Walter Cazzola

Federico Bruzzone, Walter Cazzola, Luca Favalli

Software product line (SPL) engineering has been successfully applied to software development by obtaining software systems as compositions of modular features. Existing approaches to SPL engineering, however, are typically bound to a specific technological space (such as, a programming language and a composer) and integrated development environment (IDE), and rely on extraction mechanisms that make strong assumptions on the underlying technological space. This tight coupling hinders reuse, evolution, and adoption of heterogeneous development environments. We propose a general, workbench-agnostic protocol for extracting feature models from existing software artifacts and for configuring and deriving software products. The protocol follows a bottom-up approach based on lightweight dependency units called "atoms", and organizes the extraction and configuration process around an SPL server (workbench-independent) and an SPL client with a workbench-specific backend and a generic frontend. The protocol makes few assumptions on the underlying software artifacts and is therefore applicable to varied SPLs. The applicability of this approach is presented through a prototypical implementation of the architecture in which several subsystems interact and can be swapped freely without affecting the others. In particular, we focus on the application of such a protocol in the context of language product lines (LPLs), demonstrating its applicability to concrete scenarios while preserving workbench-agnosticism. From bottom to top, the implementation comprises: Neverlang language artifacts, a Java SPL client backend, an agnostic and reusable SPL server written in Go and Prolog, and a JavaScript SPL client frontend.

Federico Bruzzone, Walter Cazzola

Achieving zero-cost specialization remains a fundamental challenge in programming language and compiler design. It often necessitates trade-offs between expressive power and type system soundness, as the interaction between conditional compilation and static dispatch can easily lead to unforeseen coherence violations and increased complexity in the formal model. This paper introduces meta-monomorphizing specializations, a novel framework that achieves specialization by repurposing monomorphization through compile-time metaprogramming. Instead of modifying the host compiler, our approach generates meta-monomorphized traits and implementations that encode specialization constraints directly into the type structure, enabling deterministic, coherent dispatch without overlapping instances. We formalize this method for first-order, predicate-based, and higher-ranked polymorphic specialization, also in presence of lifetime parameters. Our evaluation, based on a Rust implementation using only existing macro facilities, demonstrates that meta-monomorphization enables expressive specialization patterns while maintaining full compatibility with standard optimization pipelines. We show that specialization can be realized as a disciplined metaprogramming layer, offering a practical, language-agnostic path to high-performance abstraction. A comprehensive study of public Rust codebases further validates our approach, revealing numerous workarounds that meta-monomorphization can eliminate, leading to more idiomatic and efficient code. An empirical evaluation on 16 micro-benchmarks confirms that compile-time specialization matches or outperforms runtime TypeId-based dispatch, and demonstrates expressiveness gains on patterns -- such as lifetime-based dispatch, higher-ranked types, compound predicates, and wildcard matching -- that runtime dispatch structurally cannot express.

Federico Bruzzone, Walter Cazzola, Luca Favalli

Developing editing support for $L$ languages in $E$ editors is complex and time-consuming. Some languages do not provide dedicated editors, while others offer a single native editor. The $\textit{language server protocol}$ (LSP) reduces the language-editor combinations $L \times E$ to $L + E$, where a single language server communicates with editors via LSP plugins. However, overlapping implementations of linguistic components remain an issue. Existing language workbenches struggle with modularity, reusability, and leveraging type systems for language server generation. In this work, we propose: (i) Typelang, a family of domain-specific languages for modular, composable, and reusable type system implementation, (ii) a modular language server generation process, producing servers for languages built in a modular workbench, (iii) the variant-oriented programming paradigm and a cross-artifact coordination layer to manage interdependent software variants, and (iv) an LSP plugin generator, reducing $E$ to $1$ by automating plugin creation for multiple editors. To simplify editing support for language families, each language artifact integrates its own Typelang variant, used to generate language servers. This reduces combinations to $T \times 1$, where $T = L$ represents the number of type systems. Further reuse of language artifacts across languages lowers this to $N \times 1$, where $N << T$, representing unique type systems. We implement Typelang in Neverlang, generating language servers for each artifact and LSP plugins for three editors. Empirical evaluation shows a 93.48% reduction in characters needed for type system implementation and 100% automation of LSP plugin generation, significantly lowering effort for editing support in language families, especially when artifacts are reused.

Federico Bruzzone, Walter Cazzola, Matteo Brancaleoni et al.

Real-time automatic speech recognition systems are increasingly integrated into interactive applications, from voice assistants to live transcription services. However, scaling these systems to support multiple concurrent clients while maintaining low latency and high accuracy remains a major challenge. In this work, we present SWIM, a novel real-time ASR system built on top of OpenAI's Whisper model that enables true model-level parallelization for scalable, multilingual transcription. SWIM supports multiple concurrent audio streams without modifying the underlying model. It introduces a buffer merging strategy that maintains transcription fidelity while ensuring efficient resource usage. We evaluate SWIM in multi-client settings -- scaling up to 20 concurrent users -- and show that it delivers accurate real-time transcriptions in English, Italian, and Spanish, while maintaining low latency and high throughput. While Whisper-Streaming achieves a word error rate of approximately 8.2% with an average delay of approximately 3.4 s in a single-client, English-only setting, SWIM extends this capability to multilingual, multi-client environments. It maintains comparable accuracy with significantly lower delay -- around 2.4 s with 5 clients -- and continues to scale effectively up to 20 concurrent clients without degrading transcription quality and increasing overall throughput. Our approach advances scalable ASR by improving robustness and efficiency in dynamic, multi-user environments.

Walter Cazzola, Albert Shaqiri

Context: This paper presents the concept of open programming language interpreters and the implementation of a framework-level metaobject protocol (MOP) to support them. Inquiry: We address the problem of dynamic interpreter adaptation to tailor the interpreter's behavior on the task to be solved and to introduce new features to fulfill unforeseen requirements. Many languages provide a MOP that to some degree supports reflection. However, MOPs are typically language-specific, their reflective functionality is often restricted, and the adaptation and application logic are often mixed which hardens the understanding and maintenance of the source code. Our system overcomes these limitations. Approach: We designed and implemented a system to support open programming language interpreters. The prototype implementation is integrated in the Neverlang framework. The system exposes the structure, behavior and the runtime state of any Neverlang-based interpreter with the ability to modify it. Knowledge: Our system provides a complete control over interpreter's structure, behavior and its runtime state. The approach is applicable to every Neverlang-based interpreter. Adaptation code can potentially be reused across different language implementations. Grounding: Having a prototype implementation we focused on feasibility evaluation. The paper shows that our approach well addresses problems commonly found in the research literature. We have a demonstrative video and examples that illustrate our approach on dynamic software adaptation, aspect-oriented programming, debugging and context-aware interpreters. Importance: To our knowledge, our paper presents the first reflective approach targeting a general framework for language development. Our system provides full reflective support for free to any Neverlang-based interpreter. We are not aware of any prior application of open implementations to programming language interpreters in the sense defined in this paper. Rather than substituting other approaches, we believe our system can be used as a complementary technique in situations where other approaches present serious limitations.