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README.md

PaStA - Patch Stack Analysis

Getting PaStA

Clone PaStA and its resources submodule. The resources contain configuration as well as results of some sample projects.

$ git clone https://github.com/lfd/PaStA.git
$ cd PaStA
$ git submodule update --recursive --init resources

Requirements

PaStA requires Python3 and comes with the following dependencies:

  • git
  • pygit2
  • git-python (for patch_descriptions only)
  • R (tikzDevice, ggplot2)
  • fuzzywuzzy + python-levenshtein
  • procmail
  • python-anytree
  • python-dateparser
  • python scikit-learn
  • python-toml
  • python-tqdm
  • flask
    • flask-wtf
    • flask-bootstrap
    • flask-nav

On Ubuntu 18.10 as reference distro, those dependencies can easily installed with:

# apt install python3-sklearn python3-git python3-pygit2 python3-fuzzywuzzy
	      python3-flaskext.wtf python3-pip python3-tqdm \
              git procmail
$ pip3 install --user flask-bootstrap flask-nav anytree

Getting started

  • Select the active project configuration ./pasta select linux
  • Run PaStA ./pasta -h

Running PaStA

PaStA's caches

Many projects contain thousands of commits. It is time-consuming to determine and load commits. To increase overall performance, PaStA persists lists of commit hashes and creates pkl-based commit caches. Those lists will be created when needed. PaStA detects changes in the configuration file and automatically updates those lists.

The commit cache has to be created manually:

$ ./pasta sync # Creates cache file for commits on the patch stacks
$ ./pasta sync -mbox # Update / synchronise mailboxes before creating caches

Detecting and grouping similar patches

Detecting similar patches on patch stacks (i.e., branches) and eventually linking them into equivalence classes is split in two different commands: pasta analyse and pasta rate. Reason for the split is the comparatively long duration of the analysation phase. After pasta analyse, you might want to reuse the results of the analysation and run pasta rate for several times on the same data set.

The detection phase is split in four steps:

  1. Comparing successive versions on the patch stacks
    $ ./pasta analyse succ
    $ ./pasta rate
    
  2. For more fine-granular classification, compare representants of existing equivalence classes
    $ ./pasta analyse rep
    $ ./pasta rate
    
  3. Once you think you have found all equivalence classes you can find to find representants of them upstream
    $ ./pasta analyse upstream
    $ ./pasta rate
    

This will create a patch-groups file inside the resources directory of your projecta. Each line represents a group of similar patches, commit hashes are separated by whitespaces. A line can optionally end with ' => ' and point to upstream commit hash(es).

Run statistics

After PaStA created the patch-groups file, you can run some predefined statistics on your data by running

$ ./pasta statistics

This will automatically create a new directory inside your resources and place csv files that serve as input for R. Afterwards, pasta statistics automatically invokes R, plots some graphs and stores them in the same directory as png and tikz files.

If you want PaStA only to create the csv files only without running R, you can invoke it by using ./pasta statistics -noR -R /tmp/foo/. This will not invoke R and place the csvs in /tmp/foo.

PaStA commands in detail

PaStA subcommands

To get list of all available PaStA commands, run ./pasta -h. pasta sub -h gives you further detailed information about subcommands.

Tools

pasta compare

./pasta compare analyses a list of commit hashes given as command line arguments and displays the evaluation result as well as the original commits.

Creating a new PaStA project

Preparing the repository

All project-relevant file are located in resources/PROJECT_NAME/. Default locations inside that directory:

  • config: the main configuration file of the project. This file sets the project name, different version ranges, time windows and default thresholds.
  • repo/: This is the default location of the repository of the project. While not strictly required, repos are usually added as git submodules.
  • resources/patch-stack-definition.dat: Definition of the patch stacks. Lines beginning with # are interpreted as comments, lines beginning with ## group major versions of projects. Take a look at existing patch stack definitions.

PaStA configuration format

The PaStA configuration file scheme is similar to the Windows ini format. All configuration file inherit from resources/common/default.cfg and must implement some mandatory values. This is a minimal example for a project configuration file:

[PaStA]
PROJECT_NAME = foobar
MODE = mbox / patchstack

UPSTREAM = v1.0..v2.0

Set active configuration

Use the select command to set the active configuration. E.g.:

$ ./pasta select linux

All further calls on PaStA tools will use this configuration file. To use a specific configuration for a single PaStA command, this may be overridden with the -c command line parameter:

$ ./pasta -c busybox subcommand ...

PaStA Mailbox Analysis

PaStA is able to map mails from mailboxes (e.g. dumps of mailing lists or public inboxes) to commit hashes of repositories. PaStA searches for mails in the mailbox that contain patches. Yet, PaStA does not entirely understand all different mail formats. After all potential patches have been detected, PaStA will save those patches in a commit cache file. This file can be used for further analysis and is compared against all 'upstream' commits (master branch).

  1. ./pasta select linux
  2. Either get mailboxes. PaStA supports raw unix-style mailboxes and public inboxes, and add them to the configuration. Use the linux project configuration as a reference. There are several possibilities to acquire mailbox data:
  3. Parse mailboxes and create local caches with ./pasta sync -mbox

To compare all mails on the list against each other:

  1. Run ./pasta analyse rep
  2. Run ./pasta rate

To compare all mails on the list against upstream:

  1. Run ./pasta analyse upstream
  2. Run ./pasta rate
  3. Your result will be stored in resources/[project]/resources/similar-mailbox
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