As the game industry continues to evolve in pop-ularity, testing the experience of players becomes crucial for attracting and retaining players in the highly competitive market. However, the absence of automated methods for articulating and verifying player experience (PX) specifications led us to introduce EmoSTL, a specialized language that extends Linear Temporal Logic with spatial and time-interval expressions, enabling the capture of complex temporal and spatial aspects of players' emotions and their experiences within games. We conducted a user study to collect suggestive PX requirements for a game under test to assess the capabilities of EmoSTL. Findings reveal that the language formalizes 92 percent of the set PX requirements, and with runtime verification, several PX design issues are iden-tified in the game. Moreoever, EmoSTL performance evaluation demonstrates its linear execution time, showcasing the language potential usage in automated PX testing of games.

As video games become more complex and widespread, player experience (PX) testing becomes crucial in the game industry. Attracting and retaining players are key elements to guarantee the success of a game in the highly competitive market. Although a number of techniques have been introduced to measure the emotional aspect of the experience, automated testing of player experience still needs to be explored. This paper presents PX-MBT, a framework for automated player experience testing with emotion pattern verification. PX-MBT (1) utilizes a model-based testing approach for test suite generation, (2) employs a computational model of emotions developed based on a psychological theory of emotions to model players' emotions during game-plays with an intelligent agent, and (3) verifies emotion patterns given by game designers on executed test suites to identify PX-issues. We explain PX-MBT architecture and provide an example along with its result in emotion pattern verification, which asserts the evolution of emotions over time, and heat-maps to showcase the spatial distribution of emotions on the game map.

Compiler correctness is an old problem, but with the emergence of smart contracts on blockchains that problem presents itself in a new light. Smart contracts are self-contained pieces of software that control (valuable) assets in an adversarial environment; once committed to the blockchain, these smart contracts cannot be modified. Smart contracts are typically developed in a high-level contract language and compiled to low-level virtual machine code before being committed to the blockchain. For a smart contract user to trust a given piece of low-level code on the blockchain, they must convince themselves that (a) they are in possession of the matching source code and (b) that the compiler has correctly translated the source code to the given low-level code. Classic approaches to compiler correctness tackle the second point. We argue that translation certification also squarely addresses the first. We describe the proof architecture of a translation certification framework and demonstrate how we can model the compilation pipeline as a sequence of translation relations. We give a detailed account of such relations for most passes of the Plutus Tx compiler, which we formalised in Coq. This approach facilitates a modular verification methodology and is robust in the face of an evolving compiler implementation.

Abstract Player eXperience (PX) testing has attracted attention in the game industry as video games become more complex and widespread. Understanding players’ desires and their experience are key elements to guarantee the success of a game in the highly competitive market. Although a number of techniques have been introduced to measure the emotional aspect of the experience, automated testing of player experience still needs to be explored. This paper presents a framework for automated player experience testing by formulating emotion patterns’ requirements and utilizing a computational model of players’ emotions developed based on a psychological theory of emotions along with a model-based testing approach for test suite generation. We evaluate the strength of our framework by performing mutation test. The paper also evaluates the performance of a search-based generated test suite and LTL model checking-based test suite in revealing various variations of temporal and spatial emotion patterns. Results show the contribution of both algorithms in generating complementary test cases for revealing various emotions in different locations of a game level.

Systems programmers need fine-grained control over the memory layout of data structures, both to produce performant code and to comply with well-defined interfaces imposed by existing code, standardised protocols or hardware. Code that manipulates these low-level representations in memory is hard to get right. Traditionally, this problem is addressed by the implementation of tedious marshalling code to convert between compiler-selected data representations and the desired compact data formats. Such marshalling code is error-prone and can lead to a significant runtime overhead due to excessive copying. While there are many languages and systems that address the correctness issue, by automating the generation and, in some cases, the verification of the marshalling code, the performance overhead introduced by the marshalling code remains. In particular for systems code, this overhead can be prohibitive. In this work, we address both the correctness and the performance problems. We present a data layout description language and data refinement framework, called Dargent, which allows programmers to declaratively specify how algebraic data types are laid out in memory. Our solution is applied to the Cogent language, but the general ideas behind our solution are applicable to other settings. The Dargent framework generates C code that manipulates data directly with the desired memory layout, while retaining the formal proof that this generated C code is correct with respect to the functional semantics. This added expressivity removes the need for implementing and verifying marshalling code, which eliminates copying, smoothens interoperability with surrounding systems, and increases the trustworthiness of the overall system.

Haskell is a popular choice for hosting deeply embedded languages. A recurring challenge for these embeddings is how to seamlessly integrate user defined algebraic data types. In particular, one important, convenient, and expressive feature for creating and inspecting data—pattern matching—is not directly available on embedded terms. We present a novel technique, embedded pattern matching, which enables a natural and user friendly embedding of user defined algebraic data types into the embedded language, and allows programmers to pattern match on terms in the embedded language in much the same way they would in the host language.

Property-based testing (PBT) is a powerful tool that is widely available in modern programming languages. It has been used to reduce formal software verification effort. We demonstrate how PBT can be used in conjunction with formal verification to incrementally gain greater assurance in code correctness by integrating PBT into the verification framework of Cogent---a programming language equipped with a certifying compiler for developing high-assurance systems components. Specifically, for PBT and formal verification to work in tandem, we structure the tests to mirror the refinement proof that we used in Cogent's verification framework: The expected behaviour of the system under test is captured by a functional correctness specification, which mimics the formal specification of the system, and we test the refinement relation between the implementation and the specification. We exhibit the additional benefits that this mutualism brings to developers and demonstrate the techniques we used in this style of PBT, by studying two concrete examples.

Abstract This paper presents a framework aimed at significantly reducing the cost of proving functional correctness for low-level operating systems components. The framework is designed around a new functional programming language, Cogent. A central aspect of the language is its uniqueness type system, which eliminates the need for a trusted runtime or garbage collector while still guaranteeing memory safety, a crucial property for safety and security. Moreover, it allows us to assign two semantics to the language: The first semantics is imperative, suitable for efficient C code generation, and the second is purely functional, providing a user-friendly interface for equational reasoning and verification of higher-level correctness properties. The refinement theorem connecting the two semantics allows the compiler to produce a proof via translation validation certifying the correctness of the generated C code with respect to the semantics of the Cogent source program. We have demonstrated the effectiveness of our framework for implementation and for verification through two file system implementations.

Functional languages are uniquely suited to providing programmers with a programming model for parallel and concurrent computing. This is reflected in the wide range of work that is currently underway, both on parallel and concurrent functional languages, as well as on bringing functional language features to other programming languages. This has resulted in a rapidly growing number of practical applications. The Journal of Functional Programming decided to dedicate a special issue to this field to showcase the state of the art in how functional languages and functional concepts currently assist programmers with the task of managing the challenges of creating parallel and concurrent systems.

Previous work has demonstrated that it is possible to generate efficient and highly parallel code for multicore CPUs and GPUs from combinator-based array languages for a range of applications. That work, however, has been limited to operating on flat, rectangular structures without any facilities for irregularity or nesting.

Property-based testing can play an important role in reducing the cost of formal verification: It has been demonstrated to be effective at detecting bugs and finding inconsistencies in specifications, and thus can eliminate effort wasted on fruitless proof attempts. We argue that in addition, property-based testing enables an incremental approach to a fully verified system, by allowing replacement of automatically generated tests of properties stated in the specification by formal proofs. We demonstrate this approach on the verification of systems code, discuss the implications on systems design, and outline the integration of property-based testing into the Cogent framework.

We present an approach to writing and formally verifying high-assurance file-system code in a restricted language called Cogent, supported by a certifying compiler that produces C code, high-level specification of Cogent, and translation correctness proofs. The language is strongly typed and guarantees absence of a number of common file system implementation errors. We show how verification effort is drastically reduced for proving higher-level properties of the file system implementation by reasoning about the generated formal specification rather than its low-level C code. We use the framework to write two Linux file systems, and compare their performance with their native C implementations.

Existing approaches to array fusion can deal with straight-line producer consumer pipelines, but cannot fuse branching data flows where a generated array is consumed by several different consumers. Branching data flows are common and natural to write, but a lack of fusion leads to the creation of an intermediate array at every branch point. We present a new array fusion system that handles branches, based on Waters's series expression framework, but extended to work in a functional setting. Our system also solves a related problem in stream fusion, namely the introduction of duplicate loop counters. We demonstrate speedup over existing fusion systems for several key examples.

Purely functional, embedded array programs are a good match for SIMD hardware, such as GPUs. However, the naive compilation of such programs quickly leads to both code explosion and an excessive use of intermediate data structures. The resulting slow-down is not acceptable on target hardware that is usually chosen to achieve high performance.

File systems are too important, and current ones are too buggy, to remain unverified. Yet the most successful verification methods for functional correctness remain too expensive for current file system implementations --- we need verified correctness but at reasonable cost. This paper presents our vision and ongoing work to achieve this goal for a new high-performance flash file system, called BilbyFs. BilbyFs is carefully designed to be highly modular, so it can be verified against a high-level functional specification one component at a time. This modular implementation is captured in a set of domain specific languages from which we produce the design-level specification, as well as its optimised C implementation. Importantly, we also automatically generate the proof linking these two artefacts. The combination of these features dramatically reduces verification effort. Verified file systems are now within reach for the first time.

Flattening nested parallelism is a vectorising code transform that converts irregular nested parallelism into flat data parallelism. Although the result has good asymptotic performance, flattening thoroughly restructures the code. Many intermediate data structures and traversals are introduced, which may or may not be eliminated by subsequent optimisation. We present a novel program analysis to identify parts of the program where flattening would only introduce overhead, without appropriate gain. We present empirical evidence that avoiding vectorisation in these cases leads to more efficient programs than if we had applied vectorisation and then relied on array fusion to eliminate intermediates from the resulting code.

We present a refined approach to parallel array fusion that uses indexed types to specify the internal representation of each array. Our approach aids the client programmer in reasoning about the performance of their program in terms of the source code. It also makes the intermediate code easier to transform at compile-time, resulting in faster compilation and more reliable runtimes. We demonstrate how our new approach improves both the clarity and performance of several end-user written programs, including a fluid flow solver and an interpolator for volumetric data.

Existing approaches to higher-order vectorisation, also known as flattening nested data parallelism, do not preserve the asymptotic work complexity of the source program. Straightforward examples, such as sparse matrix-vector multiplication, can suffer a severe blow-up in both time and space, which limits the practicality of this method. We discuss why this problem arises, identify the mis-handling of index space transforms as the root cause, and present a solution using a refined representation of nested arrays. We have implemented this solution in Data Parallel Haskell (DPH) and present benchmarks showing that realistic programs, which used to suffer the blow-up, now have the correct asymptotic work complexity. In some cases, the asymptotic complexity of the vectorised program is even better than the original.

Stencil convolution is a fundamental building block of many scientific and image processing algorithms. We present a declarative approach to writing such convolutions in Haskell that is both efficient at runtime and implicitly parallel. To achieve this we extend our prior work on the Repa array library with two new features: partitioned and cursored arrays. Combined with careful management of the interaction between GHC and its back-end code generator LLVM, we achieve performance comparable to the standard OpenCV library.

We present a novel approach to regular, multi-dimensional arrays in Haskell. The main highlights of our approach are that it (1) is purely functional, (2) supports reuse through shape polymorphism, (3) avoids unnecessary intermediate structures rather than relying on subsequent loop fusion, and (4) supports transparent parallelisation.

If you want to program a parallel computer, a purely functional language like Haskell is a promising starting point. Since the language is pure, it is by-default safe for parallel evaluation, whereas imperative languages are by-default unsafe. But that doesn\'t make it easy! Indeed it has proved quite difficult to get robust, scalable performance increases through parallel functional programming, especially as the number of processors increases. A particularly promising and well-studied approach to employing large numbers of processors is data parallelism. Blelloch\'s pioneering work on NESL showed that it was possible to combine a rather flexible programming model (nested data parallelism) with a fast, scalable execution model (flat data parallelism). In this paper we describe Data Parallel Haskell, which embodies nested data parallelism in a modern, general-purpose language, implemented in a state-of-the-art compiler, GHC. We focus particularly on the vectorisation transformation, which transforms nested to flat data parallelism.

ML modules and Haskell type classes have proven to be highly effective tools for program structuring. Modules emphasize explicit configuration of program components and the use of data abstraction. Type classes emphasize implicit program construction and ad hoc polymorphism. In this paper, we show how the implicitly-typed style of type class programming may be supported within the framework of an explicitly-typed module language by viewing type classes as a particular mode of use of modules. This view offers a harmonious integration of modules and type classes, where type class features, such as class hierarchies and associated types, arise naturally as uses of existing module-language constructs, such as module hierarchies and type components. In addition, programmers have explicit control over which type class instances are available for use by type inference in a given scope. We formalize our approach as a Harper-Stone-style elaboration relation, and provide a sound type inference algorithm as a guide to implementation.

Haskell programmers often use a multi-parameter type class in which one or more type parameters are functionally dependent on the first. Although such functional dependencies have proved quite popular in practice, they express the programmer's intent somewhat indirectly. Developing earlier work on associated data types, we propose to add functionally dependent types as type synonyms to type-class bodies. These associated type synonyms constitute an interesting new alternative to explicit functional dependencies.

Haskell's type classes allow ad-hoc overloading, or type-indexing, of functions. A natural generalisation is to allow type-indexing of data types as well. It turns out that this idea directly supports a powerful form of abstraction called associated types, which are available in C++ using traits classes. Associated types are useful in many applications, especially for self-optimising libraries that adapt their data representations and algorithms in a type-directed manner.In this paper, we introduce and motivate associated types as a rather natural generalisation of Haskell's existing type classes. Formally, we present a type system that includes a type-directed translation into an explicitly typed target language akin to System F; the existence of this translation ensures that the addition of associated data types to an existing Haskell compiler only requires changes to the front end.

We argue that teaching purely functional programming as such in freshman courses is detrimental to both the curriculum as well as to promoting the paradigm. Instead, we need to focus on the more general aims of teaching elementary techniques of programming and essential concepts of computing. We support this viewpoint with experience gained during several semesters of teaching large first-year classes (up to 600 students) in Haskell. These classes consisted of computer science students as well as students from other disciplines. We have systematically gathered student feedback by conducting surveys after each semester. This article contributes an approach to the use of modern functional languages in first year courses and, based on this, advocates the use of functional languages in this setting.

This paper introduces a new approach to optimizing array algorithms in functional languages. We are specifically aiming at an efficient implementation of irregular array algorithms that are hard to implement in conventional array languages such as Fortran. We optimize the storage layout of arrays containing complex data structures and reduce the running time of functions operating on these arrays by means of equational program transformations. In particular, this paper discusses a novel form of combinator loop fusion, which by removing intermediate structures optimizes the use of the memory hierarchy. We identify a combinator named loop P that provides a general scheme for iterating over an array and that in conjunction with an array constructor replicate P is sufficient to express a wide range of array algorithms. On this basis, we define equational transformation rules that combine traversals of loop P and replicate P as well as sequences of applications of loop P into a single loop P traversal. Our approach naturally generalizes to a parallel implementation and includes facilities for optimizing load balancing and communication. A prototype implementation based on the rewrite rule pragma of the Glasgow Haskell Compiler is significantly faster than standard Haskell arrays and approaches the speed of hand coded C for simple examples.

This paper generalises the flattening transformation - a technique for the efficient implementation of nested data parallelism - and reconciles it with main stream functional programming. Nested data parallelism is significantly more expressive and convenient to use than the flat data parallelism typically used in conventional parallel languages like High Performance Fortran and C*. The flattening transformation of Blelloch and Sabot is a key technique for the efficient implementation of nested parallelism via flat parallelism, but originally it was severely restricted, as it did not permit general sum types, recursive types, higher-order functions, and separate compilation. Subsequent work, including some of our own, generalised the transformation and allowed higher-order functions and recursive types. In this paper, we take the final step of generalising flattening to cover the full range of types available in modern languages like Haskell and ML; furthermore, we enable the use of separate compilation. In addition, we present a completely new formulation of the transformation, which is based on the standard lambda calculus notation, and replace a previously ad-hoc transformation step by a systematic generic programming technique. First experiments demonstrate the efficiency of our approach.