h4iku/coconut_c2005

Dataset Card for CoCoNuT-C(2005)

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

• C

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:

- public synchronized StringBuffer append(char ch)
+ public StringBuffer append(Object obj)

- ensureCapacity_unsynchronized(count + 1); value[count++] = ch; return this;
+ return append(obj == null ? "null" : obj.toString());

- public String substring(int beginIndex, int endIndex)
+ public String substring(int begin)

- 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);

- 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:

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
12,577	2,735,506

Dataset Creation

Curation Rationale

Data is collected to train automated program repair (APR) models.

Citation Information

@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}
}