During the life span of large software projects, developers often apply the same code changes to different code locations in slight variations. Since the application of these changes to all locations is time-consuming and error-prone, tools exist that learn change patterns from input examples, search for possible pattern applications, and generate corresponding recommendations. In many cases, the generated recommendations are syntactically or semantically wrong due to code movements in the input examples. Thus, they are of low accuracy and developers cannot directly copy them into their projects without adjustments.

When it is necessary to express changes between two source code files as a list of edit actions (an edit script), modern tree differencing algorithms are superior to most text-based approaches because they take code movements into account and express source code changes more accurately. We present 5 general optimizations that can be added to state-of-the-art tree differencing algorithms to shorten the resulting edit scripts. Applied to Gumtree, RTED, JSync, and ChangeDistiller, they lead to shorter scripts for 18-98% of the changes in the histories of 9 open-source software repositories. These optimizations also are parts of our novel Move-optimized Tree DIFFerencing algorithm (MTDIFF) that has a higher accuracy in detecting moved code parts. MTDIFF (which is based on the ideas of ChangeDistiller) further shortens the edit script for another 20% of the changes in the repositories. MTDIFF and all the benchmarks are available under an open-source license.

Several research tools and projects require groups of similar code changes as input. Examples are recommendation and bug finding tools that can provide valuable information to developers based on such data. With the help of similar code changes they can simplify the application of bug fixes and code changes to multiple locations in a project. But despite their benefit, the practical value of existing tools is limited, as users need to manually specify the input data, i.e., the groups of similar code changes.