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Mining inline cache data to order inferred types in dynamic languages.

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Title: Mining inline cache data to order inferred types in dynamic languages.
Authors: Milojković, Nevena1 nevena@inf.unibe.ch, Béra, Clément2, Ghafari, Mohammad1, Nierstrasz, Oscar1
Source: Science of Computer Programming. Sep2018, Vol. 161, p105-121. 17p.
Subjects: Dylan (Computer program language), Cache memory, Algorithms, Smalltalk (Computer program language), Run time systems (Computer science)
Abstract: 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 called inline-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 + 2 inferred types (where n is 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. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract: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 called inline-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 + 2 inferred types (where n is 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. [ABSTRACT FROM AUTHOR]
ISSN:01676423
DOI:10.1016/j.scico.2017.11.003