Technological advancements in no/low-code platforms and generative AI are reshaping software creation. This evolution is blurring the traditional boundaries between stakeholders, product owners, and development teams, making software development accessible to a broader spectrum of business users.
Now, a more diverse audience is stepping into the software creation arena, unlocking new opportunities, and driving innovation in software engineering. This shift presents engineering leaders and product teams with a myriad of decisions when embarking on new service or application projects, along with significant challenges to navigate.
In this article, we delve into the various options available in this transforming landscape, evaluating their potential benefits and drawbacks for businesses of different scales and complexities.
Low-Code and No-Code Development:
Low-code and no-code development platforms, such as Bubble, Retool, Zapier, Backendless, and Flutterflow, are reshaping the landscape of software engineering. These platforms offer intuitive, drag-and-drop interfaces that enable rapid application development without extensive coding knowledge, democratising software creation and inviting diverse contributions. This shift is particularly significant for businesses seeking to build applications quickly and efficiently.
However, these platforms come with limitations:
Limitations of Platforms:
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Simplification vs. Flexibility: Simplifies deployment but may restrict customisation.
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Final Functionality Challenge: Achieving the last 10%-20% functionality often requires complex solutions or workarounds.
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Code Quality Concerns: These solutions can affect code quality and maintainability in the future.
Adaptability and Scaling:
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Scaling and Customising: Adapting these applications to evolving needs may lead to a complete rebuild, wasting the original development time.
Therefore, the decision to use low-code platforms versus traditional software development methods involves a strategic trade-off. For simple business applications with minimal features, low-code platforms can be an effective solution. However, for more complex, scalable, and high-performance applications, traditional software development approaches may be more appropriate.
The Rise of AI in Software Engineering
2023 has marked AI’s significant impact in various domains, with software engineering being a notable area of transformation. Developers are increasingly adopting large language models and generative AI to streamline the development process. These tools, including ChatGPT, Claude, and GitHub Copilot, are enhancing productivity and code quality.
Key highlights include:
Widespread Adoption:
GitHub reports that 92% of U.S.-based developers in large companies use AI coding tools, with 70% experiencing substantial benefits.
Value for Novice Developers:
AI tools assist new developers in producing high-quality code, offering an alternative to low-code platforms where more flexibility is needed.
Diverse Applications:
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Building functional proofs of concept rapidly.
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Conducting A/B testing with real users.
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Deploying fully tested applications based on real-time feedback.
However, the integration of AI in software engineering also presents challenges:
Performance and Security Risks:
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The emergence of fake open-source repositories generated by malicious AI.
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Vulnerability of machine learning models to manipulated public datasets.
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Lack of transparency in AI/ML decision-making processes.
Because of these challenges, AI-generated code can’t be responsibly released without the developer understanding and owning it.
This emphasises the need for:
Best Practices in Engineering: Ensuring reliability and managing business risk through established engineering practices.
Agreed architecture concepts and support models: By defining tooling and ways of working early, teams can be empowered to create at speed without risking future support or maintenance issues in the future.
Changes in Ways of Working for 2024
The evolution of software development, influenced by low/no-code platforms and AI-assisted technologies, is reshaping how teams operate and collaborate.
Key Working trends and shifts include:
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Efficiency Gains: Both low/no-code and AI-assisted development are significantly reducing development, test, and release cycle times.
- Need for Enhanced Collaboration: A GitHub survey highlighted the importance of internal collaboration and timely customer feedback in software development. 39% of respondents emphasised the value of direct feedback from customers in positively impacting their workday.
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Rapid Iteration: With shorter development cycles, Engineering and delivery leads must prioritise efficient feedback loops between customers and developers to avoid bottlenecks and minimise developer frustration.
The rapid change in software development is not only changing how we build software but also how manage teams and monitor performance.
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Focus Shift: As AI tools become more prevalent, the ability to understand, maintain, and upgrade the tech stack is becoming crucial. This requires a shift in how development teams structure their time.
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Volume vs. Value: Approximately one-third of developers report that performance assessment is currently based on code volume. This needs to shift towards innovation, problem-solving and the quality and business value of the code produced.
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Holistic Performance Evaluation: Leaders need to develop more comprehensive evaluation criteria that reflect the changing nature of software development and the increasing role of AI.
Software Engineer Support From Reed PS
To explore how we can support you in building an AI-ready infrastructure, check out Reed’s Software Development & Engineering services. Contact our Client Solutions Director, Simon Christopher, for innovative efficient software solutions tailored to your business needs.
