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Improving the Precision of Type Inference Algorithms with Lightweight Heuristics

Type of publication Peer-reviewed
Publikationsform Proceedings (peer-reviewed)
Author Milojković Nevena,
Project Agile Software Analysis
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Proceedings (peer-reviewed)

Title of proceedings SATToSE'17: Pre-Proceedings of the 10th International Seminar Series on Advanced Techniques & Tools for Software Evolution
Place Madrid

Open Access

Type of Open Access Website


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.