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

• URL: http://arxiv.org/abs/2510.24799v1
• Date: Mon, 27 Oct 2025 21:01:48 GMT
• Title: Compiler.next: A Search-Based Compiler to Power the AI-Native Future of Software Engineering
• Authors: Filipe R. Cogo, Gustavo A. Oliva, Ahmed E. Hassan,
• Abstract summary: We present Compiler.next, a novel search-based compiler designed to enable the seamless evolution of AI-native software systems.<n>Unlike traditional static compilers, Compiler.next takes human-written intents and automatically generates working software by searching for an optimal solution.<n>We present a roadmap to address the core challenges in intent compilation, including developing quality programming constructs, effective search constructs, and interoperability between compilers.
• Score: 3.8060608371616778
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• A Viable Paradigm of Software Automation: Iterative End-to-End Automated Software Development [41.295627885484855]
  We present a vision of an iterative end-to-end automated software development paradigm AutoSW.<n>It operates in an analyze-plan-implement-deliver loop, where AI systems as human partners become first-class actors.<n>The results indicate that AutoSW can successfully deliver executable software.
  arXiv  Detail & Related papers  (2025-11-19T09:57:49Z)
• The Role of Advanced Computer Architectures in Accelerating Artificial Intelligence Workloads [0.0]
  The remarkable progress in Artificial Intelligence (AI) is foundation-ally linked to a concurrent revolution in computer architecture.<n>As AI models, particularly Deep Neural Networks (DNNs), have grown in complexity, their massive computational demands have pushed traditional architectures to their limits.<n>This paper provides a structured review of this co-evolution, analyzing the architectural landscape designed to accelerate modern AI workloads.
  arXiv  Detail & Related papers  (2025-11-13T06:26:39Z)
• Generative AI and the Transformation of Software Development Practices [0.0]
  Generative AI is reshaping how software is designed, written, and maintained.<n>This paper examines how AI-assisted techniques are changing software engineering practice.
  arXiv  Detail & Related papers  (2025-10-12T22:02:10Z)
• Barbarians at the Gate: How AI is Upending Systems Research [58.95406995634148]
  We argue that systems research, long focused on designing and evaluating new performance-oriented algorithms, is particularly well-suited for AI-driven solution discovery.<n>We term this approach as AI-Driven Research for Systems ( ADRS), which iteratively generates, evaluates, and refines solutions.<n>Our results highlight both the disruptive potential and the urgent need to adapt systems research practices in the age of AI.
  arXiv  Detail & Related papers  (2025-10-07T17:49:24Z)
• A Survey on Cloud-Edge-Terminal Collaborative Intelligence in AIoT Networks [49.90474228895655]
  Cloud-edge-terminal collaborative intelligence (CETCI) is a fundamental paradigm within the artificial intelligence of things (AIoT) community.<n>CETCI has made significant progress with emerging AIoT applications, moving beyond isolated layer optimization to deployable collaborative intelligence systems.<n>This survey describes foundational architectures, enabling technologies, and scenarios of CETCI paradigms, offering a tutorial-style review for CISAIOT beginners.
  arXiv  Detail & Related papers  (2025-08-26T08:38:01Z)
• Vibe Coding vs. Agentic Coding: Fundamentals and Practical Implications of Agentic AI [0.36868085124383626]
  Review presents a comprehensive analysis of two emerging paradigms in AI-assisted software development: vibe coding and agentic coding.<n> Vibe coding emphasizes intuitive, human-in-the-loop interaction through prompt-based, conversational interaction.<n>Agentic coding enables autonomous software development through goal-driven agents capable of planning, executing, testing, and iterating tasks with minimal human intervention.
  arXiv  Detail & Related papers  (2025-05-26T03:00:21Z)
• Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey [58.50944604905037]
  Edge-cloud collaborative computing (ECCC) has emerged as a pivotal paradigm for addressing the computational demands of modern intelligent applications.<n>Recent advancements in AI, particularly deep learning and large language models (LLMs), have dramatically enhanced the capabilities of these distributed systems.<n>This survey provides a structured tutorial on fundamental architectures, enabling technologies, and emerging applications.
  arXiv  Detail & Related papers  (2025-05-03T13:55:38Z)
• Challenges and Paths Towards AI for Software Engineering [55.95365538122656]
  We discuss progress in AI for software engineering in threefold manner.<n>First, we provide a structured taxonomy of concrete tasks in AI for software engineering.<n>Second, we outline several key bottlenecks that limit current approaches.
  arXiv  Detail & Related papers  (2025-03-28T17:17:57Z)
• Overview of Current Challenges in Multi-Architecture Software Engineering and a Vision for the Future [0.0]
  The presented system architecture is based on the concept of dynamic, knowledge graph-based WebAssembly Twins. The resulting systems are to possess advanced autonomous capabilities, with full transparency and controllability by the end user.
  arXiv  Detail & Related papers  (2024-10-28T13:03:09Z)
• Using Machine Learning To Identify Software Weaknesses From Software Requirement Specifications [49.1574468325115]
  This research focuses on finding an efficient machine learning algorithm to identify software weaknesses from requirement specifications. Keywords extracted using latent semantic analysis help map the CWE categories to PROMISE_exp. Naive Bayes, support vector machine (SVM), decision trees, neural network, and convolutional neural network (CNN) algorithms were tested.
  arXiv  Detail & Related papers  (2023-08-10T13:19:10Z)
• Comparing Software Developers with ChatGPT: An Empirical Investigation [0.0]
  This paper conducts an empirical investigation, contrasting the performance of software engineers and AI systems, like ChatGPT, across different evaluation metrics. The paper posits that a comprehensive comparison of software engineers and AI-based solutions, considering various evaluation criteria, is pivotal in fostering human-machine collaboration.
  arXiv  Detail & Related papers  (2023-05-19T17:25:54Z)
• Physical Computing for Materials Acceleration Platforms [81.09376948478891]
  We argue that the same simulation and AI tools that will accelerate the search for new materials, as part of the MAPs research program, also make possible the design of fundamentally new computing mediums. We outline a simulation-based MAP program to design computers that use physics itself to solve optimization problems. We expect to introduce a new era of innovative collaboration between materials researchers and computer scientists.
  arXiv  Detail & Related papers  (2022-08-17T23:03:54Z)
• Automated Machine Learning: A Case Study on Non-Intrusive Appliance Load Monitoring [81.06807079998117]
  We propose a novel approach to enable Automated Machine Learning (AutoML) for Non-Intrusive Appliance Load Monitoring (NIALM)<n>NIALM offers a cost-effective alternative to smart meters for measuring the energy consumption of electric devices and appliances.
  arXiv  Detail & Related papers  (2022-03-06T10:12:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.