Exploring cheap type inference heuristics in dynamically typed languages

Milojković, Nevena; Nierstrasz, Oscar

doi:10.1145/2986012.2986017

Cited by 4 publications

(6 citation statements)

References 37 publications

(54 reference statements)

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“…ICTI was able to correctly infer the precise type of the variable in 37% of cases, and to near-695 guess it for only 8.5% more variables. To the best of our knowledge, there is one other simple approach that tries to infer the precise type for a variable [15], and it shows very similar results.…”

Section: Overall Results -Class-based Approachmentioning

confidence: 82%

“…Simpler type inference analyses [8,4] statically track variable assignments and the set of messages sent 1 to a variable in order to determine which classes either implement those methods, or inherit them from a superclass. Since they are neither control-nor data-flow sensitive, these approaches tend to 15 be less precise, but very fast. Nevertheless, the problem with these simple approaches in dynamically-typed languages is that they provide a developer with the set of unordered classes that represent possible type for a variable.…”

Section: Introductionmentioning

confidence: 99%

“…Unfortunately, this leaves the burden of looking for the correct type on the developer. To alleviate this issue, we have investigated a way of ordering the results of a simple 20 type inference algorithm by statically analysing the frequency of class usages and class instantiations in the source code [15]. In that work, the focus was only on static data, which results in missing certain types when dynamic class loading or reflection are used [16].…”

Section: Introductionmentioning

confidence: 99%

“…As types are inferred at the AST level in our other heuristics [15], we needed to leverage that information. The Opal compiler [41] provides a tool to map the bytecode program counter to AST nodes, which is normally used by the debugger [42] to highlight the code being executed.…”

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confidence: 99%

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Mining inline cache data to order inferred types in dynamic languages

Milojković

Béra

Ghafari

et al. 2018

Science of Computer Programming

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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 runtime 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.

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Section: Overall Results -Class-based Approachmentioning

confidence: 82%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Mining inline cache data to order inferred types in dynamic languages

Milojković

Béra

Ghafari

et al. 2018

Science of Computer Programming

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“…RoelTyper was improved by heuristics that derive data from static and dynamic analysis of class usage frequency [53], [54]. These heuristics more than doubled the number of correctly inferred types when compared to the underlying approach.…”

Section: Related Workmentioning

confidence: 99%

Exploiting Type Hints in Method Argument Names to Improve Lightweight Type Inference

Milojković

Ghafari

Nierstrasz

2017

2017 IEEE/ACM 25th International Conference on Program Comprehension (ICPC)

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The lack of static type information is one of the main obstacles to program comprehension in dynamically-typed languages. While static type inference algorithms try to remedy this problem, they usually suffer from the problem of false positives or false negatives. In order to partially compensate for the lack of static type information, a common practice in dynamically-typed languages is to name or annotate method arguments in such a way that they reveal their expected type, e.g., aString, anInt, or string: String. Recent studies confirmed that these type annotations are indeed frequently used by developers in dynamically-typed languages. We propose a lightweight heuristic that uses these hints from method argument names to augment the performance of a static type inference algorithm. The evaluation through a proof-ofconcept prototype implemented in Pharo Smalltalk shows that the augmented algorithm outperforms the basic algorithm, and correctly infers types for 81% more method arguments.

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