Software Engineering 3.0 Declaration (2026): AI‑Driven Evolution of Development Practices

The "Software Engineering 3.0 Declaration" was first issued in 2023; the earlier version is explained in the book *Software Engineering 3.0* (published May 2025). This 2026 edition updates the manifesto and will be followed by a series of related works such as "Software Engineering 3.0 Development Principles" and "Software Engineering 3.0 Best Practices".

With the rapid rise of large models and autonomous agents, software R&D output, team division, and organizational capability are being fundamentally reshaped. Traditional professional value in software engineering is undergoing a transformation, driving development and operations toward an industrialized era of intelligent assets. The authors argue that humans must deliberately preserve and reshape their irreplaceable value to manage the exponential growth of complexity.

Human role: From "code builder" to "value selector, intent leader, and result verifier".

Software role: From "code artifact" to "measurable, evolvable expression of intent value".

Engineering focus: From "solving complexity and efficient construction" to "production, management, and value flow of intelligent assets".

1. Business‑intent‑centered human‑AI collaboration beats emphasis on individual coding ability. In an era where AI‑generated code becomes routine, the professional value of developers shifts from writing code to translating business goals, risk boundaries, and value standards into clear, executable intent. Humans define the “What” and “Why”, AI efficiently implements and proposes solutions, and humans ultimately select and judge the outcomes.

2. Evolvable structured knowledge and memory (intelligent assets) beat static processes and text documents. Traditional process documents and static rules cannot cope with the complexity of emerging intelligent systems. The focus is on structured knowledge graphs that are inferable, evolvable, and reusable across projects, serving as the explicit brain of the organization and the foundation for AI reasoning and decision‑making.

3. Executable acceptance intent (verifiable business contracts) beats treating code as the goal. When AI can generate code, tests, architectures, and even decisions at scale, code itself is no longer the core asset. The authors stress the importance of executable acceptance intent as a "living contract" and the "constitution" of software systems, formalizing business goals, quality standards, and user expectations into automatically verifiable specifications.

4. Heterogeneous agents’ adversarial and cooperative mechanisms beat single‑model blind generation. Relying on a single AI model for blind generation lacks intrinsic quality safeguards. The manifesto advocates a competitive‑evolution approach where, for example, an AI developer and an AI tester use different base models to perform adversarial validation. Continuous internal competition and "algorithmic honesty" ensure high‑quality output in massive generation scenarios.

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