Software analytics aims at supporting decisions and generating insights, i.e., findings, conclusions, and evaluations about software systems and their implementation, composition, behavior, quality, evolution as well as about the activities of various stakeholders of these processes.
Methods, techniques, and tools of software analytics typically rely on gathering, measuring, analyzing, and visualizing information found in the manifold data sources stored in software development environments and ecosystems.
Automated analysis, massive data, and systematic reasoning support decision-making at almost all levels.
In general, key technologies employed by software analytics include analytical technologies such as machine learning, data mining, statistics, pattern recognition, information visualization as well as large-scale data computing & processing.
[5] In 2009, the term "software analytics" was used in a paper by Dongmei Zhang, Shi Han, Yingnong Dang, Jian-Guang Lou, and Haidong Zhang in part by the Software Analytics Group (SA) at Microsoft Research Asia (MSRA).