  Due to the growing number of cyber attacks against computer systems, we need
to pay special attention to the security of our software systems. In order to
maximize the effectiveness, excluding the human component from this process
would be a huge breakthrough. The first step towards this is to automatically
recognize the vulnerable parts in our code. Researchers put a lot of effort
into creating machine learning models that could determine if a given piece of
code, or to be more precise, a selected function, contains any vulnerabilities
or not. We aim at improving the existing models, building on previous results
in predicting vulnerabilities at the level of functions in JavaScript code
using the well-known static source code metrics. In this work, we propose to
include several so-called process metrics (e.g., code churn, number of
developers modifying a file, or the age of the changed source code) into the
set of features, and examine how they affect the performance of the
function-level JavaScript vulnerability prediction models. We can confirm that
process metrics significantly improve the prediction power of such models. On
average, we observed a 8.4% improvement in terms of F-measure (from 0.764 to
0.848), 3.5% improvement in terms of precision (from 0.953 to 0.988) and a 6.3%
improvement in terms of recall (from 0.697 to 0.760).
