The impact of generative AI on software team productivity is…complex

Will jobs for technology professionals really grow, despite the potential for huge productivity gains from generative AI tools like ChatGPT or GitHub Copilot More complicated?

People can now pump code on demand in a multitude of languages, from Java to Python. with useful tips. already, recently 95% of developers survey Sourcegraph reports they use Copilot, ChatGPT, and other general AI tools like this one.

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But auto-generating new code is only part of the problem in enterprises that already maintain cumbersome codebases, and require a high degree of cohesion, accountability, and security.

For starters, the security and quality assurance tasks associated with software jobs are not going away anytime soon. “For programmers and software engineers, ChatGPT and other large language models help create code in almost any language,” says Andy ThuraiConstellation Research analyst before talking about security concerns.

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“However, most of the code generated is security-vulnerable and cannot pass enterprise-grade code. Therefore, AI can help accelerate coding, analyze the code, find vulnerabilities, and fix it.” Care must be taken to offset some of the productivity gains that AI vendors claim.”

Then there’s code sprawl. An analogy for the rollout of generated AI in coding is the introduction of cloud computing, which made application acquisition easier when it was first rolled out, and now means a profusion of services to be managed.

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The relative ease of generating code via AI will contribute to an ever-expanding codebase – what the Sourcegraph survey authors refer to as “Big Code”. Most of the 500 developers surveyed are concerned about managing this new code, as well as its contribution to code sprawl and technical debt. Even before generative AI, close to eight in 10 say their codebase has grown fivefold in the past three years, and a similar number struggle to understand existing code generated by others.

So, the productivity possibilities for generative AI in programming are a mixed bag. Thurai points out that for IT teams on the maintenance and support end of software, AI likely helps more than complicates. “AI can also impact incident responders, site reliability engineers and support personnel,” he says.

“In their case, they can use AI to figure out any precedence, how it was decided, whether it can be automated so it doesn’t happen again, and how to provide constant warnings and wasted hours.” Help automate some mundane fixes to avoid having to do the primary things right. For customer service people, it’s tailored to individuals based on their needs, the problems they faced, and the impact that was created. Can help personalize service.

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Faster delivery of code also brings higher expectations from businesses for applications that adapt more readily to changing requirements. “We’re evolving toward a modeling-based approach and away from coding based on if-then-else rules,” he says. Preeti LoboPractice director of business integration and automation at App Associates.

“Today, apps need to be more intuitive and with the individual user in mind to create a common experience for everyone. Generative AI is already enabling this level of personalization, and much of the future coding will be developed by AI. Will go.”

Still, humans are needed at key points in the loop to ensure quality and business alignment. “Traditional developers will be relied upon to curate the training data that AI models use, and to investigate any inconsistencies or anomalies,” says Lobo.

The rise of AI-assisted code development has also required that technology managers and professionals take on more expansive roles within the business side. “IT professionals can be expected to wear many new hats,” says Lobo.

This increase in responsibility includes roles such as “ethical AI trainer, machine language engineer, data scientist, AI strategist and advisor, and quality assurance”. In addition, technology professionals will need to engage in “creating an AI strategic roadmap as well as identifying anomalies in data structures and outcomes”.

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Lobo says that generative AI puts natural language processing (NLP) front and center. “Professionals should aim to master programming languages ​​such as Python, Java, and C++, and learn more about libraries and frameworks such as NumPy, Keras, TensorFlow, Matplotlib, and Seaborn,” she says.

“But they should also be looking to have strong analytical, problem solving, and critical thinking skills, as well as linguistics. Such skills can help you quickly move into the world of NLP, which is a fundamental factor when working with AI. ”

Another additional role technology professionals are assuming is coaching and supporting more people in developing and deploying their own apps. “In the past, the art of the possible was limited because of technical limitations or limitations of IT departments,” Thurai says.

“Now, the sky’s the limit. Anyone can find a way to improve the top or bottom line of any business that can be implemented using AI to improve the business, just as they envisioned.” faster than had been imagined.”