Rust is a non-Garbage Collected (GCed) language, but the lack of GC makes expressing data-structures that require shared ownership awkward, inefficient, or both. In this paper we explore a new design for, and implementation of, GC in Rust, called Alloy. Unlike previous approaches to GC in Rust, Alloy allows existing Rust destructors to be automatically used as GC finalizers: this makes Alloy integrate better with existing Rust code than previous solutions but introduces surprising soundness and performance problems. Alloy provides novel solutions for the core problems: finalizer safety analysis rejects unsound destructors from automatically being reused as finalizers; finalizer elision optimises away unnecessary finalizers; and premature finalizer prevention ensures that finalizers are only run when it is provably safe to do so.

Several open-source memory allocators have been ported to CHERI, a hardware capability platform. In this paper we examine the security and performance of these allocators when run under CheriBSD on Arm's prototype Morello platform. We introduce a number of security attacks and show that all but one allocator are vulnerable to some of the attacks --- including the default CheriBSD allocator. We then show that while some forms of allocator performance are meaningful, comparing the performance of hybrid and pure capability (i.e. "running in non-CHERI vs. running in CHERI modes") allocators does not currently appear to be meaningful. Although we do not fully understand the reasons for this, it seems to be at least as much due to factors such as immature compiler toolchains and prototype hardware as it is due to the effects of capabilities on performance.

In this poster, we will outline the scope and contributions of the Capable VMs project, in the framework of the UKRI Digital Security by Design programme.

Syntax errors are generally easy to fix for humans, but not for parsers in general nor LR parsers in particular. Traditional 'panic mode' error recovery, though easy to implement and applicable to any grammar, often leads to a cascading chain of errors that drown out the original. More advanced error recovery techniques suffer less from this problem but have seen little practical use because their typical performance was seen as poor, their worst case unbounded, and the repairs they reported arbitrary. In this paper we introduce the CPCT+ algorithm, and an implementation of that algorithm, that address these issues. First, CPCT+ reports the complete set of minimum cost repair sequences for a given location, allowing programmers to select the one that best fits their intention. Second, on a corpus of 200,000 real-world syntactically invalid Java programs, CPCT+ is able to repair 98.37% of files within a timeout of 0.5s. Finally, CPCT+ uses the complete set of minimum cost repair sequences to reduce the cascading error problem, where incorrect error recovery causes further spurious syntax errors to be identified. Across the test corpus, CPCT+ reports 435,812 error locations to the user, reducing the cascading error problem substantially relative to the 981,628 error locations reported by panic mode.

Since composed grammars are often ambiguous, grammar composition requires a mechanism for dealing with ambiguity: either ruling it out by using delimiters (which are awkward to work with), or by using disambiguation operators to filter a parse forest down to a single parse tree (where, in general, we cannot be sure that we have covered all possible parse forests). In this paper, we show that default disambiguation, which is inappropriate for batch parsing, works well for online parsing, where it can be overridden by the user if necessary. We extend language boxes – a delimiter-based algorithm atop incremental parsing – in such a way that default disambiguation can automatically insert, remove, or resize, language boxes, leading to the automatic language boxes algorithm. The nature of the problem means that default disambiguation cannot always match a user’s intention. However, our experimental evaluation shows that automatic language boxes behave acceptably in 96.8% of tests involving compositions of real-world programming languages.

Homogeneous generative meta-programming (HGMP) enables the generation of program fragments at compile-time or run-time. We present a foundational calculus which can model both compile-time and run-time evaluated HGMP, allowing us to model, for the first time, languages such as Template Haskell. The calculus is designed such that it can be gradually enhanced with the features needed to model many of the advanced features of real languages. We demonstrate this by showing how a simple, staged type system as found in Template Haskell can be added to the calculus.

In spite of several decades of software metrics research and practice, there is little understanding of how software metrics relate to one another, nor is there any established methodology for comparing them. We propose a novel experimental technique, based on search-based refactoring, to ‘animate’ metrics and observe their behaviour in a practical setting. Our aim is to promote metrics to the level of active, opinionated objects that can be compared experimentally to uncover where they conflict, and to understand better the underlying cause of the conflict. Our experimental approaches include semi-random refactoring, refactoring for increased metric agreement/disagreement, refactoring to increase/decrease the gap between a pair of metrics, and targeted hypothesis testing. We apply our approach to five popular cohesion metrics using ten real-world Java systems, involving 330,000 lines of code and the application of over 78,000 refactorings. Our results demonstrate that cohesion metrics disagree with each other in a remarkable 55 % of cases, that Low-level Similarity-based Class Cohesion (LSCC) is the best representative of the set of metrics we investigate while Sensitive Class Cohesion (SCOM) is the least representative, and we discover several hitherto unknown differences between the examined metrics. We also use our approach to investigate the impact of including inheritance in a cohesion metric definition and find that doing so dramatically changes the metric.

Virtual Machines (VMs) with Just-In-Time (JIT) compilers are traditionally thought to execute programs in two phases: the initial warmup phase determines which parts of a program would most benefit from dynamic compilation, before JIT compiling those parts into machine code; subsequently the program is said to be at a steady state of peak performance. Measurement methodologies almost always discard data collected during the warmup phase such that reported measurements focus entirely on peak performance. We introduce a fully automated statistical approach, based on changepoint analysis, which allows us to determine if a program has reached a steady state and, if so, whether that represents peak performance or not. Using this, we show that even when run in the most controlled of circumstances, small, deterministic, widely studied microbenchmarks often fail to reach a steady state of peak performance on a variety of common VMs. Repeating our experiment on 3 different machines, we found that at most 43.5% of pairs consistently reach a steady state of peak performance.

Although run-time language composition is common, it normally takes the form of a crude Foreign Function Interface (FFI). While useful, such compositions tend to be coarse-grained and slow. In this paper we introduce a novel fine-grained syntactic composition of PHP and Python which allows users to embed each language inside the other, including referencing variables across languages. This composition raises novel design and implementation challenges. We show that good solutions can be found to the design challenges; and that the resulting implementation imposes an acceptable performance overhead of, at most, 2.6x.

This artifact contains: the SQPyte prototype, a JIT for executing SQLite queries; and PyPy-SQPyte, a version of the PyPy Python VM which embeds SQPyte. In addition, a benchmark suite is included, which allows performance comparison against standard SQLite and the Java embedded database H2.

In this paper, we compose six different Python and Prolog VMs into 4 pairwise compositions: one using C interpreters, one running on the JVM, one using meta-tracing interpreters, and one using a C interpreter and a meta-tracing interpreter. We show that programs that cross the language barrier frequently execute faster in a meta-tracing composition, and that meta-tracing imposes a significantly lower overhead on composed programs relative to mono-language programs.

Most modern languages are implemented using Virtual Machines (VMs). While the best VMs use Just-In-Time (JIT) compilers to achieve good performance, JITs are costly to implement, and few VMs therefore come with one. The RPython language allows tracing JIT VMs to be automatically created from an interpreter, changing the economics of VM implementation. In this paper, we explain, through two concrete VMs, how meta-tracing RPython VMs can be designed and optimised, and, experimentally, the performance levels one might reasonably expect from them.

Dynamically typed language implementations often use more memory and execute slower than their statically typed cousins, in part because operations on collections of elements are unoptimised. This paper describes storage strategies, which dynamically optimise collections whose elements are instances of the same primitive type. We implement storage strategies in the PyPy virtual machine, giving a performance increase of 18% on wide-ranging benchmarks of real Python programs. We show that storage strategies are simple to implement, needing only 1500LoC in PyPy, and have applicability to a wide range of virtual machines.

Slicing is useful for many software engineering applications and has been widely studied for three decades, but there has been comparatively little work on slicing extended finite state machines (EFSMs). This paper introduces a set of dependence-based EFSM slicing algorithms and an accompanying tool. We demonstrate that our algorithms are suitable for dependence-based slicing. We use our tool to conduct experiments on 10 EFSMs, including benchmarks and industrial EFSMs. Ours is the first empirical study of dependence-based program slicing for EFSMs. Compared to the only previously published dependence-based algorithm, our average slice is smaller 40 percent of the time and larger only 10 percent of the time, with an average slice size of 35 percent for termination insensitive slicing.

A large number of software metrics have been proposed in the literature, but there is little understanding of how these metrics relate to one another. We propose a novel experimental technique, based on search-based refactoring, to assess software metrics and to explore relationships between them. Our goal is not to improve the program being refactored, but to assess the software metrics that guide the auto- mated refactoring through repeated refactoring experiments.

The design of the Icon programming language's expression evaluation system, which can perform limited backtracking, was unique amongst imperative programming languages when created. In this paper I explain and critique the original Icon design and show how a similar system can be integrated into a modern dynamically typed language. Finally I detail my experiences of this system and offer suggestions for the lessons to be learned from it.

Dependence analysis underpins many activities in software maintenance such as comprehension and impact analysis. As a result, dependence has been studied widely for programming languages, notably through work on program slicing. However, there is comparatively little work on dependence analysis at the model level and hitherto, no empirical studies. We introduce a slicing tool for Extended Finite State Machines (EFSMs) and use the tool to gather empirical results on several forms of dependence found in ten EFSMs, including well-known benchmarks in addition to real-world EFSM models. We investigate the statistical properties of dependence using statistical tests for correlation and formalize and prove four of the empirical findings arising from our empirical study. The paper thus provides the maintainer with both empirical data and foundational theoretical results concerning dependence in EFSM models.

Programming languages are the medium used to express our wishes to a computer. Unlike human languages, we can alter, destroy, and create them at will. Despite this, mainstream programming languages are surprisingly homogeneous with the differences between them often amounting to little more than surface syntax issues. New ideas rarely get a toe-hold in the marketplace and those that do have typically been used for decades by a sizeable minority (e.g. dynamic typing). In this talk the speaker will suggest reasons for the inherent conservativeness of programming languages, their designers, and their users. He will look back at some largely forgotten languages - some older than others - that introduced distinctive language features, and offer thoughts on their successes and failures. He will show how a series of seemingly outlandish ideas scavenged from such languages can be blended together to form part of a coherent modern language - one that also aims to try and inject a few new memes of its own into the languages pool.

Domain specific languages (DSLs) are mini-languages that are increasingly seen as being a valuable tool for software developers and non-developers alike. DSLs must currently be created in an ad-hoc fashion, often leading to high development costs and implementations of variable quality. In this article, I show how expressive DSLs can be hygienically embedded in the Converge programming language using its compile-time meta-programming facility, the concept of DSL blocks, and specialised error reporting techniques. By making use of pre-existing facilities, and following a simple methodology, DSL implementation costs can be significantly reduced whilst leading to higher quality DSL implementations.

Refactoring aims to improve the quality of a software systems' structure, which tends to degrade as the system evolves. While manually determining useful refactorings can be challenging, search based techniques can automatically discover useful refactorings. Current search based refactoring approaches require metrics to be combined in a complex fashion, and producea single sequence of refactorings. In this paper we show how Pareto optimality can improve search based refactoring, making the combination of metrics easier, and aiding the presentation of multiple sequences of optimal refactorings to users.

Compile-time meta-programming allows programs to be constructed by the user at compile-time. Although LISP derived languages have long had such facilities, few modern languages are capable of compile-time meta-programming, and of those that do many of the most powerful are statically typed functional languages. In this paper I present the dynamically typed object orientated language Converge which allows compile-time meta-programming in the spirit of Template Haskell. Converge demonstrates that integrating powerful, safe compile-time meta-programming features into a dynamic language requires few restrictions to the flexible development style facilitated by the paradigm. In this paper I detail Converge's compile-time meta-programming facilities, much of which is adapted from Template Haskell, contain several features new to the paradigm. Finally I explain how such a facility might be integrated into similar languages.