AI is growing into its role as a development and testing assistant
Will "TuringBots" -- or AI-powered development and testing assistants -- make programming more pleasurable for professional and citizen developers alike? These generative AI bots are already recasting and injecting more productivity into development processes, industry observers agree. At the same time, developers can't rely 100% on AI -- there needs to be human skills in the process.
Examples of such AI dev/test assistants include GitHub Copilot for coding and Test Rigor for intelligent automated testing. These assistants, based on generative AI and large language models, "have made natural language a key authoring mechanism for tools across the entire software development lifecycle," state Forrester analysts John Bratincevic and Diego Lo Giudice in a recent post.
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The use of these dev/test assistants "will dramatically increase low-code adoption," they predict. "This is especially true for citizen development," they add. These assistants "will make onboarding nontechnical workers as citizen developers better, faster, and easier."
With this ease of writing code comes incredible speed in writing code. "One of our platform engineers who had no experience writing front-end web apps was able to feed a spreadsheet with data and create a simple-to-use internal web app in a matter of minutes by leveraging generative AI," recounts Mike Lempner, head of engineering and technology at Mission Lane, a fintech company. "Even the most experienced front-end engineer would have taken several hours to be able to write the code, test, and deploy something of this nature."
As an added bonus, "automating the writing of code can free up engineers' ability to focus more time on design and architecture," Lempner says. "Good design and architecture will still be needed to enable generative AI to build the right solutions for your environment."
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Generative AI represents a massive step forward in this journey "because almost anyone can ask an AI to produce a functioning program," says Patrick Stokes, executive VP of product and industries marketing with Salesforce. "The result is orders of magnitude faster than if they tried to write the code themselves. Instead of spending hours writing that code, they can spend that time testing it, securing it, and tweaking its interfaces to satisfy its users best. The outcome is higher quality apps in much less time produced by people who will inevitably be even closer to the end-user experience."
Generative AI-based development reverses the dynamic of human-machine interfaces, Stokes adds. Rather than "requiring humans to think like a computer," it enables "humans to write code like a human, empowering more people to build things more quickly."
We're only beginning "to realize how AI can improve the developer experience and software as a whole," agrees Dana Lawson, senior VP of engineering at Netlify. "AI can automate the tedious but necessary tasks of software development so the actual human developers can have more time to focus on impactful, creative work."
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Developers "are already experimenting with adding AI to their workflows to do things like review pull requests, clean up documents, and create project outlines," Lawson adds. "AI is fun to experiment with, and when applied in the right way, offers tangible benefits to the developer experience."
Natural language processing is evolving into a key enabler of low-code capabilities, starting with an initial prompt and seeing the result, Bratincevic and Lo Giudice observe. Low-code vendors are building natural language prompts into their offerings, they add. "Natural language prompts will become a normal, complementary method to interact with the required visual tools."
Generative AI-based coding also helps reduce redundancy. "It can be an assistant to a developer, and it can extend their own human abilities," says Leon Kallikkadan, vice president of technology at Atrium. "For instance, if a developer doesn't want to actually write the code themselves, they can, in a straightforward, natural human language, tell AI to write the code, state what its function is, and what it needs to do. AI can go line by line and create that. You can use AI to write the code, run it, find errors, fix the code, do more fixes, and develop acceptable code."
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As an assistant, generative AI "can suggest alternative ways, alternative codes to use," Kallikkadan continues. "One of the major benefits from a business standpoint is that unified, best practices for coding might be developed as a result of AI. Depending on the developer or development shop you use, they might produce different coding principles. With AI, you may now be able to get a standardized AI-generated code if these best practices are foundational to the way code is written."