Overview of Current Challenges in Multi-Architecture Software Engineering and a Vision for the Future

TL;DR

This paper discusses the challenges in multi-architecture software engineering and proposes a novel WebAssembly and AI-based system architecture to enhance automation, modeling, and autonomy in software development.

Contribution

It introduces a new architecture utilizing WebAssembly, neuro-symbolic AI, and knowledge graphs to model software throughout its lifecycle with autonomous and transparent capabilities.

Findings

Overview of current multi-architecture software engineering challenges

Proposal of a WebAssembly-based system architecture with knowledge graphs

Discussion of future research directions and implementation efforts

Abstract

The landscape of computing technologies is changing rapidly, straining existing software engineering practices and tools. The growing need to produce and maintain increasingly complex multi-architecture applications makes it crucial to effectively accelerate and automate software engineering processes. At the same time, artificial intelligence (AI) tools are expected to work hand-in-hand with human developers. Therefore, it becomes critical to model the software accurately, so that the AI and humans can share a common understanding of the problem. In this contribution, firstly, an in-depth overview of these interconnected challenges faced by modern software engineering is presented. Secondly, to tackle them, a novel architecture based on the emerging WebAssembly technology and the latest advancements in neuro-symbolic AI, autonomy, and knowledge graphs is proposed. The presented system architecture is based on the concept of dynamic, knowledge graph-based WebAssembly Twins, which model the software throughout all stages of its lifecycle. The resulting systems are to possess advanced autonomous capabilities, with full transparency and controllability by the end user. The concept takes a leap beyond the current software engineering approaches, addressing some of the most urgent issues in the field. Finally, the efforts towards realizing the proposed approach as well as future research directions are summarized.