Applied Software Forensics - Utilities

31 Dec 2022

The following methods, mechanisms and tool are useful in many steps of the software analysis:

Convert CSV File to JSON

The Code Maat and Maat Scripts usually result in csv formatted tables. In this static website however, JSON formatted data is passed into the liquid templates. Using the csv2json script allows to convert the data files:

# Prerequisite: Install the d3-dsv npm package. It brings the csv2json command
npm install -g d3-dsv
csv2json -o hotspots.json hotspots.csv

For more information, see section “Convert a CSV file with Headers to JSON” of the jq Cookbook.

Combine Multiple git History Files Into One

Some analyses require (work best) using a git history for multiple repositories making up the system. This history file can be generated from individual history files as follows:

MODULES="frontend server components"

cd ../analysis

# Combine all git history files into one
rm all_evo.log;  \
for SUT in $MODULES; do \
  cat ${SUT}_evo.log | awk -F '\t' "{ if (NF == 3) { print \$1 \"\\t\" \$2 \"\\t\" \"$SUT/\" \$3 } else { print \$0 } }" >> all_evo.log; \
  echo "" >> all_evo.log; \
done

Filter CSV Files

During the coupling analysis we are interested in specific entries of a CSV file. We use the Rainbow Query Language (RBQL) (npm module rbql) to filter the CSV file:

npm install -g rbql

export QUERY="SELECT * WHERE LIKE(a1, '%view/ViewPatient%') || LIKE(a2, '%view/ViewPatient%') ORDER BY a3, a4 DESC WITH (header)"; \
rbql-js --delim "," --query "$QUERY" < all_coupling.csv > viewpatient_coupling.csv

Analyse Development Cycles and Author Contributions with Gitstats

# Prerequisite: Get gitstats (requires gnuplot and python)
brew install gnuplot

# Clone gitstats and export its path to an environment variable
git clone https://github.com/gktrk/gitstats.git
export GITSTATS=$HOME/source/gktrk/gitstats

Next: Read the corresponding blog post on this site.