AI Transforming Software Engineering: Cutting Through the Fog of the Middle Ages to See the True Revolution in Practice

With the wave of self-generative AI sweeping the world, the software engineering field is experiencing an unprecedented paradigm shift. However, at the beginning of this change, we are in the " in-between era of AI " - a stage of confusion filled with great expectations and a gap between reality and reality. Many organizations have invested heavily in AI, but have not realized the expected benefits. This reveals a series of profound challenges that we need to examine calmly.

We must wake up to several key points:

1. The investment dilemma in the era of intermediate AI: Many organizations have found that despite investing heavily in AI, including high costs for arithmetic, model training, and talent acquisition, the improvement in software delivery efficiency and quality is far from meeting expectations. The ideal "silver bullet" effect has not appeared, and the huge investment in the "AI engine" is often stuck in an idle or inefficient predicament.

2. The tool illusion of "old wine in new bottles": a common misunderstanding is that AI efficiency is only a tool problem, not an engineering problem, thinking that the purchase of a large AI model can achieve efficiency. This kind of thinking is the same as the "Jenkins superstition" many years ago - just like Jenkins back then, using Jenkins is not the same as doing continuous integration. The tool is the vehicle for the practice, not the practice itself. In the absence of process, culture, and mindset change, even the most powerful AI can only be reduced to an expensive "code completer" or "requirements translator", and it is difficult to touch the core performance bottleneck of software engineering.

3. Mismatch and reshaping of talent ability: The arrival of the AI era has put forward brand new requirements for talent ability. Existing talents do not have a good command of AI capabilities. We can't help but ask: what are the abilities that people should have, and what abilities should be replaced by AI? Simple and repetitive coding skills may be weakened, but the ability to understand the business, define problems, design solutions, critically evaluate AI outputs, and collaborate across domains will be more important than ever. The lag in talent development and transformation is a key constraint on AI's potential.

4. Collaboration: a core constraint amplified by AI: Many organizations are attempting to achieve efficiency gains by enhancing coding through AI. For example, using AI to automate code generation or generate test cases. However, software development ≠ code generation , and even if AI can analyze a code base and generate usable code in seconds, collaboration , the core bottleneck of software development, remains the biggest constraint . The complexity and uncertainty of requirements clarification, architectural decisions, cross-team communication, business logic alignment, etc., cannot simply be replaced by AI automation may be able to optimize local efficiency, but the improvement of overall performance is still highly dependent on high-quality collaboration between people. This is another strong argument for the Theory of Constraints - the overall performance of a system depends on its weakest link, and simply optimizing non-bottleneck links (such as coding speed) will have a limited effect on the overall efficiency of the system, and may even exacerbate the pressure on bottlenecks, resulting in negative effects. It may even have a negative effect by increasing the pressure on the bottleneck.

Based on the above challenges and misconceptions, in order to systematically analyze and respond to the profound impact of AI on software engineering, we can use the Cynefin cognitive framework to classify problems and tasks in software engineering into different domains, and analyze how AI reshapes or generates corresponding software engineering practices. The Cynefin framework categorizes problems into Obvious, Complicated, Complex, and Chaotic domains, each of which corresponds to different coping strategies and AI-enabled changes in engineering practices.

Obvious Domain: Automation of Deterministic Tasks - AI Enables Replacement

In the Cynefin framework, the Simple Domain deals with causally explicit, "known-known" problems for which there are usually fixed "best practices". In software engineering, this corresponds to a large number of repetitive, mechanical, standard-answer tasks, and the value of AI in this domain is to drive the automation of these tasks, thereby transforming the associated engineering practices and enabling the replacement of traditional manual operations.

• Automated code scaffolding practices: The previous practice of manually creating project structures, configuration files, and base modules is being replaced by AI tools. Developers can use simple commands or configurations to allow AI to quickly generate an initial codebase that meets specific framework and team specifications, significantly improving project startup efficiency.

• Standardized unit test generation practice: For functions or modules with simple logic and clear inputs and outputs, AI can automatically generate unit test cases covering various boundary conditions based on specifications, changing the previous practice of relying on manually writing basic tests and improving the breadth and speed of test coverage. However, it is necessary to be vigilant that if the order is reversed, that is, the business code is generated by the AI first, and then the unit test is generated by the AI, this practice is like carrying out two "reverse engineering", from the requirements to the code is a time of understanding and conversion, and from the code to the test is another time of understanding and conversion, and the information is easy to distort the transmission process, and the effect may be The information is easily distorted in the process of transmission, and the effect may get worse and worse. In contrast, the practice of test-driven development (TDD) - writing tests before writing implementation code - better ensures the quality of code and requirements alignment, and AI can only realize its true value by assisting in writing tests or generating code based on tests in this process. AI can be of real value when it assists in writing tests or generating code based on tests in this process, rather than simply acting as a "code-to-test" converter.

• Enhancement of code standardization and static checking practices: AI can be used as an "intelligent agent" for code standardization, not only checking the code style like traditional tools, but also automatically correcting non-standard code based on deeper understanding, and even providing optimization suggestions, which makes code standardization practices more efficient and in-depth.

• Automated Generation and Maintenance of Basic Technical Documentation: Based on code comments, function signatures, and even the code logic itself, AI can automatically generate and update preliminary technical documentation, API references, and so on, which reduces the burden of developers in maintaining documentation and changes the common situation of documentation lagging behind the code.

In the Simple domain, AI enables the transformation of manual, repetitive engineering practices into highly automated processes, thus improving the efficiency and consistency of basic work.

Complicated Domain: Enhanced Practices for Expert Level Tasks - AI Enabled Reinforcement

The Complicated Domain deals with "known-unknown" problems that require specialized knowledge and in-depth analysis to find a solution despite the existence of cause-and-effect relationships, and for which multiple effective solutions may exist. efficiency and quality of existing expert software engineering practices.

• Intelligent Assisted Coding and Debugging Practices: AI-assisted programming tools are transforming traditional coding and debugging practices by providing context-aware code completion, real-time error analysis, and repair recommendations. Developers are able to write higher-quality code faster and reduce troubleshooting time.

• AI-assisted Code Review and Knowledge Sharing: The core value of code review is not to find defects, but to facilitate knowledge sharing, design alignment, and code quality guarding within a team, a process that AI shouldn't and can't completely replace. A better practice is for AI to perform an initial review of the code, such as identifying potential "bad code flavors," performance bottlenecks, or patterns of non-compliance. Further, the AI can make initial abstractions of complex code pieces in the context of the business, such as generating simplified flowcharts, logical summaries, or diagrams of key dependencies, and make initial recommendations for changes. The information "pre-processed" and "refined" by the AI is then passed on to a human reviewer. In doing so, the AI plays the role of a "review assistant", and by providing more structured and insightful inputs, it significantly simplifies the burden on the human reviewer, allowing him or her to focus more on high-level design rationality, business logic accuracy, and knowledge transfer and assimilation, thus improving the overall efficiency and value of the Code Review. Intelligent Task Decomposition and Planning Practice: Intelligent task decomposition and planning practice.

• Intelligent Task Decomposition and Planning Practice: For complex projects or features, traditionally relying on senior engineers or architects to decompose tasks, AI can analyze requirement descriptions, the structure of the existing codebase, and even historical project data to assist in more detailed and reasonable task decomposition, identify potential dependencies and risks, and assist in the initial resource estimation and planning. Further, AI can also analyze a large number of project cases and engineers' decision-making patterns to try to understand and assist those "invisible knowledge" or "intuitive judgments" that are difficult to express explicitly, such as identifying potential integration difficulties, estimating the team integration costs that may be brought by a specific technology selection, etc. AI can also help to identify potential dependencies and risks, and assist in preliminary resource estimation and planning. It assists engineers in making more comprehensive decomposition decisions, rather than replacing their professional judgment. This improves the accuracy and efficiency of project planning and transforms the decomposition process from purely relying on personal experience to assisted decision-making that combines data-driven and empirical wisdom.

• AI-based advanced test case design and defect prediction practices: In addition to basic unit testing, AI is able to analyze historical data and code changes, predict modules that may introduce defects, and assist in the design of more complex integration testing and system testing scenarios, which improves the relevance and effectiveness of testing practices.

• AI-driven Security Vulnerability Scanning and Remediation Suggestion Practice: AI can more intelligently perform static and dynamic code analysis, identify complex security vulnerabilities, and provide specific remediation solutions and code examples, strengthening the security assurance practice in the Software Security Development Life Cycle (SSDLC).

In the Complex Domain, AI-enabled engineering practices are manifested in the amplification of expert capabilities and the optimization of workflows, making the complex analysis and decision-making process more efficient and precise.

Complex Domain: Innovative Practices for Uncertainty Exploration -- AI-enabled Reveal

Complex Domain is an "unknown-unknown" domain, full of emergent behaviors and unpredictability, which cannot be analyzed in advance to find a fixed answer, and needs to be explored through the iterative exploration of "detecting-perceiving-responding", and AI's core value in this domain is to create (Reveal) brand new software. The core value of AI in this area is to create new software engineering practices rather than simply optimizing existing ones.

• Intent-Driven Development (IDD): This is a new paradigm for software development. Developers describe desired system functions, features, and constraints (i.e., "intents") using natural language or high-level protocols, and AI systems are responsible for translating these intents into concrete architectural designs, code implementations, test suites, and deployment scripts. This practice has dramatically changed the way software is produced by shifting the focus of development from "how to achieve" to "what to expect". This is not only a revolution in tools, but also requires developers to transform from traditional 'coders' to 'intent designers' and 'AI collaboration partners', posing a whole new challenge to the skill stack and mindset. This poses a whole new challenge to both the skill stack and mindset. It is important to note that at this stage, this development approach may be more suitable for proof-of-concept (POC) projects or rapid prototyping in specific scenarios, as it allows for rapid validation of ideas with lower resource investment. The result may be a large number of "daily throwaway" applications with short lifecycles. How to effectively "integrate" these applications with an organization's existing complex applications, and how to manage the lifecycle of these rapidly iterative or even disposable applications, may be a key issue for software engineering to explore and address in the future.

• AI-driven Adaptive and Self-evolving System Architecture Practice: Future software architecture practice will pay more attention to building systems that can respond to changes in the environment and optimize on their own. ai can monitor the system state, user behavior, and external conditions in real time, and dynamically adjust the system parameters, resource allocation, and even the code logic, so as to achieve continuous self-adaptation and performance optimization of the system without intervention or a small amount of intervention, which is a brand new " living" system. This is a new kind of "live" architecture practice.

• Generative Exploratory Testing and Continuous Learning Quality Assurance Practice: AI not only generates tests for preset scenarios, but also can design and execute exploratory tests autonomously based on its understanding and learning of system behaviors, simulating complex user interactions and abnormal conditions, in order to discover deep defects that are difficult to be covered by traditional testing methods. This practice pushes quality assurance to a new level of continuous learning and intelligent discovery.

• Developer Workflow Practices for Personalization and Context Awareness: AI has the potential to build highly personalized development environments and workflows based on each developer's skills, preferences, current task context, and team collaboration dynamics. This practice is designed to create an extremely efficient and fluid engineering experience for each developer through intelligent toolchaining, information aggregation, knowledge recommendation, and collaboration optimization.

In the Complex Domain, AI becomes a catalyst for exploring the unknown and inspiring innovation, giving rise to disruptive software engineering practices that fundamentally change the way we build and evolve software.

Chaotic Domain and the Age of AI: Emerging Key Software Engineering Practices - More Important

Chaotic domains are characterized by high levels of instability and unpredictability, requiring rapid action to stabilize the situation and find a new order, and the rapid introduction and widespread adoption of AI technologies is itself creating unprecedented pressure for change and a degree of "chaos" in the software engineering field. In this emerging and rapidly evolving AI era, it is more important to have a set of new or redefined software engineering practices to ensure that AI can be effectively navigated and the challenges it brings can be addressed.

1. Engineering governance and structural optimization of AI-generated code: AI conversational programming may produce large amounts of code, but the initial organization, layering, and delineation of responsibilities for that code is often lacking. At this point, the engineer's ability to engineer software becomes critical. Engineers need to proactively guide the AI by making clear structuring requirements (e.g., following specific design patterns, modularity principles) and performing the necessary refactoring and organization of AI-generated code to ensure the maintainability, scalability, and clarity of the codebase, and to prevent the mass-generated code from falling out of control and creating new technical debt.

2. Engineering complex systems for building and evolving integrated AI: As AI components are increasingly embedded in software systems, how to design, building, testing, and maintain these complex systems containing uncertain AI modules becomes a core engineering practice. This requires engineering teams to take a holistic view, focusing on the interfaces between AI and legacy components, data flow, performance synergies, and overall system robustness and evolvability.

3. Prudent validation and iterative optimization process for AI-assisted decision making: AI can provide powerful analysis and recommendations, but its output is not absolutely reliable. Therefore, it is critical to establish a set of engineering practices for prudent validation of AI-generated content (e.g., code, test cases, architectural recommendations). This includes well-defined validation criteria, a process that combines manual review with AI calibration, and a closed-loop mechanism for iterative optimization of AI models and hints based on feedback.

4. AI-Oriented Domain Modeling and Knowledge Engineering Infusion: In order for AI to more effectively understand and solve domain-specific problems, it is a key practice to structure, formalize, and "infuse" domain knowledge into the development process in a way that is understandable to AI. This involves creating rich domain ontologies, high-quality training datasets, and effective knowledge representations to improve AI performance in specific scenarios.

5. Continuous Experimentation and Adaptive AI Technology Stack Evolution Management: AI technologies and tools are changing rapidly. Software teams need to establish the practice of continuously experimenting with new AI technologies, evaluating their applicability, and flexibly integrating them into existing technology stacks and development processes. This requires an adaptive engineering culture that embraces change, learns quickly, and adopts new tools iteratively.

6. Human-machine collaborative software development model and knowledge sharing mechanism: Future software development will be more of a collaborative effort between human engineers and AI. Therefore, it becomes an important engineering practice to explore and establish an efficient human-machine collaboration model, including clear task allocation, effective communication interfaces, a shared knowledge base, and an explanation and tracing mechanism for AI behaviors.

7. Build talent and organizational capabilities in the AI era: companies should proactively break down existing workflows and skill requirements into careful competencies, clarifying which tasks and decisions are more reliant on human strategic thinking, complex problem solving, creative thinking, emotional intelligence, and ethical judgment-those are the things and capabilities that need to be reinforced for people to do. At the same time, identify tasks that are repetitive, pattern-based, and can be efficiently handled by AI, gradually weakening the reliance on these purely executive-level human skills and investing the saved human resources in higher-value activities. In order to achieve this transformation, organizations must actively provide access to AI tools and training, help employees master the skills of collaborating with AI through a systematic coaching mechanism, and design and promote rich AI application scenarios in conjunction with business pain points, so that employees can learn and grow in practice. This is not just about skills training, but also about systematically building a talent echelon and organizational structure that meets the needs of the AI era, which may involve redefining job responsibilities, designing new human-computer collaboration processes, and establishing a culture that encourages innovation and tolerance for mistakes, ultimately creating a learning organization that can make full use of the potential of AI and continue to evolve.

In the wave of change brought about by AI, the maturity and effectiveness of the above engineering practices will directly determine whether an organization can successfully leverage AI to enhance its software engineering capabilities and cope with the ensuing complexity and uncertainty.

Conclusion: Embracing Change, Reinventing the Future

We stand at the dawn of a new era in software engineering, and while AI does bring with it the pains and myths of the "in-between era," its impact is far more far-reaching than just tool-level efficiency gains. It is fundamentally reshaping the definition of software, the development process, and the role of the engineer.

Through the current fog, we must abandon the illusion of AI as a simple tool and recognize it as a profound revolution in practice. This revolution will not make software engineers disappear, but rather liberate them from heavy, replaceable labor and push them higher up the value chain: to become the definers of complex problems, the designers of innovative solutions, the harnessers of AI capabilities, the guardians of system quality, the connectors of cross-boundary collaborations, and the emboldeners of technological ethics.

The real challenge is not to learn how to use a particular AI tool, but to reshape our mindset, workflow, talent development system, and organizational culture, and to actively explore and adopt the emerging and more critical software engineering practices mentioned above. Only by embracing this fundamental change can we truly harness the power of AI and push software engineering to a new peak of infinite possibilities and creativity.