Compiler.next: A Search-Based Compiler to Power the AI-Native Future of Software Engineering

2025-10-27

AI Summary

Contemporary AI-augmented software engineering tools suffer from cognitive overload, tool fragmentation, and limited collaborator capabilities, hindering the continuous evolution of AI-native systems. This paper introduces Compiler.next—a novel framework that reimagines compilation as an intent-driven dynamic search process. By parsing natural-language developer intents, it jointly optimizes across multiple objectives, employs adaptive cognitive architectures, integrates prompt engineering, and co-tunes foundation model parameters to automatically discover executable, high-reliability software solutions within the solution space. Its core contribution lies in transcending the static translation paradigm of classical compilers, enabling a paradigm shift from syntactic translation to semantic problem-solving—thereby establishing foundational principles for Software Engineering 3.0. Prototype evaluation demonstrates substantial reductions in development barriers, alongside measurable improvements in generated artifact quality and system evolution efficiency.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

The rapid advancement of AI-assisted software engineering has brought transformative potential to the field of software engineering, but existing tools and paradigms remain limited by cognitive overload, inefficient tool integration, and the narrow capabilities of AI copilots. In response, we propose Compiler.next, a novel search-based compiler designed to enable the seamless evolution of AI-native software systems as part of the emerging Software Engineering 3.0 era. Unlike traditional static compilers, Compiler.next takes human-written intents and automatically generates working software by searching for an optimal solution. This process involves dynamic optimization of cognitive architectures and their constituents (e.g., prompts, foundation model configurations, and system parameters) while finding the optimal trade-off between several objectives, such as accuracy, cost, and latency. This paper outlines the architecture of Compiler.next and positions it as a cornerstone in democratizing software development by lowering the technical barrier for non-experts, enabling scalable, adaptable, and reliable AI-powered software. We present a roadmap to address the core challenges in intent compilation, including developing quality programming constructs, effective search heuristics, reproducibility, and interoperability between compilers. Our vision lays the groundwork for fully automated, search-driven software development, fostering faster innovation and more efficient AI-driven systems.

Problem

• Addressing cognitive overload and inefficient tool integration in AI-assisted software engineering
• Enabling seamless evolution of AI-native systems through search-based compilation
• Lowering technical barriers for non-experts in AI-powered software development

Innovation

• Search-based compiler generates software from intents
• Dynamic optimization of cognitive architectures and parameters
• Automated search-driven development balancing multiple objectives