Now Available: How Generative AI Transforms Software Development
As generative AI becomes more sophisticated and offers developers new solutions that write code, tests and documentation, it also introduces new challenges: bias and threats to privacy.
However, as demands grow on developers to build and deliver software faster amid rapidly changing business requirements, the need for increased engineering efficiency is critical. With generative AI now poised to serve as an extension of development teams, using the technology to debug code or automate routine tasks will become crucial for businesses to compete.
In The New Stack’s new ebook, Janakiram MSV, a longtime contributor to TNS and principal analyst at Janakiram and Associates, peels back the curtain to uncover how the latest GenAI technology is different from previous iterations and touches on how engineering teams are implementing the latest tools.
GenAI can help fill gaps on teams that are hard to staff with human developers.
The International Data Corporation, a market intelligence firm, forecasts a global shortfall of 4 million developers by 2025; this talent shortage could result in approximately $8.5 trillion in unrealized annual revenues by 2030.
The concern that companies will lack the critical software engineers they need to deliver on time is driving the need for increased developer productivity. From reskilling to new workflows and tool adoptions in the development streams, the conventional way of doing things is being rewritten.
Building Apps ‘Better, Faster, Stronger’
In the new TNS ebook, “Better, Faster, Stronger: How Generative AI Transforms Software Development” the author explores how GenAI can improve developer productivity and supercharge processes across the entire development life cycle.
The ebook provides valuable insight into:
- What generative AI is and how it differs from previous versions of AI.
- What it has to do with developers (and how it can improve developer productivity).
- How generative AI can enhance all phases of the software development life cycle.
- Where the security risks are and how to mitigate them.
- What to consider when evaluating AI coding assistants, large language models and more.
Thank you to our sponsor, Tabnine for making this ebook possible.
