AI Is Reshaping Software Engineering: From the Fog of the Past to a True Revolution in Practice

The wave of self-generated AI is sweeping the world, and the field of software engineering is undergoing an unprecedented paradigm shift. However, in the early stages of this change, we are in the "middle age of AI" - a confusing stage full of huge gaps in expectations and reality. Many enterprises invest a lot of money in AI, but do not get the expected returns. Behind this is a series of profound challenges that we need to calmly examine.

We must be clear about several key points:

1. Investment dilemma in the middle era of AI : Many organizations have found that despite the huge investment in AI—including high computing power, model training and talent introduction costs—the improvement in software delivery efficiency and quality is far from expected. The ideal "silver bullet" effect has not appeared, and the "AI engine" with huge investments is often in a dilemma of idleness or inefficiency.

2. The tool hallucination of "old wine in new bottles": A common misunderstanding is that only AI efficiency improvement is a tool problem, not engineering problem, and think that by purchasing a large AI model, it can achieve efficiency improvement. This thinking is exactly the same as the "Jenkins superstition" years ago - Just like Jenkins back then, using Jenkins does not mean continuous integration. Tools are the carriers of practice, not practice itself. Without the transformation of processes, culture and thinking, no matter how powerful AI is, it can only become an expensive "code completion device" or "demand translator", and it is difficult to touch the core performance bottleneck of software engineering.

3. Mismatch and Reshaping of Human Talents : The arrival of the AI ​​era has put forward new requirements for the Human Talents . The existing talents do not have a good ability to control AI . We can’t help but ask: What abilities are people that should have and which abilities should be replaced by AI? Simple and repetitive coding skills may be weakened, but the importance of understanding the business, defining problems, designing plans, critically assessing AI output, and collaborating across fields will be unprecedentedly important. The lag in talent training and transformation is the key factor restricting AI from realizing its potential.

4. Coordination: Core constraints amplified by AI : Many organizations try to improve coding efficiency through AI, thereby achieving efficiency improvement. For example, use AI to automatically generate code or generate test cases. However, Software development ≠ code generation , even if AI can analyze the code base and generate available code in seconds, the core bottleneck of software development— Cooperation is still the biggest constraint. The complexity and uncertainty of links such as demand clarification, architectural decision-making, cross-team communication, business logic alignment, etc. cannot be simply replaced by AI automation. AI may be able to optimize local efficiency, but the improvement of overall efficiency still depends on high-quality collaboration between people. This once again strongly demonstrates the theory of constraints. The overall performance of the system depends on its weakest link. The improvement of overall efficiency by simply optimizing non-bottleneck links (such as coding speed) is limited, and may even have negative effects due to the intensification of the pressure on the bottleneck link.

On the basis of recognizing the above challenges and misunderstandings, in order to more systematically analyze and respond to the far-reaching impact of AI on software engineering, we can use the Cynefin cognitive framework to divide the problems and tasks in software engineering into different domains, and analyze how AI reshapes or spawns corresponding software engineering practices. The Cynefin framework divides problems into simple domains (Obvious), complex domains (Complex), and Chaotic domains (Chaotic). Each domain corresponds to different response strategies and AI-enabled engineering practice changes.

Obvious Domain: Automation Practice of Deterministic Tasks - AI Implementation "Replace"

In the Cynefin framework, simple domains deal with problems with clear causality and "known-known" relationships, and there are usually fixed "best practices". In software engineering, this corresponds to a lot of repetitive, mechanical, and standard answers. The value of AI in this field lies in promoting the automation of these tasks, thereby transforming relevant engineering practices and realizing the replacement of traditional manual operations.

• Automatic code scaffolding practice : The past practice of manually creating project structures, configuration files and basic modules is being replaced by AI tools. Developers can use simple instructions or configurations to enable AI to quickly generate initial code bases that comply with specific frameworks and team specifications, significantly improving project startup efficiency.

• Standardized unit test generation practice : For functions or modules with simple logic and clear input and output, AI can automatically generate unit test cases covering various boundary conditions based on specifications, changing the previous practice of relying on manual writing of 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 AI ​​will first generate business code, and then generate unit tests. This practice is like "reverse engineering" twice. From requirements to code, it is one understanding and transformation, and from code to test is another understanding and transformation. The information is easily distorted during the transmission process, and the effect may become worse and worse. In contrast, the practice of test-driven development (TDD)—written tests before implementing code—will better ensure code quality and requirements alignment. In this process, AI can help write tests or generate code based on tests to achieve its true value, rather than simply being a "code-to-test" converter.

• Enhanced Code Normalization and Static Inspection Practice : AI can serve as a "intelligent proxy" for code specification execution. It can not only check the code style like traditional tools, but also automatically correct irregular code based on a deeper understanding, and even provide optimization suggestions to make the code standardization practice more efficient and in-depth.

• Automatic generation and maintenance practice of basic technical documents : According to code comments, function signatures and even code logic itself, AI can automatically generate and update preliminary technical documents, API references, etc., reducing the burden on developers to maintain documents and changing the common situation where documents lag behind code.

In simple domains, the application of AI has transformed the original relying on manual and repetitive engineering practices into highly automated processes, thereby improving the efficiency and consistency of basic work.

Complicated Domain: Enhanced Practice of Expert-level Tasks - AI Helps "Reinforce"

Complicated Domain deals with "known-unknown" problems. Although there is a causal relationship, it requires professional knowledge and in-depth analysis to find a solution. There may be multiple effective solutions. AI does not directly replace experts here, but serves as a powerful auxiliary tool to improve the efficiency and quality of existing expert-level software engineering practices.

• Intelligent auxiliary coding and debugging practices : AI-assisted programming tools are changing traditional coding and debugging practices by providing context-aware code completion, real-time error analysis and repair suggestions. Developers can write higher quality code faster and reduce troubleshooting time.

• New Practices of Code Review and Knowledge Sharing Assisted by AI : The core value of Code Review does not lie in finding defects, but more importantly, it promotes knowledge sharing, design idea alignment and code quality within the team. AI should not and cannot completely replace this process. A better practice is to first conduct a preliminary review of the code by AI, such as identifying potential “bad code tastes,” performance bottlenecks, or non-compliant patterns. Further, AI can combine complex code snippets with business context to make preliminary abstractions, such as generating simplified flowcharts, logical summary or key dependency diagrams, and making preliminary revision suggestions. Information that has been "preprocessed" and "refined" by AI will be handed over to human reviewers. In doing so, AI plays the role of a "review assistant" and significantly simplifies the burden on human reviewers by providing more structured and insightful inputs, allowing them to focus more on high-level design rationality, business logic accuracy, and knowledge transfer and absorption, thereby improving the overall efficiency and value of Code Review.

• Intelligent task decomposition and planning practice : For complex projects or features, senior engineers or architects have traditionally relied on task decomposition. AI can assist in more detailed and reasonable task disassembly by analyzing requirements descriptions, existing code base structures, and even historical project data, identify potential dependencies and risk points, and assist in preliminary resource estimation and planning. Further, AI can also try to understand and assist "invisible knowledge" or "intuitive judgment" that is difficult to express clearly by analyzing a large number of project cases and engineers' decision-making patterns, such as identifying potential integration difficulties, estimating the possible team running-in costs of specific technology selection, etc., thereby assisting engineers in making more comprehensive solutions rather than completely replacing engineers' professional judgments. 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 intelligence.

• Advanced test case design and defect prediction practice based on AI : In addition to basic unit testing, AI can analyze historical data and code changes, predict modules that may be introduced, and assist in designing more complex integrated testing and system testing scenarios, improving the pertinence and effectiveness of testing practices.

• AI-driven security vulnerability scanning and repair recommendations : AI can perform static and dynamic code analysis more intelligently, identify complex security vulnerabilities, and provide specific repair solutions and code examples, strengthening security guarantee practices in the Software Security Development Life Cycle (SSDLC).

In complex areas, AI-enabled engineering practice is manifested as amplification of expert capabilities and optimization of workflows, making complex analysis and decision-making processes more efficient and accurate.

Complex Domain: An innovative practice of uncertainty exploration - AI empowers "creation" (Reveal)

Complex Domain is an "unknown-unknown" field, full of emergent behavior and unpredictability, and cannot find a fixed answer through pre-analysis. It is necessary to explore iteratively through "detection-perception-response". The core value of AI in this field lies in the creation of new software engineering practices, rather than simple optimization of existing practices.

• Intent-Driven Development (IDD) : This is a brand new paradigm for software development. Developers use natural language or advanced regulations to describe desired system functions, features, and constraints (i.e., "intentions"), and AI systems are responsible for converting these intents into specific architectural designs, code implementations, test suites, and deployment scripts. This practice shifts the core of development from "how to implement" to "what to expect", which greatly changes the way software is produced. This is not only an innovation in tools, but also requires developers to transform from traditional ‘code craftsmen’ to ‘intention designers’ and ‘AI collaborative partners’, posing new challenges to both the skill stack and thinking patterns. It should be noted that at the current stage, this development method may be more suitable for proof of concept (POC) projects or rapid prototyping in specific scenarios, as it can quickly verify ideas with a lower resource investment. What is generated may be a large number of "daily thrown" applications with short life cycles. How these applications effectively "integrate" with the complex application systems that the organization has existing, and how to manage the life cycle of these rapid iterative or even one-time applications may be a problem that future software engineering needs to focus on exploring and solving.

• AI-driven adaptive and self-evolutionary system architecture practice: Future software architecture practices will focus more on building systems that can respond to environmental changes and optimize independently. AI can monitor system status, user behavior and external conditions in real time, dynamically adjust system parameters, resource allocation and even code logic, and realize the system's continuous adaptation and performance optimization without human intervention or a small amount of intervention. This is a new "live" architectural practice.

• The quality assurance practice of generative exploratory testing and continuous learning : AI can not only generate tests for preset scenarios, but also design and execute exploratory tests based on understanding and learning of system behavior, simulate complex user interactions and abnormal conditions, so as to discover deep defects that are difficult for traditional testing methods to cover. This practice has pushed quality assurance to a new level of continuous learning and intelligent discovery.

• Developer Workflow Practice for Personalization and Situation Awareness : AI has the potential to build a highly personalized development environment and workflow based on each developer’s skills, preferences, current task context, and team collaboration dynamics. This practice aims to create an extremely efficient and smooth engineering experience for every developer through intelligent toolchain, information aggregation, knowledge recommendation and collaborative optimization.

In complex domains, AI has become a catalyst for exploring the unknown and inspiring innovation, giving birth to disruptive software engineering practices that fundamentally change the way we build and evolve software.

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

The Chaotic Domain is characterized by high instability and unpredictability, requiring rapid action to stabilize the situation and find a new order. The rapid introduction and wide application of AI technology itself has brought unprecedented reform pressure and a certain degree of "chaos" to the field of software engineering. In this emerging, rapidly evolving AI era, a series of new or redefined software engineering practices have become more important to ensure that AI can be effectively navigated and addressed the challenges it poses.

1. Engineering governance and structural optimization of AI generated code : AI dialogue programming may generate a large amount of code, but the initial organization, stratification and division of responsibilities of these codes are often lacking. At this point, the software engineering capabilities of engineers become crucial. Engineers need to actively guide AI, put forward clear structured requirements (for example, following specific design patterns and modular principles), and refactor and organize the code generated by AI to ensure the maintainability, scalability and clarity of the code base, preventing a large number of generated code from falling into an out-of-control state, and forming new technical debts.

2. Constructing and evolving complex system engineering that integrates AI : As AI components increasingly embedded in software systems, how to design, build, test and maintain these complex systems containing uncertain AI modules has become a core engineering practice. This requires engineering teams to have a holistic perspective, focusing on the interface between AI and traditional components, data flow, performance collaboration, and the robustness and evolution of the overall system.

3. Prudent verification and iterative optimization process for AI-assisted decision-making : AI can provide powerful analysis and suggestions, but its output is not absolutely reliable. Therefore, it is crucial to establish an engineering practice that carefully verifies AI-generated content (such as code, test cases, architectural recommendations). This includes well-defined verification criteria, a process that combines manual review with AI verification, and a closed-loop mechanism that iteratively optimizes AI models and prompts based on feedback.

4. Domain Modeling and Engineering Injection of Knowledge for AI : In order to make AI more effectively understand and solve problems in specific fields, it has become a key practice to structure and formalize domain knowledge and "inject" into the development process in an understandable way by AI. This involves creating rich domain ontology, high-quality training data sets, and effective knowledge representation methods to improve AI performance in specific scenarios.

5. Continuous experimentation and evolution management of adaptive AI technology stack : AI technologies and tools are changing with each passing day. Software teams need to establish practices that continuously experiment with new AI technologies, evaluate their suitability, and integrate them flexibly into existing technology stacks and development processes. This requires an adaptive engineering culture that embraces change, learns quickly, and iterates new tools.

6. The software development model and knowledge sharing mechanism of human-computer collaboration : Future software development will be more reflected in the collaborative operation between human engineers and AI agents. Therefore, exploring and establishing efficient human-machine collaboration models, including clear task allocation, effective communication interfaces, shared knowledge bases, and interpretation and traceability mechanisms for AI behaviors, has become an important engineering practice.

7. Build talent and organizational capabilities in the AI ​​era: Enterprises should actively decompose existing work processes and skills needs in detail, and clarify which tasks and decisions rely more on human strategic thinking, complex problem solving, creative thinking, emotional intelligence and ethical judgments - these are things and abilities that need to be strengthened by people. At the same time, identify those repetitive, pattern-based, and efficient tasks that can be processed by AI, gradually weaken the dependence on these purely execution-level artificial skills, and invest the saved human resources into higher value activities. In order to achieve this transformation, organizations must actively provide access and training for AI tools, help employees master the skills of collaborating with AI through systematic tutoring mechanisms, and design and promote rich AI application scenarios in combination with business pain points, so that employees can learn and grow in practice. This is not just skills training, but also a systematic construction of talent echelons and organizational structures that meet the needs of the AI ​​era. It may involve redefining job responsibilities, designing new human-machine collaboration processes, establishing a cultural atmosphere that encourages innovation and tolerance, and ultimately creating a learning organization that can fully utilize the potential of AI and continue to evolve.

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

Conclusion: Embrace change and reshape the future

We are standing at the dawn of a new era of software engineering. AI does bring pain and myths of the "middle age", but its far-reaching impact is by no means just the improvement of efficiency at the tool level. It is fundamentally reshaping the definition, development process and role of engineers in software.

Walking through the current fog, we must abandon the illusion of treating AI as a simple tool and realize that it is a profound practical revolution. This revolution will not disappear from software engineers, but will free them from heavy, replaceable labor and push them to higher levels of the value chain: becoming the definers of complex problems, designers of innovative solutions, masters of AI capabilities, guardians of system quality, connecters of cross-border collaboration, and responsible persons of technical ethics.

The real challenge is not to learn how to use an AI tool, but to reshape our way of thinking, 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 full of infinite possibilities and creativity.