PyDriller documentation!

Overview / Install

PyDriller is a Python framework that helps developers on mining software repositories. With PyDriller you can easily extract information from any Git repository, such as commits, developers, modifications, diffs, and source codes, and quickly export CSV files.

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Requirements

Installing PyDriller

Installing PyDriller is easily done using pip. Assuming it is installed, just run the following from the command-line:

# pip install pydriller

This command will download the latest version of GitPython from the Python Package Index and install it to your system. This will also install the necessary dependencies.

Source Code

PyDriller’s git repo is available on GitHub, which can be browsed at:

and cloned using:

$ git clone https://github.com/ishepard/pydriller
$ cd pydriller

Optionally (but suggested), make use of virtualenv:

$ virtualenv -p python3 venv
$ source venv/bin/activate

Install the requirements:

$ pip install -r requirements
$ unzip test-repos.zip

and run the tests using pytest:

$ pytest

How to cite PyDriller

@inbook{PyDriller,
    title = "PyDriller: Python Framework for Mining Software Repositories",
    abstract = "Software repositories contain historical and valuable information about the overall development of software systems. Mining software repositories (MSR) is nowadays considered one of the most interesting growing fields within software engineering. MSR focuses on extracting and analyzing data available in software repositories to uncover interesting, useful, and actionable information about the system. Even though MSR plays an important role in software engineering research, few tools have been created and made public to support developers in extracting information from Git repository. In this paper, we present PyDriller, a Python Framework that eases the process of mining Git. We compare our tool against the state-of-the-art Python Framework GitPython, demonstrating that PyDriller can achieve the same results with, on average, 50% less LOC and significantly lower complexity.URL: https://github.com/ishepard/pydrillerMaterials: https://doi.org/10.5281/zenodo.1327363Pre-print: https://doi.org/10.5281/zenodo.1327411",
    author = "Spadini, Davide and Aniche, Maurício and Bacchelli, Alberto",
    year = "2018",
    doi = "10.1145/3236024.3264598",
    booktitle = "The 26th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE)",
}

Getting Started

Using PyDriller is very simple. You only need to create RepositoryMining: this class will receive in input the path to the repository and will return a generator that iterates over the commits. For example:

for commit in RepositoryMining('path/to/the/repo').traverse_commits():
    print('Hash {}, author {}'.format(commit.hash, commit.author.name))

will print the name of the developers for each commit.

Inside RepositoryMining, you will have to configure which projects to analyze, for which commits, for which dates etc. For all the possible configurations, have a look at Configuration.

Let’s make another example: print all the modified files for every commit. This does the magic:

for commit in RepositoryMining('path/to/the/repo').traverse_commits():
    for modification in commit.modifications:
        print('Author {} modified {} in commit {}'.format(commit.author.name, modification.filename, commit.hash))

That’s it!

Behind the scenes, PyDriller opens the Git repository and extracts all the necessary information. Then, the framework returns a generator that can iterate over the commits.

Furthermore, PyDriller can calculate structural metrics of every file changed in a commit. To calculate these metrics, Pydriller relies on Lizard, a powerful tool that can analyze source code of many different programming languages, both at class and method level!

for commit in RepositoryMining('path/to/the/repo').traverse_commits():
    for mod in commit.modifications:
        print('{} has complexity of {}, and it contains {} methods'.format(
              mod.filename, mod.complexity, len(mod.methods))

Configuration

One of the main advantage of using PyDriller to mine software repositories, is that is highly configurable. Let’s start with selecting which commit to analyze.

Selecting projects to analyze

The only required parameter of RepositoryMining is path_to_repo, which specifies the repo(s) to analyze. It must be of type str or List[str], meaning analyze only one repository or more than one.

Furthermore, PyDriller supports both local and remote repositories: if you pass an URL, PyDriller will automatically create a temporary folder, clone the repository, run the study, and finally delete the temporary folder.

For example, the following are all possible inputs for RepositoryMining:

url = "repos/pydriller/" # analyze only 1 local repository
url = ["repos/pydriller/", "repos/anotherrepo/"]  # analyze 2 local repositories
url = ["repos/pydriller/", "https://github.com/apache/hadoop.git", "repos/anotherrepo"] # analyze both local and remote
url = "https://github.com/apache/hadoop.git" # analyze 1 remote repository

Selecting the Commit Range

By default, PyDriller analyzes all the commits in the repository. However, filters can be applied to RepositoryMining to visit only specific commits.

  • single: str: single hash of the commit. The visitor will be called only on this commit

FROM:

  • since: datetime: only commits after this date will be analyzed
  • from_commit: str: only commits after this commit hash will be analyzed
  • from_tag: str: only commits after this commit tag will be analyzed

TO:

  • to: datetime: only commits up to this date will be analyzed
  • to_commit: str: only commits up to this commit hash will be analyzed
  • to_tag: str: only commits up to this commit tag will be analyzed

Examples:

# Analyze single commit
RepositoryMining('path/to/the/repo', single='6411e3096dd2070438a17b225f44475136e54e3a').traverse_commits()

# Since 8/10/2016
RepositoryMining('path/to/the/repo', since=datetime(2016, 10, 8, 17, 0, 0)).traverse_commits()

# Between 2 dates
dt1 = datetime(2016, 10, 8, 17, 0, 0)
dt2 = datetime(2016, 10, 8, 17, 59, 0)
RepositoryMining('path/to/the/repo', since=dt1, to=dt2).traverse_commits()

# Between tags
from_tag = 'tag1'
to_tag = 'tag2'
RepositoryMining('path/to/the/repo', from_tag=from_tag, to_tag=to_tag).traverse_commits()

# Up to a date
dt1 = datetime(2016, 10, 8, 17, 0, 0, tzinfo=to_zone)
RepositoryMining('path/to/the/repo', to=dt1).traverse_commits()

# !!!!! ERROR !!!!! THIS IS NOT POSSIBLE
RepositoryMining('path/to/the/repo', from_tag=from_tag, from_commit=from_commit).traverse_commits()

IMPORTANT: it is not possible to configure more than one filter of the same category (for example, more than one from). It is also not possible to have the single filter together with other filters!

Filtering commits

PyDriller comes with a set of common commit filters that you can apply:

  • only_in_branches: List[str]: only analyses commits that belong to certain branches.
  • only_in_main_branch: bool: only analyses commits that belong to the main branch of the repository.
  • only_no_merge: bool: only analyses commits that are not merge commits.
  • only_modifications_with_file_types: List[str]: only analyses commits in which at least one modification was done in that file type, e.g., if you pass “.java”, then, the it will visit only commits in which at least one Java file was modified; clearly, it will skip other commits.

Examples:

# Only commits in main branch
RepositoryMining('path/to/the/repo', only_in_main_branch=True).traverse_commits()

# Only commits in main branch and no merges
RepositoryMining('path/to/the/repo', only_in_main_branch=True, only_no_merge=True).traverse_commits()

# Only commits that modified a java file
RepositoryMining('path/to/the/repo', only_modifications_with_file_types=['.java']).traverse_commits()

Commit Object

A Commit contains a hash, a committer (name and email), an author (name, and email), a message, the authored date, committed date, a list of its parent hashes (if it’s a merge commit, the commit has two parents), and the list of modification.

For example:

for commit in RepositoryMining('path/to/the/repo').traverse_commits():
    print(
        'Hash: {}\n'.format(commit.hash),
        'Author: {}'.format(commit.author.name),
        'Committer: {}'.format(commit.committer.name),
        'Author date: {}'.format(commit.author_date.strftime("%Y-%m-%d %H:%M:%S")),
        'Message: {}'.format(commit.msg),
        'Merge: {}'.format(commit.merge),
        'In main branch: {}'.format(commit.in_main_branch)
)

Modifications

You can get the list of modified files, as well as their diffs and current source code. To that, all you have to do is to get the list of Modifications that exists inside Commit. A modification object has the following fields:

  • old_path: old path of the file (can be _None_ if the file is added)
  • new_path: new path of the file (can be _None_ if the file is deleted)
  • change_type: type of the change: can be Added, Deleted, Modified, or Renamed.
  • diff: diff of the file as Git presents it (e.g., starting with @@ xx,xx @@).
  • source_code: source code of the file (can be _None_ if the file is deleted)
  • added: number of lines added
  • removed: number of lines removed
  • nloc: Lines Of Code (LOC) of the file
  • complexity: Cyclomatic Complexity of the file
  • token_count: Number of Tokens of the file
  • methods: list of methods of the file. The list might be empty if the programming language is not supported or if the file is not a source code file.

For example:

for commit in RepositoryMining('path/to/the/repo').traverse_commits():
    for m in commit.modifications:
        print(
            "Author {}".format(commit.author.name),
            " modified {}".format(m.filename),
            " with a change type of {}".format(m.change_type.name),
            " and the complexity is {}".format(m.complexity)
        )

Git Repository

GitRepository is a wrapper for the most common utilities of Git. It receives in input the path to repository, and it takes care of the rest. For example, with GitRepository you can checkout a specific commit:

gr = GitRepository('test-repos/git-1/')
gr.checkout('a7053a4dcd627f5f4f213dc9aa002eb1caf926f8')

However, be careful! Git checkout changes the state of the repository on the hard disk, hence you should not use this command if other processes (maybe threads? or multiple repository mining?) read from the same repository.

GitRepository also contains a function to parse the a diff, very useful to obtain the list of lines added or deleted for future analysis. For example, if we run this:

diff = '@@ -2,6 +2,7 @@ aa'+\
    ' bb'+\
    '-cc'+\
    ' log.info(\"aa\")'+\
    '+log.debug(\"b\")'+\
    ' dd'+\
    ' ee'+\
    ' ff'
gr = GitRepository('test-repos/test1')
parsed_lines = gr.parse_diff(diff)

added = parsed_lines['added']
deleted = parsed_lines['deleted']

print('Added: {}'.format(added))      # result: Added: [(4, 'log.debug("b")')]
print('Deleted: {}'.format(deleted))  # result: Deleted: [(3, 'cc')]

the result is:

Added: [(4, 'log.debug("b")')]
Deleted: [(3, 'cc')]

Another very useful API (especially for researchers ;) ) is the one that, given a commit, allows you to retrieve all the commits that last “touched” the modified lines of the file (if you pass a bug fixing commit, it will retrieve the bug inducing). Let’s see an example:

# commit abc modified line 1 of file A
# commit def modified line 2 of file A
# commit ghi modified line 3 of file A
# commit lmn deleted lines 1 and 2 of file A

gr = GitRepository('test-repos/test5')

commit = gr.getcommit('lmn')
buggy_commits = gr.get_commits_last_modified_lines(commit)
print(buggy_commits)      # result: (abc, def)

Since in commit lmn 2 lines were deleted (line 1 and 2), PyDriller can retrieve the commits in which those lines were last modified (in our example, commit abc and def).

Isn’t it cool? :)

Checkout the API reference of this class for the complete list of the available functions.

API Reference

GitRepository

class pydriller.git_repository.GitRepository(path: str)
__init__(path: str)

Init the Git Repository.

Parameters:path (str) – path to the repository
__module__ = 'pydriller.git_repository'
checkout(_hash: str) → None

Checkout the repo at the speficied commit. BE CAREFUL: this will change the state of the repo, hence it should not be used with more than 1 thread.

Parameters:_hash – commit hash to checkout
files() → List[str]

Obtain the list of the files (excluding .git directory).

Returns:List[str], the list of the files
get_commit(commit_id: str) → pydriller.domain.commit.Commit

Get the specified commit.

Parameters:commit_id (str) – hash of the commit to analyze
Returns:Commit
get_commit_from_gitpython(commit: git.objects.commit.Commit) → pydriller.domain.commit.Commit

Build a PyDriller commit object from a GitPython commit object. This is internal of PyDriller, I don’t think users generally will need it.

Parameters:commit (GitCommit) – GitPython commit
Returns:Commit commit: PyDriller commit
get_commit_from_tag(tag: str) → pydriller.domain.commit.Commit

Obtain the tagged commit.

Parameters:tag (str) – the tag
Returns:Commit commit: the commit the tag referred to
get_commits_last_modified_lines(commit: pydriller.domain.commit.Commit, modification: pydriller.domain.commit.Modification = None) → Set[str]

Given the Commit object, returns the set of commits that last “touched” the lines that are modified in the files included in the commit. It applies SZZ. The algorithm works as follow: (for every file in the commit)

1- obtain the diff

2- obtain the list of deleted lines

3- blame the file and obtain the commits were those lines were added

Can also be passed as parameter a single Modification, in this case only this file will be analyzed.

Parameters:
  • commit (Commit) – the commit to analyze
  • modification (Modification) – single modification to analyze
Returns:

the set containing all the bug inducing commits

get_head() → pydriller.domain.commit.Commit

Get the head commit.

Returns:Commit of the head commit
get_list_commits() → List[pydriller.domain.commit.Commit]

Return the list of all the commits in the repo.

Returns:List[Commit], the list of all the commits in the repo
parse_diff(diff: str) → Dict[str, List[Tuple[int, str]]]

Given a diff, returns a dictionary with the added and deleted lines. The dictionary has 2 keys: “added” and “deleted”, each containing the corresponding added or deleted lines. For both keys, the value is a list of Tuple (int, str), corresponding to (number of line in the file, actual line).

Parameters:diff (str) – diff of the commit
Returns:Dictionary
reset() → None

Reset the state of the repo, checking out the main branch and discarding local changes (-f option).

total_commits() → int

Calculate total number of commits.

Returns:the total number of commits

RepositoryMining

class pydriller.repository_mining.RepositoryMining(path_to_repo: Union[str, List[str]], single: str = None, since: datetime.datetime = None, to: datetime.datetime = None, from_commit: str = None, to_commit: str = None, from_tag: str = None, to_tag: str = None, reversed_order: bool = False, only_in_main_branch: bool = False, only_in_branches: List[str] = None, only_modifications_with_file_types: List[str] = None, only_no_merge: bool = False)
__init__(path_to_repo: Union[str, List[str]], single: str = None, since: datetime.datetime = None, to: datetime.datetime = None, from_commit: str = None, to_commit: str = None, from_tag: str = None, to_tag: str = None, reversed_order: bool = False, only_in_main_branch: bool = False, only_in_branches: List[str] = None, only_modifications_with_file_types: List[str] = None, only_no_merge: bool = False)

Init a repository mining.

Parameters:
  • or List[str] path_to_repo (str) – absolute path to the repository (or list of absolute paths) you have to analyze
  • single (str) – hash of a single commit to analyze
  • since (datetime) – starting date
  • to (datetime) – ending date
  • from_commit (str) – starting commit (only if since is None)
  • to_commit (str) – ending commit (only if to is None)
  • from_tag (str) – starting the analysis from specified tag (only if since and from_commit are None)
  • to_tag (str) – ending the analysis from specified tag (only if to and to_commit are None)
  • reversed_order (bool) – whether the commits should be analyzed in reversed order
  • only_in_main_branch (bool) – whether only commits in main branch should be analyzed
  • only_in_branches (List[str]) – only commits in these branches will be analyzed
  • only_modifications_with_file_types (List[str]) – only modifications with that file types will be analyzed
  • only_no_merge (bool) – if True, merges will not be analyzed
__module__ = 'pydriller.repository_mining'
clone_remote_repos(tmp_folder: str, repo: str) → str
get_repo_name_from_url(url: str) → str
isremote(repo: str) → bool
traverse_commits() → Generator[[pydriller.domain.commit.Commit, None], None]

Analyze all the specified commits (all of them by default), returning a generator of commits.

Commit

class pydriller.domain.commit.Commit(commit: git.objects.commit.Commit, path: str, project_name: str, main_branch: str)
__init__(commit: git.objects.commit.Commit, path: str, project_name: str, main_branch: str) → None

Create a commit object.

__module__ = 'pydriller.domain.commit'
author

Return the author of the commit as a Developer object.

Returns:author
author_date

Return the authored datetime.

Returns:datetime author_datetime
author_timezone

Author timezone expressed in seconds from epoch.

Returns:int timezone
branches

Return the set of branches that contain the commit.

Returns:set(str) branches
committer

Return the committer of the commit as a Developer object.

Returns:committer
committer_date

Return the committed datetime.

Returns:datetime committer_datetime
committer_timezone

Author timezone expressed in seconds from epoch.

Returns:int timezone
hash

Return the SHA of the commit.

Returns:str hash
in_main_branch

Return True if the commit is in the main branch, False otherwise.

Returns:bool in_main_branch
merge

Return True if the commit is a merge, False otherwise.

Returns:bool merge
modifications

Return a list of modified files.

Returns:List[Modification] modifications
msg

Return commit message.

Returns:str commit_message
parents

Return the list of parents SHAs.

Returns:List[str] parents
class pydriller.domain.commit.Method(func)
__init__(func)

Initialize a method object. This is calculated using Lizard: it parses the source code of all the modifications in a commit, extracting information of the methods contained in the file (if the file is a source code written in one of the supported programming languages).

__module__ = 'pydriller.domain.commit'
class pydriller.domain.commit.Modification(old_path: str, new_path: str, change_type: pydriller.domain.commit.ModificationType, diff_and_sc: Dict[str, str])
__init__(old_path: str, new_path: str, change_type: pydriller.domain.commit.ModificationType, diff_and_sc: Dict[str, str])

Initialize a modification. A modification carries on information regarding the changed file. Normally, you shouldn’t initialize a new one.

__module__ = 'pydriller.domain.commit'
added

Return the total number of added lines in the file.

Returns:int lines_added
complexity

Calculate the Cyclomatic Complexity of the file.

Returns:Cyclomatic Complexity of the file
filename

Return the filename. Given a path-like-string (e.g. “/Users/dspadini/pydriller/myfile.py”) returns only the filename (e.g. “myfile.py”)

Returns:str filename
methods

Return the list of methods in the file. Every method contains various information like complexity, loc, name, number of parameters, etc.

Returns:list of methods
nloc

Calculate the LOC of the file.

Returns:LOC of the file
removed

Return the total number of deleted lines in the file.

Returns:int lines_deleted
token_count

Calculate the token count of functions.

Returns:token count
class pydriller.domain.commit.ModificationType

An enumeration.

ADD = (1,)
COPY = (2,)
DELETE = (4,)
MODIFY = 5
RENAME = (3,)
__module__ = 'pydriller.domain.commit'

Developer

class pydriller.domain.developer.Developer(name: str, email: str)
__init__(name: str, email: str)

Class to identify a developer.

Parameters:
  • name (str) – name and surname of the developer
  • email (str) – email of the developer
__module__ = 'pydriller.domain.developer'

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