The semantics of gradually typed languages is typically given indirectly via an elaboration into a cast calculus. This contrasts with more conventional formulations of programming language semantics, where the semantics of a language is given directly using, for instance, an operational semantics. This paper presents a new approach to give the semantics of gradually typed languages directly. We use a recently proposed variant of small-step operational semantics called type-directed operational semantics (TDOS). In a TDOS, type annotations become operationally relevant and can affect the result of a program. In the context of a gradually typed language, type annotations are used to trigger type-based conversions on values. We illustrate how to employ a TDOS on gradually typed languages using two calculi. The first calculus, called $\lambda B^{g}$, is inspired by the semantics of the blame calculus, but it has implicit type conversions, enabling it to be used as a gradually typed language. The second calculus, called $\lambda e$, explores an eager semantics for gradually typed languages using a TDOS. For both calculi, type safety is proved. For the $\lambda B^{g}$ calculus, we also present a variant with blame labels and illustrate how the TDOS can also deal with such an important feature of gradually typed languages. We also show that the semantics of $\lambda B^{g}$ with blame labels is sound and complete with respect to the semantics of the blame calculus, and that both calculi come with a gradual guarantee. All the results have been formalized in the Coq theorem prover.

We present an arrow calculus with operations and handlers and its operational and denotational semantics. The calculus is an extension of Lindley, Wadler and Yallop’s arrow calculus.

The denotational semantics is given using a strong (pro)monad $\mathcal{A}$ in the bicategory of categories and profunctors. The construction of this strong monad $\mathcal{A}$ is not trivial because of a size problem. To build denotational semantics, we investigate what $\mathcal{A}$-algebras are, and a handler is interpreted as an $\mathcal{A}$-homomorphisms between $\mathcal{A}$-algebras.

The syntax and operational semantics are derived from the observations on $\mathcal{A}$-algebras. We prove the soundness and adequacy theorem of the operational semantics for the denotational semantics.

The naive combination of polymorphic effects and polymorphic type assignment has been well known to break type safety. In the literature, there are two kinds of approaches to this problem: one is to restrict how effects are triggered and the other is to restrict how they are implemented. This work explores a new approach to ensuring the safety of the use of polymorphic effects in polymorphic type assignment. A novelty of our work is to restrict effect interfaces. To formalize our idea, we employ algebraic effects and handlers, where an effect interface is given by a set of operations coupled with type signatures. We propose signature restriction, a new notion to restrict the type signatures of operations and show that signature restriction ensures type safety of a language equipped with polymorphic effects and unrestricted polymorphic type assignment. We also develop a type-and-effect system to enable the use of both of the operations that satisfy and those that do not satisfy the signature restriction in a single program.

We study a fundamental efficiency benefit afforded by delimited control, showing that for certain higher-order functions, a language with advanced control features offers an asymptotic improvement in runtime over a language without them. Specifically, we consider the generic count problem in the context of a pure functional base language ${\lambda_{\textrm{b}}}$ and an extension ${\lambda_{\textrm{h}}}$ with general effect handlers. We prove that ${\lambda_{\textrm{h}}}$ admits an asymptotically more efficient implementation of generic count than any implementation in ${\lambda_{\textrm{b}}}$. We also show that this gap remains even when ${\lambda_{\textrm{b}}}$ is extended to a language ${{{{{{\lambda_{\textrm{a}}}}}}}}$ with affine effect handlers, which is strong enough to encode exceptions, local state, coroutines and single-shot continuations. This locates the efficiency difference in the gap between ‘single-shot’ and ‘multi-shot’ versions of delimited control.

To our knowledge, these results are the first of their kind for control operators.

Gradual typing integrates static and dynamic typing by introducing a dynamic type and a consistency relation. A problem of gradual type systems is that dynamic types can easily hide erroneous data flows since consistency relations are not transitive. Therefore, a more rigorous static check is required to reveal these hidden data flows statically. However, in order to preserve the expressiveness of gradually typed languages, static checks for gradually typed languages cannot simply reject programs with potentially erroneous data flows. By contrast, a more reasonable request is to show how these data flows can affect the execution of the program. In this paper, we propose and formalize Static Blame, a framework that can reveal hidden data flows for gradually typed programs and establish the correspondence between static-time data flows and runtime behavior. With this correspondence, we build a classification of potential errors detected from hidden data flows and formally characterize the possible impact of potential errors in each category on program execution, without simply rejecting the whole program. We implemented Static Blame on Grift, an academic gradually typed language, and evaluated the effectiveness of Static Blame by mutation analysis to verify our theoretical results. Our findings revealed that Static Blame exhibits a notable level of precision and recall in detecting type-related bugs. Furthermore, we conducted a manual classification to elucidate the reasons behind instances of failure. We also evaluated the performance of Static Blame, showing a quadratic growth in run time as program size increases.

The Knuth–Morris–Pratt (KMP) algorithm for string search is notoriously difficult to understand. Lost in a sea of index arithmetic, most explanations of KMP obscure its essence. This paper constructs KMP incrementally, using pictures to illustrate each step. The end result is easier to comprehend. Additionally, the derivation uses only elementary functional programming techniques.

Invertibility is a fundamental concept in computer science, with various manifestations in software development (serializer/deserializer, parser/printer, redo/undo, compressor/decompressor, and so on). Full invertibility necessarily requires bijectivity, but the direct approach of composing bijective functions to develop invertible programs is too restrictive to be useful. In this paper, we take a different approach by focusing on partially invertible functions—functions that become invertible if some of their arguments are fixed. The simplest example of such is addition, which becomes invertible when fixing one of the operands. More involved examples include entropy-based compression methods (e.g., Huffman coding), which carry the occurrence frequency of input symbols (in certain formats such as Huffman tree), and fixing this frequency information makes the compression methods invertible.

We develop a language Sparcl for programming such functions in a natural way, where partial invertibility is the norm and bijectivity is a special case, hence gaining significant expressiveness without compromising correctness. The challenge in designing such a language is to allow ordinary programming (the “partially” part) to interact with the invertible part freely, and yet guarantee invertibility by construction. The language Sparcl is linear-typed and has a type constructor to distinguish data that are subject to invertible computation and those that are not. We present the syntax, type system, and semantics of the language and prove that Sparcl correctly guarantees invertibility for its programs. We demonstrate the expressiveness of Sparcl with examples including tree rebuilding from preorder and inorder traversals, Huffman coding, arithmetic coding, and LZ77 compression.

We present PolyChor$\lambda$, a language for higher-order functional choreographic programming—an emerging paradigm for concurrent programming. In choreographic programming, programmers write the desired cooperative behaviour of a system of processes and then compile it into an implementation for each process, a translation called endpoint projection. Unlike its predecessor, Chor$\lambda$, PolyChor$\lambda$ has both type and process polymorphism inspired by System F$_\omega$. That is, PolyChor$\lambda$ is the first (higher-order) functional choreographic language which gives programmers the ability to write generic choreographies and determine the participants at runtime. This novel combination of features also allows PolyChor$\lambda$ processes to communicate distributed values, leading to a new and intuitive way to write delegation. While some of the functional features of PolyChor$\lambda$ give it a weaker correspondence between the semantics of choreographies and their endpoint-projected concurrent systems than some other choreographic languages, we still get the hallmark end result of choreographic programming: projected programmes are deadlock-free by design.