Codeface

Overview

Codeface is a framework for analysing the technical and social evolution of software projects.

Motivated to analyse software development processes in large-scale software projects, Codeface mines data from version control systems (VCS), developer communication channels and static dependencies to gain insights into developer collaboration and communication as well as software quality.

You can read more about Codeface's motivation on its website.

Installation

We recommend to set up Codeface with Docker.

Install Codeface with Docker

If Docker is not yet installed on your system, please consult its installation instructions.

1. Clone this repository

2. Build the Docker image from the Dockerfile in your local repository (this may take up to an hour)

   docker build -t codeface .
   

3. Run a Docker container from your codeface image

   docker run --name codeface -d -t --user codeface codeface
   

   Optionally, you can export the MySQL database port (3306) to your host system or share data and source code between your host system and the container. Please consult the docker run documentation for details.

Getting started

1. Log in to your Docker container

   docker exec -it codeface bash
   

2. Run an example analysis of the QEMU processor emulator (this may take several hours).

   cd /home/codeface/codeface && ./analysis_example.sh
   

3. Verify the results in the local codeface database (user and password codeface) and in the result directory (/home/codeface/res).

Custom software analyses

Codeface supports several analysis modes which can be used for your custom analyses. Each analysis mode is reflected by a command in Codeface's CLI.

Standard analysis

The standard analysis bundles a set of analyses which can be run on each git repository.

Codeface analyses software evolution in time windows. It parses commits from the repository and stores them in a local database. Codeface constructs the developer collaboration network on the fly. Based on the results, it detects communities, summarises structural project trends and estimates development efforts over time.

Analysis steps

1. Provide the Git repository of the software project of choice, for example using git clone.

2. Create a project configuration file in the conf folder. The existing configuration files may serve as a reference.

3. Run the standard analysis using the codeface run command.

   A codeface run command takes the following form:

   codeface run [OPTIONS] -c [TOOL CONF] -p [PROJECT CONF] [RESULT DIR] [GIT DIR]
   

   Option	Description
   -c, --config	Codeface configuration file (codeface.conf)
   -p, --project	Project configuration file
   -l, --loglevel	Log level. Can be one of debug, devinfo, info, warning, error.
   -f, --logfile	Optional log file.
   -j, --jobs	Number of cores to use in parallel. Default is 1.
   --recreate	Force a delete of the project in the database.
   --reuse-vcs-analysis	Re-use an existing vcs-analysis.db from a previous analysis run.
   --no-export	Skip LaTeX report generation and dot compilation (local artificats).

4. Inspect the results in the local codeface database (user and password codeface) and in the given result directory.

Mailing list analysis

In large distributed open source software projects, mailing lists are an established means of communication.

This analysis mode parses mails from an existing mail box, analyses frequent subjects and constructs the communication network.

Analysis steps

1. Run all standard analyses for the project of choice as described above.

2. Download the mailing list for the project and export it to an .mbox file.

3. Run the mailing list analysis using the codeface ml command.

   A codeface ml command takes the following form:

   codeface ml [OPTIONS] -c [TOOL CONF] -p [PROJECT CONF] [RESULT DIR] [MAIL DIR]
   

   Option	Description
   -c, --config	Codeface configuration file (codeface.conf)
   -p, --project	Project configuration file
   -l, --loglevel	Log level. Can be one of debug, devinfo, info, warning, error.
   -f, --logfile	Optional log file.
   -j, --jobs	Number of cores to use in parallel. Default is 1.
   --use-corpus	Re-use an existing corpus file from a previous analysis run.

4. Inspect the results in the local codeface database (user and password codeface) and in the given result directory.