Though the 2010s saw many research publications about languages such as JavaScript and Python, there currently appears to be a general loss of interest in dynamic languages, with popular new languages such as Rust and Zig being statically typed, and AOT ...

Cloud platforms allow applications to meet fluctuating levels of demand through automatic horizontal scaling. These deployment models are characterized by short-lived applications running in resource-constrained environments. This amplifies the ...

Julia is a dynamic language for scientific computing. For a dynamic language, Julia is surprisingly typeful. Types are used not only to structure data but also to guide dynamic dispatch – the main design tool in the language. No matter the dynamism,...

Records are finite functions from keys to values. In this work we focus on two main distinct usages of records: structs and maps. The former associate different keys to values of different types, they are accessed by providing nominal keys, and trying to ...

Applications written in dynamic languages are becoming larger and larger and companies increasingly use multi-million line codebases in production. At the same time, dynamic languages rely heavily on dynamic optimizations, particularly those that ...

We extend classic union and intersection type systems with a type-case construction and show that the combination of the union elimination rule of the former and the typing rules for type-cases of our extension encompasses occurrence typing. To apply ...

Dynamic programming languages have been embracing gradual typing, which supports optional type annotations in source code. Type-annotating a complex and long-lasting codebase is indeed a gradual and expensive process, where two issues have troubled ...

The R programming language is widely used for statistical computing. To enable interactive data exploration and rapid prototyping, R encourages a dynamic programming style. This programming style is supported by features such as first-class ...

As a scientific programming language, Julia strives for performance but also provides high-level productivity features. To avoid performance pathologies, Julia users are expected to adhere to a coding discipline that enables so-called type stability. ...

Most dynamic languages allow users to turn text into code using various functions, often named <tt>eval</tt>, with language-dependent semantics. The widespread use of these reflective functions hinders static analysis and prevents compilers from ...

Many researchers have explored type inference for dynamic languages. However, traditional type inference computes most general types which, for complex type systems—which are often needed to type dynamic languages—can be verbose, complex, and difficult ...

Dynamically-typed programming languages are often implemented using interpreters, which offer several advantages in terms of portability and flexibility of the implementation. However, as a language matures and its programs get bigger, programmers may ...

Dynamic languages, such as JavaScript, employ string-to-code primitives to turn dynamically generated text into executable code at run-time. These features make standard static analysis extremely hard if not impossible, because its essential data ...

Many researchers have explored retrofitting static type systems to dynamic languages. This raises the question of how to add type annotations to code that was previously untyped. One obvious solution is type inference. However, in complex type systems, ...

Dynamic programming languages face semantic and performance challenges in the presence of features, such as eval, that can inject new code into a running program. The Julia programming language introduces the novel concept of world age to insulate ...

The R programming language is widely used in a variety of domains. It was designed to favor an interactive style of programming with minimal syntactic and conceptual overhead. This design is well suited to data analysis, but a bad fit for tools such as ...

Adaptive software becomes more and more important as computing is increasingly context-dependent. Runtime adaptability can be achieved by dynamically selecting and applying context-specific code. Role-oriented programming has been proposed as a paradigm ...

The growing interest in deep learning has created a demand to compile computation graphs to accelerate execution and to deploy applications on various devices. Modern deep learning frameworks construct computation graphs dynamically. This gives rise to ...

Dynamic languages, such as JavaScript, employ string-to-code primitives to turn dynamically generated text into executable code at run-time. These features make standard static analysis extremely hard if not impossible because its essential data ...

Dynamic programming languages continue to increase in popularity. While just-in-time (JIT) compilation can improve the performance of dynamic programming languages, a significant performance gap remains with respect to ahead-of-time compiled languages. ...