  Two key contributions presented in this paper are: i) A method for building a
dataset containing source code features extracted from source files taken from
Open Source Software (OSS) and associated bug reports, ii) A predictive model
for estimating defectiveness of a given source code. These artifacts can be
useful for building tools and techniques pertaining to several automated
software engineering areas such as bug localization, code review, and
recommendation and program repair.
  In order to achieve our goal, we first extract coding style information (e.g.
related to programming language constructs used in the source code) for source
code files present on GitHub. Then the information available in bug reports (if
any) associated with these source code files are extracted. Thus fetched un(/
semi)-structured information is then transformed into a structured knowledge
base. We considered more than 30400 source code files from 20 different GitHub
repositories with about 14950 associated bug reports across 4 bug tracking
portals. The source code files considered are written in four programming
languages (viz., C, C++, Java, and Python) and belong to different types of
applications.
  A machine learning (ML) model for estimating the defectiveness of a given
input source code is then trained using the knowledge base. In order to pick
the best ML model, we evaluated 8 different ML algorithms such as Random
Forest, K Nearest Neighbour and SVM with around 50 parameter configurations to
compare their performance on our tasks. One of our findings shows that best
K-fold (with k=5) cross-validation results are obtained with the NuSVM
technique that gives a mean F1 score of 0.914.
