From Declarative to Iterative: How Software Development is Evolving

Software development is an ever-changing landscape. Over the years, it has become easier to generate high-quality code faster, though the definition of “faster” is a moving target. 

Take low-code tools, for example. With them, developers can build most of the functionality they need with the platform, so they only need to write the custom code the application requires. Low-code tools have also democratized software development -- particularly with the addition of AI.  

GenAI is accelerating development even further, and it’s changing the way developers think about code. 

Siddharth Parakh, senior engineering manager at Medable, expects Ai to “revolutionize” productivity. 

“The ability for AI to automate repetitive tasks, refactor code and even generate solutions from scratch would allow developers to focus on higher-order problem-solving and strategic design decisions,” says Parakh in an email interview. “With AI handling routine coding, developers could become orchestrators of complex systems rather than line-by-line authors of software.” 

But there’s a catch: Currently, AI-generated code cannot fully replace human intuition in areas such as creative problem solving, contextual understanding, and domain-specific decision-making. Also, AI models are only as good as the data they are trained on, which can lead to bias issues, error propagation or unsafe coding practices, he says. Quality control, debugging, and nuanced decision-making are still areas where human expertise is necessary. 

Related:What Developers Should Know About Embedded AI

How AI Helps 

The operative work is “automation.” 

“If AI takes over the majority of coding tasks, it would drive unprecedented efficiency and speed in software development,” says Medable’s Parakh. “Teams could iterate faster, adapt to changes more fluidly and scale projects without the traditional bottlenecks of manual coding. This could democratize software development, enabling non-experts to create functional software with minimal input.” 

Geoffrey Bourne, co-founder of social media API company Ayrshare, says GenAI coding assistants are now an integral part of his coding. 

“They produce lines of code which save me hours on a weekly basis. But, although the results are improving, they’re correct less than 40% of the time. You need the experience to know the code just isn’t up to scratch and needs adjusting or a redo,” says Bourne in an email interview. “Newbie coders are starting out with these assistants at their fingertips but without the years of experience writing code their seniors have. We’ve got to take this into account and not necessarily limit their access but find creative ways to inject that knowledge. You need to find a balance [between] the instant code fix with healthy experience and a critical eye.”  

Related:Let's Revisit Quality Assurance

The evolution of programming, especially through abstraction layers and GenAI, has significantly transformed the way Surabhi Bhargava, a machine learning tech lead at Adobe, approaches her work.  

“GenAI has made certain aspects of development much faster. Writing boilerplate code, prototyping and even debugging is now more streamlined. Finding information across different documents is easier with AI and copilots,” says Bhargava in an email interview. “[Though] AI can speed things up, I now [must] critically assess AI-generated outputs. It has made me more analytical in reviewing the work produced by these systems, ensuring it aligns with my expectations and needs, particularly when handling complex algorithms or compliance-driven work.” 

AI tools are also helping her create rapid prototypes and they’re reducing the cognitive load.  

“I can focus more on strategic thinking, which improves productivity and gives me room to innovate,” says Bhargava. “Sometimes, it’s tempting to lean too heavily on AI for code generation or decision-making. AI-generated solutions aren’t always optimized or tailored for the specific needs of a project, resulting in bugs and issues in prod. [And] sometimes, it takes more time to set it up if the tools are complex to use.” 

Related:Soft Skills, Hard Code: The New Formula for Coding in the AI Era

Hands-Free Coding Still Hasn’t Arrived 

At present, AI struggles with its own set of issues such as misinterpretation, hallucination and incorrect “facts.” Over-reliance on AI-generated code could lead to a lack of deep technical expertise in development teams.  

“With humans less involved in the nitty-gritty of coding, we could see a decline in the essential skills needed to debug, optimize, or creatively problem-solve at a low level. Additionally, ethical and security concerns could arise as AI systems might unknowingly introduce vulnerabilities or generate biased solutions,” says Parakh.  

Tom Taulli, author of AI-Assisted Programming: Better Planning, Coding, Testing, and Deployment has been using AI-assisted programming tools for the past couple years. This technology has had the most transformative impact “by far” on his work in his over 40-year work history. 

“What’s interesting is that I approach a project in terms of natural language prompts, not coding or doing endless searchers on Google and StackOverflow.  In fact, I set up a product requirements document that is a list of prompts. Then, I go through each one for the development of an application,” says Taulli. “These systems are far from perfect. But it only takes a few seconds to generate the code -- and this means I have more time to review it and make iterations.” 

Taulli has been a backend developer primarily, but AI assisted programming has allowed him to do more front-end development.   

“The funny thing is that one of the biggest drawbacks is the pace of innovation with these tools.  It can be tough to keep up with the many developments,” says Taulli. “True, there are other well-known disadvantages, such as with security and intellectual property. Is the code being copied?  Do you really own the code you create?” says Taulli. “However, I think one of the biggest drawbacks is the context window. Basically, the LLMs cannot ‘understand’ the large codebases.  This can make it difficult for sophisticated code refactoring.”.   

Another issue is the cut-off date of the LLMs. They may not have the latest packages and frameworks, but the benefits outweigh the drawbacks, he says. 

Tom Jauncey, head nerd at digital marketing agency Nautilus Marketing, says GenAI tools like GitHub Copilot have accelerated the coding process by letting him think about high-level architecture and design. His advice is to use AI to save time on boilerplate code and documentation. 

“Some of the things that I had to learn were how to prompt AI tools and think critically about their output. It is important to remember that while AI is great at generative code, it doesn't always understand broader context and business requirements,” says Jauncey. “Thus, always cross-check the AI-generated code with official documentation. AI-powered tools ease the effort of exploring a new language or framework without having to go into syntax details.” 

Edward Tian, CEO of GPTZero, believes it’s better to use GenAI to assist coding rather than relying on it entirely. 

“Personalization is such a key aspect of coding, and GenAI sometimes just can’t quite personalize things in the way you want. It can certainly create complicated code, but it just often falls short in terms of uniqueness,” says Tian. 

Bottom Line 

GenAI is accelerating development by generating code quickly but beware of its limitations. While it’s good for writing boilerplate code and documentation, creating quick prototypes and debugging, it’s important to verify the outputs. Prompt engineering skills also help boost productivity.