Abstracting gradual typing moving forward: precise and space-efficient

Gradual typing allows programs to enjoy the benefits of both static typing and dynamic typing. While it is often desirable to migrate a program from more dynamically typed to more statically typed or vice versa, gradual typing itself does not provide a way to facilitate this migration. This places the burden on programmers who have to manually add or remove type annotations. Besides the general challenge of adding type annotations to dynamically typed code, there are subtle interactions between these annotations in gradually typed code that exacerbate the situation. For example, to migrate a program to be as static as possible, in general, all possible combinations of adding or removing type annotations from parameters must be tried out and compared. In this paper, we address this problem by developing migrational typing , which efficiently types all possible ways of replacing dynamic types with fully static types for a gradually typed program. The typing result supports automatically migrating a program to be as static as possible or introducing the least number of dynamic types necessary to remove a type error. The approach can be extended to support user-defined criteria about which annotations to modify. We have implemented migrational typing and evaluated it on large programs. The results show that migrational typing scales linearly with the size of the program and takes only 2–4 times longer than plain gradual typing.

Gradual typing lets programmers evolve their dynamically typed programs by gradually adding explicit type annotations, which confer benefits like improved performance and fewer run-time failures. However, we argue that such evolution often requires a giant leap, and that type inference can offer a crucial missing step. If omitted type annotations are interpreted as unknown types, rather than the dynamic type, then static types can often be inferred, thereby removing unnecessary assumptions of the dynamic type. The remaining assumptions of the dynamic type may then be removed by either reasoning outside the static type system, or restructuring the code. We present a type inference algorithm that can improve the performance of existing gradually typed programs without introducing any new run-time failures. To account for dynamic typing, types that flow in to an unknown type are treated in a fundamentally different manner than types that flow out. Furthermore, in the interests of backward-compatibility, an escape analysis is conducted to decide which types are safe to infer. We have implemented our algorithm for ActionScript, and evaluated it on the SunSpider and V8 benchmark suites. We demonstrate that our algorithm can improve the performance of unannotated programs as well as recover most of the type annotations in annotated programs.

Static type systems are essential in computer science. However, there is hardly any knowledge about the impact of type systems on the resulting piece of software. While there are authors that state that static types increase the development speed, other authors argue the other way around. A previous experiment suggests that there are multiple factors that play a role for a comparison of statically and dynamically typed language. As a follow-up, this paper presents an empirical study with 21 subjects that compares programming tasks performed in Java and Groovy - programming tasks where the number of expected type casts vary in the statically typed language. The result of the study is, that the dynamically typed group solved the complete programming tasks significantly faster for most tasks - but that for larger tasks with a higher number of type casts no significant difference could be found.

Static and dynamic typing have different strengths and weaknesses. Gradual typing allows a programmer to combine statically and dynamically typed sections of code in one program. This allows the best typing choice for each part of the code. Sadly, the advantages of efficient program execution that come with static typing do not yet carry over to a gradual typing system. This is because no optimizations have yet been proposed that take advantage of the type information present in this context. In this work, we propose optimizations for gradually typed applications, focused on four areas: optimizing method lookup, optimizing type checking, breaking encapsulation and improving infrastructure. We also propose how we are going to validate these optimizations.

Some trade-offs in reducing the overhead of assertion run-time checks via static analysis

Developers who write code in a statically typed language are denied the ability to obtain dynamic feedback by executing their code during periods when it fails the static type checker. They are further confined to the static typing discipline during times in the development process where it does not yield the highest productivity. If they opt instead to use a dynamic language, they forgo the many benefits of static typing, including machine-checked documentation, improved correctness and reliability, tool support (such as for refactoring), and better runtime performance. We present a novel approach to giving developers the benefits of both static and dynamic typing, throughout the development process, and without the burden of manually separating their program into statically and dynamically-typed parts. Our approach, which is intended for temporary use during the development process, relaxes the static type system and provides a semantics for many type-incorrect programs. It defers type errors to run time, or suppresses them if they do not affect runtime semantics. We implemented our approach in a publicly available tool, DuctileJ, for the Java language. In case studies, DuctileJ conferred benefits both during prototyping and during the evolution of existing code.

This special issue of EMSE comprises four selected papers on program comprehension, an unusually wide-ranging research area within software engineering. It encompasses the human activities of comprehending software, the technologies for supporting the program comprehension, and their evaluation. It is notable for combining aspects of cognitive psychology, tool design, and empirical study of users, tools, and software artifacts. In “High-MCC Functions in the Linux Kernel”, the authors challenge the assumption that functions with high values of McCabe Cyclomatic Complexity (MCC) are likely to be problematic to understand and maintain. They explore the nuances of what a high MCC value might mean, and examine functions from the Linux kernel with high MCC values. They find that often the structure of these functions is found to be easy to understand by users, leading to the conclusion that syntactic measures alone may not be a good metric for understandability. In “Measuring and Modeling Programming Experience”, the authors describe a controlled experiment in assessing how the experience of software developers can best be measured. This is a particularly important problem in program comprehension research, as developer experience is often a key factor that must be explicitly taken into account when studying how programmers perform problem-solving tasks. In “An Empirical Study on the Impact of Static Typing on Software Maintainability”, the authors tackle one of the classic programming language debates: How does static typing affect human software development capabilities? The authors approach this question from an empirical perspective. In a carefully designed experiment, the authors assess the impact of static typing on understanding undocumented code, fixing type errors, and fixing semantic errors. The results show rigorous empirical evidence that static types are indeed beneficial to these activities, except when fixing semantic errors. In “Labeling Source Code with Information Retrieval Methods: An Empirical Study”, the authors address augmenting source code with explanatory labels. Automated techniques for this exist, which are based on, e.g., Vector Space Models, Latent Semantic Indexing (LSI), and latent Dirichlet allocation (LDA). The authors describe an experiment in which such Empir Software Eng (2014) 19:1259–1260 DOI 10.1007/s10664-014-9329-5

Dynamically typed languages trade flexibility and ease of use for safety, while statically typed languages prioritize the early detection of bugs, and provide a better framework for structure large programs. The idea of optional typing is to combine the two approaches in the same language: the programmer can begin development with dynamic types, and migrate to static types as the program matures. The challenge is designing a type system that feels natural to the programmer that is used to programming in a dynamic language.This paper presents the initial design of Typed Lua, an optionally-typed extension of the Lua scripting language. Lua is an imperative scripting language with first class functions and lightweight metaprogramming mechanisms. The design of Typed Lua's type system has a novel combination of features that preserves some of the idioms that Lua programmers are used to, while bringing static type safety to them. We show how the major features of the type system type these idioms with some examples, and discuss some of the design issues we faced.

Combining static and dynamic typing within the same language offers clear benefits to programmers. It provides dynamic typing in situations that require rapid prototyping, heterogeneous data structures, and reflection, while supporting static typing when safety, modularity, and efficiency are primary concerns. Siek and Taha (2006) introduced an approach to combining static and dynamic typing in a fine-grained manner through the notion of type consistency in the static semantics and run-time casts in the dynamic semantics. However, many open questions remain regarding the semantics of gradually typed languages. In this paper we present Reticulated Python, a system for experimenting with gradual-typed dialects of Python. The dialects are syntactically identical to Python 3 but give static and dynamic semantics to the type annotations already present in Python 3. Reticulated Python consists of a typechecker and a source-to-source translator from Reticulated Python to Python 3. Using Reticulated Python, we evaluate a gradual type system and three approaches to the dynamic semantics of mutable objects: the traditional semantics based on Siek and Taha (2007) and Herman et al. (2007) and two new designs. We evaluate these designs in the context of several third-party Python programs.

Instrumenting programs for performing runtime checking of properties, such as regular shapes, is a common and useful technique that helps programmers detect incorrect program behaviors. This is specially true in dynamic languages such as Prolog. However, such runtime checks inevitably introduce runtime overhead (in execution time, memory, energy, etc.). Several approaches have been proposed for reducing this overhead, such as eliminating the checks that can statically be proved to always succeed, and/or optimizing the way in which the (remaining) checks are performed. However, there are cases in which it is not possible to remove all checks statically (e.g., open libraries which must check their interfaces, complex properties, unknown code, etc.) and in which, even after optimizations, these remaining checks may still introduce an unacceptable level of overhead. It is thus important for programmers to be able to determine the additional cost due to the runtime checks and compare it to some notion of cost. The common practice used for estimating runtime checking overhead is profiling, which is not exhaustive by nature. Instead, we propose a method that uses static analysis to estimate such overhead, with the advantage that the estimations are functions parameterized by input data sizes. Unlike profiling, this approach can provide guarantees for all possible execution traces, and allows assessing how the overhead grows as the size of the input grows. Our method also extends an existing assertion verification framework to express admissible overheads, and statically and automatically checks whether the instrumented program conforms with such specifications. Finally, we present an experimental evaluation of our approach that suggests that our method is feasible and promising.

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 types à 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 article 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 article 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. The algorithmic version employs techniques developed by Dunfield and Krishnaswami for higher-rank polymorphism to deal with instantiation. We prove that the new calculus satisfies all static aspects of the refined criteria for gradual typing. We also study an extension of the type system with static and gradual type parameters, in an attempt to support a variant of the dynamic criterion for gradual typing. Assuming a coherence conjecture for the extended calculus, we show that the dynamic gradual guarantee of our source language can be reduced to that of λ B, which, at the time of writing, is still an open question. Most of the metatheory of this article, except some manual proofs for the algorithmic type system and extensions, has been mechanically formalized using the Coq proof assistant.

Gradual typing is an approach to integrating static and dynamic type checking within the same language [Siek and Taha 2006]. Given the name typing, one might think that the most interesting aspect is the type system. It turns out that the dynamic semantics of gradually-typed languages is more complex than the static semantics, with many points in the design space [Wadler and Findler 2009; Siek et al. 2009] and many challenges concerning efficiency [Herman et al. 2007; Hansen 2007; Siek and Taha 2007; Siek and Wadler 2010; Wrigstad et al. 2010; Rastogi et al. 2012]. In this distilled tutorial, we explore the meaning of gradual typing and the challenges to efficient implementation by writing several definitional interpreters and abstract machines in Scheme for the gradually-typed lambda calculus.

Whereas dynamic typing enables rapid prototyping and easy experimentation, static typing provides early error detection and better compile time optimization. Gradual typing [26] provides the best of both worlds. This paper shows how to define and implement gradual typing in Dylan, traditionally a dynamically typed language. Dylan poses several special challenges for gradual typing, such as multiple return values, variable-arity methods and generic functions (multiple dispatch).In this paper Dylan is extended with function types and parametric polymorphism. We implemented the type system and aunification-based type inference algorithm in the mainstream Dylan compiler. As case study we use the Dylan standard library (roughly 32000 lines of code), which witnesses that the implementation generates faster code with fewer errors. Some previously undiscovered errors in the Dylan library were revealed.

In this paper we propose a scheme that combines type inference and run-time checking to make existing C programs type safe. We describe the CCured type system, which extends that of C by separating pointer types according to their usage. This type system allows both pointers whose usage can be verified statically to be type safe, and pointers whose safety must be checked at run time. We prove a type soundness result and then we present a surprisingly simple type inference algorithm that is able to infer the appropriate pointer kinds for existing C programs.Our experience with the CCured system shows that the inference is very effective for many C programs, as it is able to infer that most or all of the pointers are statically verifiable to be type safe. The remaining pointers are instrumented with efficient run-time checks to ensure that they are used safely. The resulting performance loss due to run-time checks is 0-150%, which is several times better than comparable approaches that use only dynamic checking. Using CCured we have discovered programming bugs in established C programs such as several SPECINT95 benchmarks.

Developers using statically typed languages such as C $^\sharp$ and Java are increasingly having to interoperate with APIs and object models defined in dynamic languages. This impedance mismatch results in code that is difficult to understand, awkward to analyze, and expensive to maintain. In this paper we describe new features in C $^\sharp$ 4.0 that support the safe combination of dynamically and statically typed code by deferring type checking of program fragments with static type dynamic until runtime. When executed, these dynamic code fragments are type-checked and resolved using the same rules as statically typed code. We formalize these features in a core fragment of C $^\sharp$ and prove important safety properties. In particular, we show that subtyping remains transitive.