
+```
+
+```diff
+- ensureCapacity_unsynchronized(count + 1); value[count++] = ch; return this;
++ return append(obj == null ? "null" : obj.toString());
+```
+
+```diff
+- public String substring(int beginIndex, int endIndex)
++ public String substring(int begin)
+```
+
+```diff
+- if (beginIndex < 0 || endIndex > count || beginIndex > endIndex) throw new StringIndexOutOfBoundsException(); if (beginIndex == 0 && endIndex == count) return this; int len = endIndex - beginIndex;  return new String(value, beginIndex + offset, len, (len << 2) >= value.length);
++ return substring(begin, count);
+```
+
+```diff
+- public Object next() {
++ public FSEntry next() { 
+```
+
+`context` contains the associated "context". Context is the (in-lined) buggy function (including the buggy lines and comments).
+For example, the context of 
+
+```
+public synchronized StringBuffer append(char ch)
+```
+
+is its associated function:
+
+```java
+public synchronized StringBuffer append(char ch)  {    ensureCapacity_unsynchronized(count + 1);    value[count++] = ch;    return this;  }
+```
+
+`meta` contains some metadata about the project:
+
+```
+1056	/local/tlutelli/issta_data/temp/all_java0context/java/2006_temp/2006/1056/68a6301301378680519f2b146daec37812a1bc22/StringBuffer.java/buggy/core/src/classpath/java/java/lang/StringBuffer.java
+```
+
+`1056` is the project id. `/local/...` is the absolute path to the buggy file. This can be parsed to extract the commit id: `68a6301301378680519f2b146daec37812a1bc22`, the file name: `StringBuffer.java` and the original path within the project 
+`core/src/classpath/java/java/lang/StringBuffer.java`
+
+| Number of projects | Number of Instances |
+| ------------------ |-------------------- |
+| 13,899             | 480,777             |
+
+## Dataset Creation
+
+### Curation Rationale
+
+Data is collected to train automated program repair (APR) models.
+
+### Citation Information
+
+```bib
+@inproceedings{lutellierCoCoNuTCombiningContextaware2020,
+  title = {{{CoCoNuT}}: Combining Context-Aware Neural Translation Models Using Ensemble for Program Repair},
+  shorttitle = {{{CoCoNuT}}},
+  booktitle = {Proceedings of the 29th {{ACM SIGSOFT International Symposium}} on {{Software Testing}} and {{Analysis}}},
+  author = {Lutellier, Thibaud and Pham, Hung Viet and Pang, Lawrence and Li, Yitong and Wei, Moshi and Tan, Lin},
+  year = {2020},
+  month = jul,
+  series = {{{ISSTA}} 2020},
+  pages = {101--114},
+  publisher = {{Association for Computing Machinery}},
+  address = {{New York, NY, USA}},
+  doi = {10.1145/3395363.3397369},
+  url = {https://doi.org/10.1145/3395363.3397369},
+  urldate = {2022-12-06},
+  isbn = {978-1-4503-8008-9},
+  keywords = {AI and Software Engineering,Automated program repair,Deep Learning,Neural Machine Translation}
+}
+```