Design and evaluation of gradual typing for python

Vitousek, Michael M.; Kent, Andrew M.; Siek, Jeremy G.; Baker, J. Wesley

doi:10.1145/2661088.2661101

Cited by 82 publications

(28 citation statements)

References 32 publications

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“…On the practical side there is a growing number of programming languages adopting gradual typing. Those languages include Clojure [6], Python [27], TypeScript [5], Hack [26], and the addition of Dynamic to C# [4], to cite a few. On the theoretical side, recent years have seen a large body of research that defines the foundations of gradual typing [8,9,13], explores their use for both functional and object-oriented programming [21,22], as well as its applications to many other areas [3,24].…”

Section: Introductionmentioning

confidence: 99%

Consistent Subtyping for All

Xie

Oliveira

2018

Programming Languages and Systems

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Abstract. Consistent subtyping is employed in some gradual type systems to validate type conversions. The original definition by Siek and Taha serves as a guideline for designing gradual type systems with subtyping. Polymorphic typesà la System F also induce a subtyping relation that relates polymorphic types to their instantiations. However Siek and Taha's definition is not adequate for polymorphic subtyping. The first goal of this paper is to propose a generalization of consistent subtyping that is adequate for polymorphic subtyping, and subsumes the original definition by Siek and Taha. The new definition of consistent subtyping provides novel insights with respect to previous polymorphic gradual type systems, which did not employ consistent subtyping. The second goal of this paper is to present a gradually typed calculus for implicit (higher-rank) polymorphism that uses our new notion of consistent subtyping. We develop both declarative and (bidirectional) algorithmic versions for the type system. We prove that the new calculus satisfies all static aspects of the refined criteria for gradual typing, which are mechanically formalized using the Coq proof assistant.

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Section: Introductionmentioning

confidence: 99%

Consistent Subtyping for All

Xie

Oliveira

2018

Programming Languages and Systems

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“…Reticulated Python [47] includes runtime checks for annotated types using three different methods. The two bugs described in [47] which were caught using Reticulated Python would have occurred as exceptions without the runtime checks; with the present work, we are more interested in bugs which might have otherwise gone undetected.…”

Section: Related Workmentioning

confidence: 99%

Refinement Type Contracts for Verification of Scientific Investigative Software

Shinn

2020

Lecture Notes in Computer Science

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Our scientific knowledge is increasingly built on software output. User code which defines data analysis pipelines and computational models is essential for research in the natural and social sciences, but little is known about how to ensure its correctness. The structure of this code and the development process used to build it limit the utility of traditional testing methodology. Formal methods for software verification have seen great success in ensuring code correctness but generally require more specialized training, development time, and funding than is available in the natural and social sciences. Here, we present a Python library which uses lightweight formal methods to provide correctness guarantees without the need for specialized knowledge or substantial time investment. Our package provides runtime verification of function entry and exit condition contracts using refinement types. It allows checking hyperproperties within contracts and offers automated test case generation to supplement online checking. We co-developed our tool with a mediumsized (≈3000 LOC) software package which simulates decision-making in cognitive neuroscience. In addition to helping us locate trivial bugs earlier on in the development cycle, our tool was able to locate four bugs which may have been difficult to find using traditional testing methods. It was also able to find bugs in user code which did not contain contracts or refinement type annotations. This demonstrates how formal methods can be used to verify the correctness of scientific software which is difficult to test with mainstream approaches.

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“…Gradual typing [31] lets developers add types gradually as programs evolve; Vitousek et al recently implemented gradual typing for Python [39]. Like types [41] bring some of the flexibility of dynamic typing to statically typed languages.…”

Section: Related Workmentioning

confidence: 99%

Just-in-time static type checking for dynamic languages

Ren

Foster

2016

SIGPLAN Not.

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Dynamic languages such as Ruby, Python, and JavaScript have many compelling benefits, but the lack of static types means subtle errors can remain latent in code for a long time. While many researchers have developed various systems to bring some of the benefits of static types to dynamic languages, prior approaches have trouble dealing with metaprogramming, which generates code as the program executes. In this paper, we propose Hummingbird, a new system that uses a novel technique, just-in-time static type checking, to type check Ruby code even in the presence of metaprogramming. In Hummingbird, method type signatures are gathered dynamically at run-time, as those methods are created. When a method is called, Hummingbird statically type checks the method body against current type signatures. Thus, Hummingbird provides thorough static checks on a per-method basis, while also allowing arbitrarily complex metaprogramming. For performance, Hummingbird memoizes the static type checking pass, invalidating cached checks only if necessary. We formalize Hummingbird using a core, Ruby-like language and prove it sound. To evaluate Hummingbird, we applied it to six apps, including three that use Ruby on Rails, a powerful framework that relies heavily on metaprogramming. We found that all apps typecheck successfully using Hummingbird, and that Hummingbird's performance overhead is reasonable. We applied Hummingbird to earlier versions of one Rails app and found several type errors that had been introduced and then fixed. Lastly, we demonstrate using Hummingbird in Rails development mode to typecheck an app as live updates are applied to it.

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Design and evaluation of gradual typing for python

Cited by 82 publications

References 32 publications

Consistent Subtyping for All

Consistent Subtyping for All

Refinement Type Contracts for Verification of Scientific Investigative Software

Just-in-time static type checking for dynamic languages

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