Virtual Machines, Dynamic Compllers, and Implementation Frameworks make dynamic langauges easier and more efficient to optimize. Meanwhile, IDEs, provers, dependent types, type inferencers, and (so-called) “generative AI” mean programmers can express - statically - more information about the dynamic behaviour of their programs. Component libraries in these languages will come with assertions and proofs of their behaviour, and their advocates fantasise about transforming programming into the composition of dependently-typed higher-order yoneda morphisms, ensuring programs are correct-by-construction (where that construction is carried out by yet more generative AI). In this talk, I’ll speculate about what the resulting world will be like for programmers. Rather than a static world of platonic mathematical abstractions, I argue that the opposite will be true: that all languages will be dynamic.
Polyglot programming is the practice of writing an application with multiple languages to capture additional functionality and efficiency not available to a single language. This happens more often than people think. Some reasons are: to support different platforms (e.g., Android, iOS), to be more efficient on some parts, to take advantage of features unique to a different ecosystem (e.g., dedicated APIs). But are we ready for polyglot programming? This talk will try to explore the open issues from the point of view of both the multiple programming language integration and from the software engineering development for polyglot programming.
WebAssembly (Wasm) is a virtual machine whose defining characteristic is that it is low-level: Wasm is designed to abstract the hardware below, not language concepts above. This is a prerequisite for providing predictable performance and for avoiding language bias without feature creep. At the same time, it is a hard requirement that Wasm is safe and portable, which sometimes necessitates raising its abstraction level above the raw metal. Yet ultimately, the intention is that language runtimes are largely implemented _on top_ of Wasm, in Wasm itself.
Dynamic languages pose a challenge for this model, because achieving acceptable performance for them often requires every dirty trick from the books. Not all of these techniques are easily ported to Wasm with some of its abstractions, or they incur higher cost because a Wasm engine cannot know or trust invariants in the higher-level runtime and may need to perform redundant checks to maintain its own safety. In particular, Wasm will need to supply additional mechanisms to efficiently support techniques like jit compilation or inline caches.
Programming languages offer a number of abstractions such as dynamic typing, sandboxing, and automatic garbage collection which, however, come at a performance cost. Looking back, the most influential programming languages were proposed at a time when Moore’s Law was still in place. Nowadays, post-Moore’s law, scalability, and elasticity become crucial requirements, leading to an increasing tension between programming language design and implementation, and performance. It is now time to discuss the impact of programming languages and language runtimes in the context of scalable and elastic cloud computing platforms with the goal of forecasting their role in the new cloud era.
Over the past decade software development has shifted from a process centered around writing code to a process that increasingly involves composition of external packages and managing the integration of code from other team members. The next decade-plus will be defined by the shift from a process where humans are the central developers of code into one where AI agents, likely Large Language Model (LLM) based, will be the major creators of code and humans will shift to a supervisory role as curators, integrating rich framework-functionality and code developed by AI programming agents.
In this new world we must ask ourselves – are programming languages as they exist today fit for purpose and how do they evolve to meet the needs of this future programming model. This talk represents an opinionated take on the question and attempts to outline specific areas of investigation that need to be addressed by the PL community as part of this journey including:
What programming language features help/hinder AI agents when understanding and generating code?
What programming language features help/hinder human agents when working with an AI Copilot?
What programming language tools are needed to empower AI agents in creating grounded and reliable outputs?
How can intents be expressed as part of the program representation – examples, constraints, natural language, external documents?
How do we empower end-users as part of this transformation?
What programming language features are needed to support new AI driven workflows – live coding, interactive requirement gathering, AI TDD?
Effectively answering these questions plays a key role in determining if AI driven programming represents a revolution in how software is developed or is limited to being a programming productivity aid for existing development workflows. As such our community should play a central role in understanding this space and leading in the development of this technological transformation!
Since the first bug was discovered in the Mark Harvard II electromechanical computer it was clear that finding bugs and debugging of computer systems would be an extremely challenging task. Today, various reports indicated that programmers spend approximately 50% of their time on debugging related tasks resulting in an annual cost of $312 billion. Given these astronomical amounts of resources being put into debugging, any technique that improves debugging efficiency is tremendously valuable.
In the last decades various new debugging techniques have been put forward to ease debugging and finding the root cause of a failures. Techniques like record-replay, deltadebugging, model checking, tracing, visualisation, fuzzing, automated debugging, and many more help programmers to be more effective while debugging. Recently, we have seen that some of techniques are slowly finding their way into mainstream debugging practices. In this talk we first give an overview of recent exiting debugging techniques, show their advantages and limitations to then reflect on the challenges and opportunities for further research.