Think your coding job is safe from AI? Think again. Advances in artificial intelligence and machine learning are poised to radically reshape software development in 2024. From writing code, to testing, to deployment, AI-driven tools will automate, augment, and potentially replace many human tasks. Veteran developers may scoff, but AI is no fad – it’s the unstoppable force that will fundamentally change programming.
Don’t take my word for it. Here are 5 jaw-dropping ways experts predict AI will disrupt software development next year:
AI Will Write Basic Code
AI code generation tools like GitHub Copilot and TabNine leverage vast training datasets to turn simple prompts into working code. Developers can describe the logic in plain English, and boom – valid code appears instantly.
“90% of programmer work can be automated,” claims AI pioneer Andrew Ng. While AI-generated code still requires human review, these tools will allow developers to produce rough code for crud functions in seconds instead of minutes. What coder wouldn’t want a sidekick that writes boilerplate code for them? Hello speed and productivity!
Of course, some argue AI threatens programming jobs. But SAP’s Juergen Mueller believes “AI will automate the mundane, not eliminate the human.” The more roles evolve from tedious coding to higher-value strategic work, the better for innovation.
Tools like GitHub Copilot and TabNine use a training method called Codex, where deep learning algorithms ingest millions of lines of public code to learn programming techniques. They can generate code across over a dozen languages including Python, Java, JavaScript, TypeScript, Ruby, C++, and more.
In demos, GitHub Copilot has produced functional React and JavaScript code from simple English prompts. However, its code does contain bugs and requires human oversight. As AI expert Melanie Mitchell explains, “AI today can write syntactically correct code, but it doesn’t build mental models of what the code needs to accomplish.” The human programmer provides the strategic thinking.
Developers at companies like NVIDIA and Upwork have reported Copilot doubling or tripling their productivity on repetitive coding tasks. But some raise ethical concerns about Copilot’s code originating from open source projects without attribution. GitHub maintains they have the right to use public code, but will throttle output if it verbatim copies code snippets.
AI Will Dramatically Expand Coding Capabilities
Even expert coders have limits on what they can build alone. But AI promises to smash through those barriers by exponentially increasing what an individual developer can achieve. How? By allowing lone coders to tap into the collective skills of the entire software community.
Tools like GitHub Copilot analyze millions of open source projects to “learn” how to code in every language and paradigm. This means a developer can get AI assistance coding in React, Python, Go, Haskell – you name it. Suddenly that iOS dev can build a scalable cloud back-end. That web dev can ship a real-time neural network. The possibilities are limitless.
“AI will democratize access to software skills,” explains Anthropic researcher Zachary Lipton. Every coder will code like an expert. If that doesn’t blow your mind, I don’t know what will!
Imagine a mobile developer who’s never worked in cloud engineering now able to utilize Copilot to build a robust backend architecture on AWS or Google Cloud complete with load balancing, scaling, security, and database optimization. They describe the functional goals in plain English and Copilot handles translating it into real working code.
Similarly, a traditionally front-end web developer could leverage AI assistance to incorporate machine learning capabilities like optical character recognition, predictive user input, or localization into their apps and sites. The AI has knowledge they lack on implementing complex algorithms and neural networks.
Of course, completely novice coders won’t become experts overnight with these tools. But AI can significantly broaden the scope of what programmers can achieve. As AI researcher Jeremy Howard puts it, “Programming is going to become more about problem solving and less about knowing a language’s syntax.”
AI Will Eliminate Buggy Code
Goodbye QA team, hello AI!rather than manually hunting bugs, Anthropic’s Clara AI reviews code contextually to detect errors and security flaws. This allows developers to catch issues proactively instead of in production.
Experts estimate AI will eliminate up to 45% of bugs pre-deployment. “We will look back and be stunned we ever built software without the help of AI,” says Andreas Schroeter, CEO of machine learning company Functionize.
AI will also revolutionize incident response by pinpointing root causes of complex bugs in minutes rather than hours or days. Talk about a game changer!
Anthropic’s Clara uses a technique called self-supervised learning to review code and detect potential bugs. The AI scans source code to learn the expected sequence of functions, analyzes execution paths, and compares to deviations that typically indicate bugs. During training, Clara achieved a 96% accuracy rate in identifying bugs implanted in open source projects.
Some experts believe AI code reviewers may eventually even suggest context-based fixes. Machine learning company Cohere explains “The ideal scenario is a human programmer and an AI assistant in a tight collaboration loop – the human provides intent and high-level logic, the AI surfaces bugs and recommends repairs.”
AI Will Automate Testing
Previously, developers had to manually write test cases to validate code pre-release. But AI algorithms can now generate robust test scenarios exponentially faster.
For example, Functionize’s Teddy Liaw says their AI platform “allows a single coder to properly test what would normally require an entire quality team.” By automatically exploring different real-world user paths, data values, device settings, and edge cases, AI overcomes human blindspots.
AI also enables ongoing testing after deployment to catch any emerging issues. According to Samsung’s Onshape, AI may eventually “downsize testing teams by 90% or more.” I don’t envy all those suddenly unemployed QA analysts!
AI testing platforms like Applitools use computer vision and optical character recognition to validate that UIs look and function correctly across different devices, OS versions, and screen sizes. This frees engineers from time-consuming cross-browser testing.
Startups like Unsupervised provide AI systems to continually test and monitor production apps without any test cases needed. Instead of rules-based checks, Unsupervised uses deep learning trained on normal app behavior to detect anomalies indicative of bugs or outages.
AI Will Deploy And Operate Software
Finally, AI will inherit developers’ least favorite task – the care and feeding of applications post-deployment. Tools like Microsoft’s MLOps automate monitoring, updates, scaling, security patching, and more.
With AI DevOps, engineers are freed from being “on call” at all hours to handle production incidents. “Serverless, self-healing software removes so much stress,” remarks Amanda Silver of Microsoft. “And anything that decreases developer burnout is a win in my book!”
Tools like Harness and Iterative.ai leverage machine learning to diagnose root causes of production issues from application logs up to 30x faster than humans. This enables rapid remediation before problems severely impact customers.
IBM’s AIOps utilizes AI to instantly roll back failing updates and auto-scale cloud resources to prevent site crashes due to traffic spikes. Such automation will eventually enable a single developer to manage complex applications without needing dedicated DevOps engineers.
Bonus: AI Can Design UX
Think AI will transform just the coding? Think bigger! Sophisticated algorithms can already generate endless UI and UX design options more efficiently than any human designer.
Just input brand elements and user needs, and AI can spit out everything from color schemes, to wireframes, to interactive prototypes. While a bit of art direction helps, AI design tools from Autodesk, Adobe, and others enable stunning results in a fraction of the time.
Adobe recently revealed an AI concept called Creative Curse Generator that allows designers to describe a desired user interface in natural language. The AI then generates functional web page templates complete with layouts, fonts, colors, images, and even text blurbs.
While AI web design tools are still emerging, some believe they could be a game changer for rapid prototyping. “With AI I can brainstorm far more design options than I ever could manually,” says UX designer Lisa Welch. “It’s like having a whole product team inside a single tool.”
The Verdict
The bottom line is nearly every part of software development stands to be revolutionized by AI in 2024 – for better and worse. Those who embrace AI will accomplish what was previously impossible, while refuseniks who shun change will swiftly become obsolete.
“With great power comes great responsibility,” warns AI ethics pioneer Timnit Gebru. The onus is on developers to direct these technologies toward creative augmentation over human replacement. The potential for good is immense.
But amidst all the disruption, take comfort in knowing that code may come and go, but the exhilaration of seeing software you created spring to life cannot be replicated by any AI. The magic of software development remains beautifully and forever human.
