Enterprise AI Analysis
A Research Roadmap for Augmenting Software Engineering Processes and Software Products with Generative AI
Generative AI (GenAI) is rapidly transforming software engineering (SE) practices, influencing how SE processes are executed, as well as how software systems are developed, operated, and evolved. This paper applies design science research to build a roadmap for GenAI-augmented SE. The process consists of three cycles that incrementally integrate multiple sources of evidence, including collaborative discussions from the FSE 2025 "Software Engineering 2030" workshop, rapid literature reviews, and external feedback sessions involving peers. McLuhan's tetrads were used as a conceptual instrument to systematically capture the transforming effects of GenAI on SE processes and software products. The resulting roadmap identifies four fundamental forms of GenAI augmentation in SE and systematically characterizes their related research challenges and opportunities. These insights are then consolidated into a set of future research directions. By grounding the roadmap in a rigorous multi-cycle process and cross-validating it among independent author teams and peers, the study provides a transparent and reproducible foundation for analyzing how GenAI affects SE processes, methods and tools, and for framing future research within this rapidly evolving area. Based on these findings, the article finally makes ten predictions for SE in the year 2030.
Executive Impact & Key Metrics
GenAI implies a paradigm shift in SE, profoundly reshaping both development processes and the nature of software products. To navigate this complex transformation, we introduced a systematic framework for understanding and analyzing the impact of GenAI on SE. We proposed a structuring of GenAI augmentation along two principal dimensions: what is being augmented (process versus product) and the level of autonomy of the augmentation (passive versus active). This categorization yields four distinct and tangible forms of GenAI augmentation: the GenAI Copilot, GenAIware, the GenAI Teammate, and the GenAI Robot. Using this framework as our analytical lens, we conducted an examination of the state-of-the-art for each of the four forms. Our methodology - grounded in a multi-cycle design science approach - employed rapid literature reviews to gather evidence, which is then synthesized using McLuhan's tetrads. This approach enabled a holistic assessment of each form of GenAI augmentation, identifying what it enhances, what established practices it reverses when pushed to extremes, what past concepts it retrieves, and what it may render obsolete. This multidimensional analysis revealed a complex interplay of benefits and challenges, from enhanced productivity and retrieved formalisms and paradigms to issues such as trustworthiness, accountability, and the potential erosion of human expertise.
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Code Autonomy by 2030
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Prompt Engineering Job Postings by 2030
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Regulated Projects with AI Compliance by 2030
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
GenAI Copilot
GenAI Teammate
GenAIware
GenAI Robot
GenAI Copilots enhance tasks like requirement elicitation, code generation, testing, and project management. They improve efficiency and quality, but also introduce concerns regarding trustworthiness, explainability, and accountability. Traditional manual debugging and documentation may become obsolete.
GenAI Teammates proactively collaborate with human engineers, automating software development, accelerating time to market, and enhancing debugging skills. However, this raises issues of Git versioning relevance, environmental sustainability (due to LLM costs), accountability, and the erosion of code comprehension.
GenAIware involves GenAI models integrated as software components, enhancing prompt programming, data management, and user experience. Challenges include ensuring reliability, standardization, transparency, and efficiency. It retrieves manual evaluation and runtime verification but obsoletes manual annotation and traditional static testing.
GenAI Robots deliver autonomous functionality within software systems, leading to novel human-computer interactions and increased human-AI team performance. Concerns arise about human-agent balance, traditional separation of concerns, and static assurance of legal/ethical norms. Agent-oriented Software Engineering (AOSE) concepts are retrieved, while natural language AI-to-AI communication may become obsolete.
Enterprise Process Flow
Initial Investigation
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Preliminary Design
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Final Artifact Development
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of boilerplate code will be autonomously generated by GenAI Teammates by 2030.
| Form | Description | Autonomy Level |
|---|---|---|
| GenAI Copilot | GenAI is used as a tool to automate various SE tasks (e.g., code generation, testing). | Passive Role |
| GenAIware | GenAI is used to realize software functionality, invoked from code to perform specific computations. | Passive Role |
| GenAI Teammate | GenAI acts as an agent proactively participating in software development processes, collaborating with humans. | Active Role |
| GenAI Robot | GenAI acts as an autonomous, goal-driven agent to deliver parts of software system functionality. | Active Role |
The Shift in Software Engineering Roles
The role of the software engineer will shift from manual coders to oversight and strategic decision makers, allowing them to focus on more complex and innovative problem solving activities. This means that manual coding will become obsolete for routine functionality.
This paradigm shift is driven by autonomous GenAI agents capable of handling iterative development, debugging, and quality assurance autonomously. This frees up human developers to engage in higher-level architectural and orchestrational tasks.
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Annual Cost Savings
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Annual Hours Reclaimed
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Phased Implementation Roadmap
The overall roadmap consists of five parts for each form of GenAI augmentation, and for cross-form aspects. It provides a structured and evidence-based perspective, refining and updating existing roadmaps, serving as part of an overall prescriptive guide for the SE community contained within this special issue.
Death of manual coding for routine tasks
By 2030, 70% of boilerplate code, CRUD operations, and standard API integrations will be autonomously generated by GenAI Teammates with zero human keystrokes. Human developers will spend less than 20% of their time writing code from scratch. Our roadmap indicates that manual coding will become obsolete. The shift from developer-as-coder to developer-as-architect/orchestrator will be complete for routine functionality.
Prompt Engineering as a core SE discipline
By 2030, "Prompt Architect" will be a recognized job title with professional certifications. Universities will offer dedicated courses on prompt engineering, and 40% of SE job postings will list prompt engineering as a required skill alongside traditional programming languages. Our roadmap indicates that prompt programming will be a new "programming" paradigm. As GenAIware proliferates, prompt quality directly determines system reliability.
Emergence of "Software Compliance as Code"
By 2030, legal compliance, ethical constraints, and safety requirements will be encoded as formal guardrails in machine-readable formats (e.g., "compliance specification languages") that GenAI systems must satisfy in real-time. 40% of regulated software projects will use automated compliance verification that checks both human and AI contributions against these formal specifications. The EU AI Act and similar regulations will mandate this for high-risk AI systems.
Rise of AI accountability standards
By 2030, at least three major organizations (ACM, IEEE, or ISO) will have published formal standards for AI accountability in software development, contributing to the aforementioned "software compliance as code". 50% of Fortune 500 companies will require explicit "AI Attribution Metadata" in their codebases, documenting which code was human-written, AI-suggested, or AI-autonomous.
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