Improving the Precision of Type Inference Algorithms with Lightweight Heuristics

Dynamically-typed languages allow faster software development by not posing the type constraints. Static type information facilitates program comprehension and software maintenance. Type inference algorithms attempt to reconstruct the type information from the code, yet they suffer from the problem of false positives or false negatives. The use of complex type inference algorithms is questionable during the development phase, due to their performance costs. Instead, we propose lightweight heuristics to improve simple type inference algorithms and, at the same time, preserve their swiftness.