  Software Analytics (SA) is a new branch of big data analytics that has
recently emerged (2011). What distinguishes SA from direct software analysis is
that it links data mined from many different software artifacts to obtain
valuable insights. These insights are useful for the decision-making process
throughout the different phases of the software lifecycle. Since SA is
currently a hot and promising topic, we have conducted a systematic literature
review, presented in this paper, to identify gaps in knowledge and open
research areas in SA. Because many researchers are still confused about the
true potential of SA, we had to filter out available research papers to obtain
the most SA-relevant work for our review. This filtration yielded 19 studies
out of 135. We have based our systematic review on four main factors: which
software practitioners SA targets, which domains are covered by SA, which
artifacts are extracted by SA, and whether these artifacts are linked or not.
The results of our review have shown that much of the available SA research
only serves the needs of developers. Also, much of the available research uses
only one artifact which, in turn, means fewer links between artifacts and fewer
insights. This shows that the available SA research work is still embryonic
leaving plenty of room for future research in the SA field.
