How will AI and MachineLearning change SoftwareEngineering? In a new article for TechRepublic, Gartner analyst Van Baker shares how ML models that generate code could enhance developer productivity. Read more here. GartnerAPPS
The impact of AI and ML on software engineering is not limited to embedding models in applications; it extends to the tools that designers are using to create compelling user experiences for their digital products. The workflow of transferring design assets and specifications from UX designers to software engineers is shown to be increasingly automated. The increasing adoption of design systems has helped to facilitate this transfer.
Historically, the different perspectives of designers and developers have caused problems in creating applications with a compelling UX. Looking to the future of digital product design in the enterprise, digital product team leaders will have both design and development skills. A “design strategist” role will emerge to lead converged teams of designers and developers to deliver better digital products quicker, while improving the quality of the applications.
Several new vendors have entered the AI-augmented software-testing market, and vendor acquisitions were prevalent in the last year. Gartner predicts that by 2027, 80% of enterprises will have integrated AI-augmented testing tools into their software engineering toolchain, a significant increase from 10% in 2022. As applications become increasingly complex, AI-augmented testing will play a critical role in helping teams to deliver high-quality applications rapidly.
The impact of AI and ML on software engineering is significant, and the positive impact of the combined effort between data science and software engineering should not be underestimated. The wealth of data that the enterprise possesses can add significant value to business applications through models that generate forecasts, scoring models, next-best-action recommendations and other valuable business-enhancing tools.
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