Mining inline cache data to order inferred types in dynamic languages

摘要

The lack of static type information in dynamically-typed languages often poses obstacles for developers. Type inference algorithms can help, but inferring precise type information requires complex algorithms that are often slow.A simple approach that considers only the locally used interface of variables can identify potential classes for variables, but popular interfaces can generate a large number of false positives. We propose an approach calledinline-cache type inference(ICTI) to augment the precision of fast and simple type inference algorithms. ICTI uses type information available in the inline caches during multiple software runs, to provide a ranked list of possible classes that most likely represent a variable's type. We evaluate ICTI through a proof-of-concept that we implement in Pharo Smalltalk. The analysis of the top-n+2inferred types (wherenis the number of recorded run-time types for a variable) for 5486 variables from four different software systems shows that ICTI produces promising results for about 75% of the variables. For more than 90% of variables, the correct run-time type is present among first six inferred types. Our ordering shows a twofold improvement when compared with the unordered basic approach,i.e., for a significant number of variables for which the basic approach offered ambiguous results, ICTI was able to promote the correct type to the top of the list.