Mining version histories to guide software changes

We apply data mining to version histories in order to guide programmers along related changes: “Programmers who changed these functions also changed... ”. Given a set of existing changes, such rules (a) suggest and predict likely further changes, (b) show up item coupling that is in-detectable by program analysis, and (c) prevent errors due to incomplete changes. After an initial change, our ROSE prototype can correctly predict 26 % of further files to be changed—and 15 % of the precise functions or variables. The topmost three suggestions contain a correct location with a likelihood of 64%. 1