Inferring Types by Mining Class Usage Frequency from Inline Caches

Milojković, Nevena; Bera, Clément; Ghafari, Mohammad; Nierstrasz, Oscar

Document type :

Communication dans un congrès avec actes

DOI :

10.1145/2991041.2991047

Title :

Inferring Types by Mining Class Usage Frequency from Inline Caches

Author(s) :

Milojković, Nevena [Auteur]
Software Composition Group [Bern] [SCG]
Bera, Clément [Auteur]
Analyses and Languages Constructs for Object-Oriented Application Evolution [RMOD]
Ghafari, Mohammad [Auteur]
Software Composition Group [Bern] [SCG]
Nierstrasz, Oscar [Auteur]
Software Composition Group [Bern] [SCG]

Conference title :

Proceedings of the 11th edition of the International Workshop on Smalltalk Technologies

City :

Pragues

Country :

République tchèque

Start date of the conference :

2016

HAL domain(s) :

Informatique [cs]/Langage de programmation [cs.PL]

English abstract : [en]

Dynamically typed languages allow developers to write more expressive source code, but their lack of static information about types of variables increases the complexity of a program. Static type information about types ...
Show more >Dynamically typed languages allow developers to write more expressive source code, but their lack of static information about types of variables increases the complexity of a program. Static type information about types of variables facilitates program comprehension and maintenance. Simple type inference algorithms suffer from the problem of false positives or negatives, thus complex approaches are required to avoid this problem. We propose a simple heuristic that uses easily accessible run-time information about the usage of each class as a receiver type for a message send. This frequency serves as a proxy for the likelihood that a run-time type of the variable is that class and it is used 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 our heuristic is reasonably precise to detect correct types on average in 65.92% to 82.83% of cases.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL

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