Lase: Locating and applying systematic edits by learning from examples

Adding features and fixing bugs often require systematic edits that make similar, but not identical, changes to many code locations. Finding all the relevant locations and making the correct edits is a tedious and error-prone process for developers. This paper addresses both problems using edit scripts learned from multiple examples. We design and implement a tool called Lase that (1) creates a context-aware edit script from two or more examples, and uses the script to (2) automatically identify edit locations and to (3) transform the code. We evaluate Lase on an oracle test suite of systematic edits from Eclipse JDT and SWT. Lase finds edit locations with 99% precision and 89% recall, and transforms them with 91% accuracy. We also evaluate Lase on 37 example systematic edits from other open source programs and find Lase is accurate and effective. Furthermore, we confirmed with developers that Lase found edit locations which they missed. Our novel algorithm that learns from multiple examples is critical to achieving high precision and recall; edit scripts created from only one example produce too many false positives, false negatives, or both. Our results indicate that Lase should help developers in automating systematic editing. Whereas most prior work either suggests edit locations or performs simple edits, Lase is the first to do both for nontrivial program edits.