Graal leverages on the Git backend of Perceval and enhances it to set up ad-hoc source code analysis. Thus, it fetches the commits from a Git repository and provides a mechanism to plug third party tools/libraries focused on source code analysis.
How it works
The Perceval Git backend creates a local mirror of a Git repository (local or remote), fetches the metadata of commits in chronological order and returns them as a list of JSON documents (one per commit). Graal leverages on the incremental functionalities provided by the Git backend and enhances the logic to handle Git repositories by creating a working tree to perform checkout operations (which are not possible on a Git mirror). Graal intercepts each JSON document and enables the user to perform the following steps:
- Filter. The filtering is used to select or discard commits based on the information available in the JSON document and/or via the Graal parameters. For any selected commit, Graal executes a checkout on the working tree using the commit hash, thus setting the state of the working tree at that given revision.
- Analyze. The analysis takes the JSON document and the current working tree and enables the user to set up ad-hoc source code analysis by plugging existing tools through system calls or their Python interfaces, when possible. The results of the analysis are parsed and manipulated by the user and then automatically embedded in the JSON document. It is worth noting that in this step the user can rely on some predefined functionalities of Graal to deal with the repository snapshot (e.g., listing files, creating archives).
- Post-process. In the final step, the inflated JSON document can be optionally processed to alter (e.g., renaming, removing) its attributes, thus granting the user complete control over the output of Graal executions.
Several parameters (inherited from the Git backend) are available to control the execution; for instance, from_date and to_date allow to select commits authored since and before a given date, branches allows to fetch commits only from specific branches, and latest_items returns only those commits which are new since the last fetch operation. Graal includes additional parameters to drive the analysis to filter in/out files and directories in the repository (in_paths and out_paths), set the entrypoint and define the details level of the analysis (useful when analyzing large software projects).
How to install/create the executables:
$> sudo apt-get install cloc
$> git clone https://github.com/fossology/fossology $> cd <...>/fossology/src/nomos/agent $> make -f Makefile.sa
Follow the instructions on the ScanCode GitHub repository
How to install/uninstall
Graal is being developed and tested mainly on GNU/Linux platforms. Thus it is very likely it will work out of the box on any Linux-like (or Unix-like) platform, upon providing the right version of Python (3.5, 3.6).
To install, run:
$> git clone https://github.com/valeriocos/graal $> python3 setup.py build $> python3 setup.py install
To uninstall, run:
$> pip3 uninstall graal
Several backends have been developed to assess the genericity of Graal. Those backends leverage on source code analysis tools, where executions are triggered via system calls or their Python interfaces. In the current status, the backends mostly target Python code, however other backends can be easily developed to cover other programming languages. The currently available backends are:
- CoDep extracts package and class dependencies of a Python module and serialized them as JSON structures, composed of edges and nodes, thus easing the bridging with front-end technologies for graph visualizations. It combines PyReverse and NetworkX.
- CoQua retrieves code quality insights, such as checks about line-code’s length, well-formed variable names, unused imported modules and code clones. It uses PyLint.
- CoVuln scans the code to identify security vulnerabilities such as potential SQL and Shell injections, hard-coded passwords and weak cryptographic key size. It relies on Bandit.
- CoLic scans the code to extract license information. It currently supports Nomos and ScanCode. They can be
activated by passing the corresponding category:
How to develop a backend
Creating your own backend is pretty easy, you only need to redefine the following methods of Graal:
- _filter_commit. This method is used to select or discard commits based on the information available in the JSON document and/or via the Graal parameters (e.g., the commits authored by a given user or targeting a given software component). For any selected commit, Graal executes a checkout on the working tree using the commit hash, thus setting the state of the working tree at that given revision.
- _analyze. This method takes the document and the current working tree and allows to connect existing tools through system calls or their Python interfaces, when possible. The results of the analysis, parsed and manipulated by the user, are automatically embedded in the JSON document.
- _post. This method allows to alter (e.g., renaming, removing) the attributes of the inflated JSON documents.
How to use
Graal can be used from command line or directly from Python, both usages are described below.
From command line
Launching Graal from command line does not require much effort, but only some basic knowledge of GNU/Linux shell commands.
The example below shows how easy it is to fetch code complexity information from a Git repository. The CoCom backend requires the URL where the repository is located (https://github.com/chaoss/grimoirelab-perceval) and the local path where to mirror the repository (/tmp/graal-cocom). Then, the JSON documents produced are redirected to the file graal-cocom.test.
$ graal cocom https://github.com/chaoss/grimoirelab-perceval --git-path /tmp/graal-cocom > /graal-cocom.test Starting the quest for the Graal. Git worktree /tmp/... created! Fetching commits: ... Git worktree /tmp/... deleted! Fetch process completed: .. commits inspected Quest completed.
Graal’s functionalities can be embedded in Python scripts. Again, the effort of using Graal is minimum. In this case the user only needs some knowledge of Python scripting. The example below shows how to use Graal in a script.
The graal.backends.core.cocom module is imported at the beginning of the file, then the repo_uri and repo_dir variables are set to the URI of the Git repository and the local path where to mirror it. These variables are used to initialize a CoCom class object. In the last line of the script, the commits inflated with the result of the analysis are retrieved using the fetch method. The fetch method inherits its argument from Perceval, thus it optionally accept two Datetime objects to gather only those commits after and before a given date, a list of branches to focus on specific development activities, and a flag to collect the commits available after the last execution.
#! /usr/bin/env python3 from graal.backends.core.cocom import CoCom # URL for the git repo to analyze repo_uri = ’http://github.com/chaoss/grimoirelab-perceval’ # directory where to mirror the repo repo_dir = ’/tmp/graal-cocom’ # Cocom object initialization cc = CoCom(uri=repo_url, gitpath=repo_dir) # fetch all commits commits = [commit for commit in cc.fetch()]
How to integrate it with Arthur
Arthur is another tool of the Grimoirelab ecosystem. It was originally designed to allow to schedule and run Perceval executions at scale through distributed Redis queues, and store the obtained results in an ElasticSearch database.
Arthur has been extended to allow handling Graal tasks, which inherit from Perceval Git tasks. The code to make this extension possible is available at: https://github.com/chaoss/grimoirelab-kingarthur/pull/33.
Information about Arthur is available at https://github.com/chaoss/grimoirelab-kingarthur.