From Separate Compilation to Sound Language Composition

TL;DR

This paper introduces nlgcheck, a static analysis tool for the Neverlang language workbench, which detects potential runtime attribute errors at compile time, enabling separate compilation without sacrificing correctness or modularity.

Contribution

It presents a theoretically sound static analysis method that ensures attribute correctness in language extensions, improving reliability in modular language development.

Findings

nlgcheck effectively detects undefined attribute errors at compile time.

The approach maintains separate compilation and modularity.

Performance impact is minimal, supporting practical adoption.

Abstract

The development of programming languages involves complex theoretical and practical challenges, particularly when addressing modularity and reusability through language extensions. While language workbenches aim to enable modular development under the constraints of the language extension problem, one critical constraint -- separate compilation -- is often relaxed due to its complexity. However, this relaxation undermines artifact reusability and integration with common dependency systems. A key difficulty under separate compilation arises from managing attribute grammars, as extensions may introduce new attributes that invalidate previously generated abstract syntax tree structures. Existing approaches, such as the use of dynamic maps in the Neverlang workbench, favor flexibility at the cost of compile-time correctness, leading to potential runtime errors due to undefined attributes. This work addresses this issue by introducing nlgcheck, a theoretically sound static analysis tool based on data-flow analysis for the Neverlang language workbench. nlgcheck detects potential runtime errors -- such as undefined attribute accesses -- at compile time, preserving separate compilation while maintaining strong static correctness guarantees. Experimental evaluation using mutation testing on Neverlang-based projects demonstrates that nlgcheck effectively enhances robustness without sacrificing modularity or flexibility and with a level of performance that does not impede its adoption in daily development activities.