CodRep is a machine learning competition on source code data. The goal of the competition is provide different communities (machine learning, software engineering, programming language) with a common playground to test and compare ideas. The competition is designed with the following principles:

1. There is no specific background or skill requirements on program analysis to understand the data.
2. The systems that use the competition data can be used beyond the competition itself. In particular, there potential usages in the field of automated program repair.

To take part to the competition, you have to write a program which predicts where to insert a specific line into a source code file. In particular, we consider replacement insertions, where the new line replaces an old line, such as

public class test{
  int a = 1;
-  int b = 0.1;
+  double b = 0.1;
}

More specifically, the program takes as input a set of pairs (source code line, source code file), and outputs, for each pair, the predicted line number of the line to be replaced by in the initial source code file.

The competition is organized by KTH Royal Institute of Technology, Stockholm, Sweden. The organization team is Zimin Chen and Martin Monperrus.

To participate to the competition, simply send an email to [email protected] with your team name, members and institution/company.

To be informed about news, intermediate rankings and final results, register to the CodRep mailing list: [email protected]

Important dates:

• Official competition start: April 14th 2018.
• Submission deadline for intermediate ranking: July 4th 2018.
• Announcement of the intermediate ranking: July 14th 2018.
• Final submission deadline: Oct. 4th 2018.
• Announcement of the final ranking & end of the competition Oct 14th 2018.

Hall of fame:

The intermediate and final ranking will be computed based on hidden datasets, which are not public or part of datasets in this competition. The hidden datasets used in intermediate and final ranking will be different, and they will be published after the respective deadlines. In order to maintain integrity, the hash or the encrypted version of the hidden datasets will be uploaded beforehand. To give participants an intuition about of their performance compared with others, there will be a weekly updated ranking based on the score from datasets in this competition. The intermediate and final ranking could be very different from the weekly ranking, since the weekly ranking is more likely to overfit the datasets.

The winner's tool will be published after the deadline.

What the participants get?

1. All participants get their name in the CodRep hall of fame
2. All participants will be invited to present their solutions at a physical workshop with proceedings

What the winner gets?

1. The ultimate CodRep fame
2. Nice KTH goodies by post
3. To be invited to take part to the design of a futuristic program repair bot in a research group
4. An official KTH certificate

The provided data are in Datasets/.../Tasks/*.txt. The txt files are meant to be parsed by competing programs. Their format is as follows, each file contains:

{Code line to insert}
\newline
{The full program file}

For instance, let's consider this example input file, called foo.txt.

double b = 0.1;

public class test{
  int a = 1;
  int b = 0.1;
}

In this example, double b = 0.1; is the code line to be added somewhere in the file in place of another line.

For such an input, the competing programs output for instance foo.txt 3, meaning replacing line 3 (int b = 0.1;) with the new code line double b = 0.1;.

To train the system, the correct answer for all input files is given in folder Datasets/.../Solutions/*.txt, e.g. the correct answer to Datasets/Datasets1/Tasks/1.txt is in Datasets/Datasets1/Solutions/1.txt

The data used in the competition is taken from real commits in open-source projects. For a number of different projects, we have analyzed all commits and extracted all the one line replacement changes. We have further filtered the data based on the following criteria (best effort):

• Only source code files are kept (Java files in dataset00)
• Comment-only changes are discarded (e.g. replacing // TODO with // Fixed)
• Inserted or removed lines are not empty lines, and are not space-only changes
• Only one replaced code line in the whole file

The datasets used in this competition are from:

Contributing: If you like to contribute with a new dataset, drop us a new email.

To play in the competition, your program takes as input input a folder name, that folder containing input data files (per the format explained above).

$ your-predictor Files

Your programs outputs on the console, for each input data file, the predicted line number. Warning: by convention, line numbers start from 1 (and not 0). Your program does not have to predict something for all input files, if there is no clear answer, simply don't output anything, the error computation takes that into account, more information about this in Loss function below.

<Path1> <line numer>
<Path2> <line numer>
<Path3> <line numer>
...

E.g.;

/Users/foo/bar/CodRep-competition/Datasets/Dataset1/Tasks/1.txt 42
/Users/foo/bar/CodRep-competition/Datasets/Dataset1/Tasks/2.txt 78
/Users/foo/bar/CodRep-competition/Datasets/Dataset1/Tasks/3.txt 30
...

You can evaluate the performance of your program by piping the output to Baseline/evaluate.py, for example:

your-program Files | python evaluate.py

The output of evaluate.py will be:

Total files: 15463
Average line error: 0.988357635773 (the lower, the better)
Recall@1: 0.00750177843885 (the higher, the better)

For evaluating specific datasets, use [-d] or [-datasets=] options and specify paths to datasets. The default behaviour is evaluating on all datasets. The path must be absolute path and multiple paths should be separated by :, for example:

your-program Files | python evaluate.py -d /Users/foo/bar/CodRep-competition/Datasets/Dataset1:/Users/foo/bar/CodRep-competition/Datasets/Dataset2

Explanation of the output of evaluate.py:

• Total files: Number of prediction tasks in datasets
• Average error: A measurement of the errors of your prediction, as defined in Loss function below. This is the only measure used to win the competition
• Recall@1: The percentage of predictions where the correct answer is in your top 1 predictions. As such, Recall@1 is the percentage of perfect predictions. We give the recall because it is easily understandable, however, it is not suitable for the competition itself, because it does not has the right properties (explained in Loss function below)

The average error is a loss function, output by evaluate.py, it measures how well your program performs on predicting the lines to be replaced. The lower the average line is, the better are your predictions.

The loss function for one prediction task is tanh(abs({correct line}-{predicted line})). The average line error is the loss function over all tasks, as calculated as the average of all individual loss.

This loss function is designed with the following properties in mind:

• There is 0 loss when the prediction is perfect
• There is a bounded and constant loss even when the prediction is far away
• Before the bound, the loss is logarithmic
• A perfect prediction is better, but only a small penalty is given to almost-perfect ones. (in our context, some code line replacement are indeed insensitive to the exact insertion locations)
• The loss is symmetric, continuous and differentiable (except at 0)

We note that the recall@1 does not comply with all those properties.

We provide 5 dumb systems for illustrating how to parse the data and having a baseline performance. These are:

• guessFirst.py: Always predict the first line of the file
• guessMiddle.py: Always predict the line in the middle of the file
• guessLast.py: Always predict the last line of the file
• randomGuess.py: Predict a random line in the file
• maximumError.py: Predict the worst case, the farthest line from the correct solution

Thanks to the design of the loss function, guessFirst.py, guessMiddle.py, guessLast.py and randomGuess.py have the same order of magnitude of error, therefore the value of average error are comparable.