Twenty years ago we published a paper titled “Mining Version Histories to Guide Software Changes” in the IEEETransactions of Software Engineering. The paper is considered to be one of the seminal papers of the mining software repositories (MSR) field. In this retrospective, we reflect on the original work, the field of mining software repositories and its community, and its impact on software engineering.

While there is a considerable amount of research on analyzing the change information stored in software repositories, only few researcher have looked at software changes contained in email archives in form of patches. In this paper we look at the email archives of two open source projects and answer questions like the following: How many emails contain patches? How long does it take for a patch to be accepted? Does the size of the patch influence its chances to be accepted or the duration until it gets accepted? Obviously, the answers to these questions can be helpful for the authors of patches, in particular because some of the answers are surprising.

Software has been and is still mostly refactored without tool support. Moreover, as we found in our case studies, programmers tend not to document these changes as refactorings, or even worse label changes as refactorings, although they are not. In this paper we present a technique to detect changes that are likely to be refactorings and rank them according to the likelihood. The evaluation shows that the method has both a high recall and a high precision - it finds most of the refactorings, and most of the found refactoring candidates are really refactorings

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, the mined association rules 1) suggest and predict likely further changes, 2) show up item coupling that is undetectable by program analysis, and 3) can prevent errors due to incomplete changes. After an initial change, our ROSE prototype can correctly predict further locations to be changed; the best predictive power is obtained for changes to existing software. In our evaluation based on the history of eight popular open source projects, ROSE's topmost three suggestions contained a correct location with a likelihood of more than 70 percent.

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 indetectable 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%.