When Code Becomes Abundant: Redefining Software Engineering Around Orchestration and Verification
- URL: http://arxiv.org/abs/2602.04830v1
- Date: Wed, 04 Feb 2026 18:18:53 GMT
- Title: When Code Becomes Abundant: Redefining Software Engineering Around Orchestration and Verification
- Abstract summary: We argue that Software Engineering must redefine itself around intent articulation, architectural control, and verification.<n>This shift introduces accountability collapse as a central risk and requires fundamental changes to research priorities, educational curricula, and industrial practices.
- Score: 1.217503190366097
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Software Engineering (SE) faces simultaneous pressure from AI automation (reducing code production costs) and hardware-energy constraints (amplifying failure costs). We position that SE must redefine itself around human discernment-intent articulation, architectural control, and verification-rather than code construction. This shift introduces accountability collapse as a central risk and requires fundamental changes to research priorities, educational curricula, and industrial practices. We argue that Software Engineering, as traditionally defined around code construction and process management, is no longer sufficient. Instead, the discipline must be redefined around intent articulation, architectural control, and systematic verification. This redefinition shifts Software Engineering from a production-oriented field to one centered on human judgment under automation, with profound implications for research, practice, and education.
Related papers
- The Specification Paradox: Rethinking Requirements Engineering in the Age of AI [0.0]
This article discusses the transition from a code-centered paradigm to Specification-Driven Development.<n>We argue that artificial intelligence reduces some of the effort associated with writing source code, but it does not eliminate the complexity of developing professional software systems.<n>We conclude that the future of Software Engineering will depend not only on machines' ability to generate code, but also on humans' ability to correctly specify, evaluate, and evolve what is intended to be built.
arXiv Detail & Related papers (2026-08-17T14:19:14Z) - From Determinism to Delegation: AI-Native Software Engineering and the Evolution of the Agentic Engineer [0.0]
Software engineering is experiencing its most significant transformation since the emergence of high-level programming languages.<n>This paper argues that AI-Native Software Engineering is a paradigm shift rather than a mere tooling advance.<n>We define core mechanisms of autonomous agents, including reasoning-acting loops, context engineering, tool use, memory, behavioral drift, and compositional error.
arXiv Detail & Related papers (2026-06-27T07:43:43Z) - Human-AI Collaboration and the Transformation of Software Engineering Work [0.0]
The integration of Generative AI (GenAI) and Agentic AI into software development is reconfiguring software engineering.<n>We map which traditional activities are being automated, which are being augmented, and which are newly emerging.<n>We derive nine empirically testable propositions and articulate implications for theory, industry workforce transformation, university curricula, and organizational leadership.
arXiv Detail & Related papers (2026-06-02T09:36:39Z) - Cognitive Atrophy and Systemic Collapse in AI-Dependent Software Engineering [0.0]
This paper introduces "Epistemological Debt," the hidden carrying cost incurred when engineers substitute logical derivation with passive AI verification.<n>This debt erodes the mental models essential for root-cause analysis, widening the gap between system complexity and human comprehension.<n>Using the 2026 Amazon outages as a case study, this research illustrates how "mechanized convergence" leads to systemic fragility.
arXiv Detail & Related papers (2026-04-29T16:20:25Z) - Rethinking Software Engineering for Agentic AI Systems [0.0]
Large language models and agentic AI systems have created an unprecedented abundance of automatically generated code.<n>This paper examines whether the discipline should be reoriented around orchestration, verification, and human-AI collaboration.
arXiv Detail & Related papers (2026-04-12T12:08:53Z) - Understanding by Reconstruction: Reversing the Software Development Process for LLM Pretraining [66.89012795621349]
Large Language Models (LLMs) often struggle with the deep, long-horizon reasoning required for complex software engineering.<n>We propose a novel paradigm: understanding via reconstruction.<n>We introduce a framework that synthesizes latent agentic trajectories using a multi-agent simulation.
arXiv Detail & Related papers (2026-03-11T09:23:20Z) - Revisiting Software Engineering Education in the Era of Large Language Models: A Curriculum Adaptation and Academic Integrity Framework [0.0]
This paper proposes a theoretical framework for analyzing how generative AI alters core software engineering competencies.<n> Attention is given to computer engineering programs in Turkey, where centralized regulation, large class sizes, and exam-oriented assessment practices amplify these challenges.
arXiv Detail & Related papers (2026-01-06T22:41:47Z) - Vibe Coding as a Reconfiguration of Intent Mediation in Software Development: Definition, Implications, and Research Agenda [3.6109780623192305]
vibe coding is a software development paradigm where humans and generative AI engage in collaborative flow to co-create software artifacts.<n>We show that vibe coding reconfigures cognitive work by redistributing labor between humans and machines.<n>We identify key opportunities, including democratization, acceleration, and systemic leverage, alongside risks.
arXiv Detail & Related papers (2025-07-29T15:44:55Z) - 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) - Generative AI and Empirical Software Engineering: A Paradigm Shift [9.284024538100063]
This vision paper examines how the integration of large language models into software engineering disrupts established research paradigms.<n>We discuss how it transforms the phenomena we study, the methods and theories we rely on, the data we analyze, and the threats to validity that arise in dynamic AI-mediated environments.<n>Our aim is to help the empirical software engineering community adapt its questions, instruments, and validation standards to a future in which AI systems are not merely tools, but active collaborators shaping software engineering and its study.
arXiv Detail & Related papers (2025-02-12T04:13:07Z) - Abstraction Engineering [6.091612632147657]
Abstraction is already used across many disciplines involved in software development.
This paper looks at these new challenges and proposes to address them through the lens of Abstraction.
We discuss the foundations of Abstraction Engineering, identify key challenges, highlight the research questions that help address these challenges, and create a roadmap for future research.
arXiv Detail & Related papers (2024-08-26T07:56:32Z) - Defining and executing temporal constraints for evaluating engineering
artifact compliance [56.08728135126139]
Process compliance focuses on ensuring that the actual engineering work is followed as closely as possible to the described engineering processes.
Checking these process constraints is still a daunting task that requires a lot of manual work and delivers feedback to engineers only late in the process.
We present an automated constraint checking approach that can incrementally check temporal constraints across inter-related engineering artifacts upon every artifact change.
arXiv Detail & Related papers (2023-12-20T13:26:31Z) - ChatDev: Communicative Agents for Software Development [84.90400377131962]
ChatDev is a chat-powered software development framework in which specialized agents are guided in what to communicate.
These agents actively contribute to the design, coding, and testing phases through unified language-based communication.
arXiv Detail & Related papers (2023-07-16T02:11:34Z) - Empowered and Embedded: Ethics and Agile Processes [60.63670249088117]
We argue that ethical considerations need to be embedded into the (agile) software development process.
We put emphasis on the possibility to implement ethical deliberations in already existing and well established agile software development processes.
arXiv Detail & Related papers (2021-07-15T11:14:03Z) - Technology Readiness Levels for AI & ML [79.22051549519989]
Development of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end.
Engineering systems follow well-defined processes and testing standards to streamline development for high-quality, reliable results.
We propose a proven systems engineering approach for machine learning development and deployment.
arXiv Detail & Related papers (2020-06-21T17:14:34Z) - Machine Learning for Software Engineering: A Systematic Mapping [73.30245214374027]
The software development industry is rapidly adopting machine learning for transitioning modern day software systems towards highly intelligent and self-learning systems.
No comprehensive study exists that explores the current state-of-the-art on the adoption of machine learning across software engineering life cycle stages.
This study introduces a machine learning for software engineering (MLSE) taxonomy classifying the state-of-the-art machine learning techniques according to their applicability to various software engineering life cycle stages.
arXiv Detail & Related papers (2020-05-27T11:56:56Z) - Thinking While Moving: Deep Reinforcement Learning with Concurrent
Control [122.49572467292293]
We study reinforcement learning in settings where sampling an action from the policy must be done concurrently with the time evolution of the controlled system.
Much like a person or an animal, the robot must think and move at the same time, deciding on its next action before the previous one has completed.
arXiv Detail & Related papers (2020-04-13T17:49:29Z)
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.