Nevena Milojković and Oscar Nierstrasz. Exploring Cheap Type Inference Heuristics in Dynamically Typed Languages. In Proceedings of the 2016 ACM International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software, Onward! 2016, p. 43—56, ACM, New York, NY, USA, 2016. Details.
Although dynamically typed languages allow developers to be more productive in writing source code, their lack of information about types of variables is one of the main obstacles during program comprehension. Static type information helps developers to decrease software maintenance time. Inferring the types of variables requires complex algorithms in order to avoid false positives or negatives. Their main aim is to shorten the list of possible types for a variable. We have developed a couple of cheap heuristics that use easily accessible information about the presence of each class in the available source code to promote the correct type towards the top of the list of possible types for a variable. Our evaluation of a proof-of-concept prototype, implemented in Pharo Smalltalk, shows that both for domain- specific types and standard libraries these heuristics tend to work well. The evaluated heuristics prove to be reasonably precise, promoting the correct types of a variable towards the top of the list in 50.67% up to 89.09% of cases on average, depending on the applied heuristic. The heuristic that has proven to be the best was compared with one existing type inference algorithm and the best heuristic yields significantly better results with less effort.