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Core data sources include [[source code]], "check-ins, work items, bug reports and test executions [...] recorded in software repositories such as CVS, Subversion, GIT, and Bugzilla."<ref>Harald Gall, Tim Menzies, Laurie Williams, and Thomas Zimmerman. "Software Development Analytics". Dagstuhl Reports, Vol. 4, Issue 6, pp. 64-83.</ref> [[telemetry | Telemetry data]] as well as execution traces or logs can also be taken into account.
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. For example, software analytics tools allow users to map derived analysis results by means of [[software map]]s, which support interactively exploring system artifacts and correlated software metrics. There are also software analytics tools using analytical technologies on top of [[software quality]] models in [[agile software development]] companies, which supports assessing software qualities (e.g., reliability), and derive actions for their improvement <ref>{{Cite journal|last=Martínez-Fernández|first=Silverio|last2=Vollmer|first2=Anna Maria|last3=Jedlitschka|first3=Andreas|last4=Franch|first4=Xavier|last5=Lopez|first5=Lidia|last6=Ram|first6=Prabhat|last7=Rodriguez|first7=Pilar|last8=Aaramaa|first8=Sanja|last9=Bagnato|first9=Alessandra|date=2019|title=Continuously assessing and improving software quality with software analytics tools: a case study
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