trace2vary - An Algorithm to Recover Feature-Code Traceability and Variability in Variant-Rich Systems

About

trace2vary is a traceability recovery algorithm for variant-rich systems written in Python. It takes machine learning methods to improve the precision and recall during the indication of relevance between a feature and a source code file.

Methods

trace2vary has two machine learning algorithm options when recovering feature-source code file links:

• SGD: it refers to the Stochastic Gradient Descent method.
• AutoML: it refers to the automatic identification of the machine learning method.

How to use

Dataset preparation

Before running the trace2vary command, you have to unzip the following files (they are compressed due to GitHub file size constraints):

• projects/openldap-2.4.45/data.csv.zip
• projects/freebsd/data.csv.zip
• projects/netbsd/data.csv.zip
• projects/openbsd/data.csv.zip

In addition, the following python packages have to be installed:

• nltk
• pandas
• numpy
• scipy
• scikit-learn
• auto_ml
• bokeh
• python-Levenshtein

Command syntax

You must comply with the following command syntax:

python3 trace2vary.py <options> [-default]

<options>
        -p      Apply pre-processing for the project to be analyzed.
        -fit    Fit the machine learning model considering the training set. It requires the [method] argument.
        -tr     Recovers the traces for the analyzed project.
        -cv     Performs the commonality and variability (C & V) analysis for the stated products'projects.
        -vis    Starts the visualization client for the *trace2vary* output file.
        -core   Runs the -tr (with the default method), -cv and -vis tasks in a sequential way.
        -pre    Prepares *trace2vary* for the core tasks, running the -p and -fit tasks with the default method.

[-default]
        It runs only the default method, Stochastic Gradient Descent, for the requested task.
        This is only applied to the -fit and -tr options,
        since they do not use a machine learning algorithm to accomplish the task.

Examples:
        python3 trace2vary.py -core
        python3 trace2vary.py -pre
        python3 trace2vary.py -p
        python3 trace2vary.py -fit
        python3 trace2vary.py -fit -default
        python3 trace2vary.py -tr
        python3 trace2vary.py -tr -default
        python3 trace2vary.py -cv
        python3 trace2vary.py -vis