grading-on-a-curve/code/README.md

SZZUnleashed

A implementation of the SZZ algorithm as described by Zeller et al's in "When Do Changes Induce Fixes?". It also contains further improvements as described by Williams et al's in Szz revisited: verifying when changes induce fixes.

What is the usage of this algorithm?

The SZZ algorithm is used to find bug introducing commits from a set of bug fixing commits. The bug introducing commits can be extracted either from a bug tracking system such as JIRA or simply by searching for commits that states that they are fixing something. The found bug introducing commits can then be used to build datasets for machine learning purposes such as when buggy commits wants to be found.

Prerequisites:

• Java 8
• Gradle

Usage SZZ algorithm

Grab issues

The fetch script is an example of how one can extract issues from a bug tracking system.

python fetch.py

It creates a directory with issues. To convert these into a format where they can be processed, use:

python git_log_to_array.py <path_to_local_repo>

This creates a file gitlog.json that is used to link the issues to bug fixing commits. Using the find_bug_fixes.py and this file, we can get a json file that contains the Issue and its corresponding commit SHA-1, the commit date, the creation date and the resolution date.

Find the bug introducing commits

This implementation works regardless which language and file type. It uses JGIT to parse a git repository.

To build a runnable jar file, use the gradle build script in the szz directory like:

gradle build && gradle fatJar

Or if the algorithm should be runned without building a jar:

gradle build && gradle runJar

The algorithm tries to use as many cores as possible during runtime. The more the merrier so to say.

To get the bug introducing commits from a repository using the file produced by the previous issue to bug fix commit step, run:

java -jar szz_find_bug_introducers-<version_number>.jar -i <path_to_issues> -r <path_to_local_repo>

To assemble the results if the algorithm was able to use more than one core, run the assembler.py script on the results directory.

Output

The output can then be seen in three different files commits.json, annotations.json and fix_and_bug_introducing_pairs.json.

The commits.json file includes all commits that have been blamed to be bug introducing but which haven't been analyzed by any anything.

The annotations.json is a representation of the graph that is generated by the algorithm in the blaming phase. Each bug fixing commit is linked to all possible commits which could be responsible for the bug. Using the improvement from Williams et al's, the graph also contains subgraphs which gives a deeper search for responsible commits. It enables the algorithm to blame other commits than just the one closest in history for a bug.

Lastly, the fix_and_bug_introducing_pairs.json includes all possible pairs which could lead to a bug introduction and fix. This file is not sorted in any way and it includes doublettes when it comes to both introducers and fixes. A fix can be made several times and a introducer could be responsible for many fixes.

Feature Extraction

Now that the potential bug introducing commits has been identified, the repository can be mined for features.

Code Churns

The most simple features are the code churns. These are easily extracted by just parsing each diff for each commit. The ones that are extracted are:

1. Total lines of code - Which simply is how many lines of code in total for all changed files.
2. Churned lines of code - This is how many lines that have been inserted.
3. Deleted lines of code - The number of deleted lines.
4. Number of Files - The total number of changed files.

To get these features, run: python assemble_code_churns.py <path_to_repo> <branch>

Diffusion Features

The diffusion features are:

1. The number of modified subsystems.
2. The number of modified subdirectories.
3. The entropy of the change.

To extract the diffusion features, just run: python assemble_diffusion_features.py --repository <path_to_repo> --branch <branch>

Experience Features

Maybe the most uncomfortable feature group. The experience features are the features that measures how much experience a developer has, both how recent but also how much experience the developer has overall with the code.

The features are:

1. Overall experience.
2. Recent experience.

The script builds a graph to keep track of each authors experience. So the intial run is: python assemble_experience_features.py --repository <repo_path> --branch <branch> --save-graph

This will result in a graph which the script could use for future analysis

To rerun the analysis without generating a new graph, just run: python assemble_experience_features.py --repository <repo_path> --branch <branch>

History Features

The history are as follows:

1. The number of authors in a file.
2. The time between contributions made by the author.
3. The number of unique changes between the last commit.

The same as with the experience features, the script must initially generate a graph where the file meta data is saved. python assemble_history_features.py --repository <repo_path> --branch <branch> --save-graph

To rerun the script without generating a new graph, use: python assemble_history_features.py --repository <repo_path> --branch <branch>

Purpose Features

The purpose feature is just a single feature and that is if the commit is a fix o not. To extract it use:

python assemble_purpose_features.py --repository <repo_path> --branch <branch>

Coupling

A more complex number of features are the coupling features. These indicates how strong the relation is between files and modules for a revision. This means that two files can have a realtion even though they don't have a realtion inside the source code itself. So by mining these, features that gives indications in how many files that a commit actually has made changes to are found.

The mining is made by a docker image containing the tool code-maat.

These features takes long time to extract but is mined using:

python assemble_features.py --image code-maat --repo-dir <path_to_repo> --result-dir <path_to_write_result>
python assemble_coupling_features.py <path_to_repo>

It is also possible to specify which commits to analyze. This is done with the CLI option --commits <path_to_file_with_commits>. The format of this file is just lines where each line is equal to the corresponding commit SHA-1.

If the analyzation is made by several docker containers, one has to specify the --assemble option which stands for assemble. This will collect and store all results in a single directory.

The script is capable of checking if the are any commits that haven't been analyzed. To do that, specify the --missing-commits option.

Classification

Now that data has been assembled the training and testing of the ML model can be made. To do this, simply run the model script in the model directory:

python model.py train

Authors

Oscar Svensson Kristian Berg