The future of work for application development and delivery (AD&D) experts will have to change!
Today, roughly 70% of the work is about developing glue code and wiring things together. From the UI front end to the back end of apps and in the integration layer, there are lots of repetitive tasks, boring design patterns, and custom code written. And what’s worse, many teams develop the same code over and over repetitively.
The creative business logic often represents the smallest effort. This waste increases even more when you try to build new, creative, and differentiating custom software.
But as more AI-driven innovation becomes available, more possibilities emerge that can help developers gain productivity — coming in particular from the enhancements of AI-infused development tools shared.
In addition, considerable progress is being made by traditional tech giants, like IBM with AI for code and Project CodeNet and Microsoft through GitHub Copilot. Both bring augmentation and automation to enterprise application modernization efforts, coding productivity gains, and simplification for developers.
Enterprise TuringBots: A deep dive into the future
This is where “TuringBots” or SW Bots that help build enterprise software come into play. We coined the term TuringBots in Forrester after the British genius Alan Turing. We believe that in the next five to 10 years or sooner, based on the groundbreaking innovation in AI, like AI 2.0, TuringBots will be created by several tech vendors. Enterprises can look forward to leveraging TuringBots for coding applications better, faster, and bug-free. Packaged application business platforms, low code environments, professional development, and testing tools will leverage TuringBots and are starting to do so already.
TuringBots will use AI and machine learning (ML) to build models that “learn” from existing code and identify which code generator can meet the business applications and infrastructure requirements to generate and deliver source and executable code. Reinforcement learning (see figure) seems a likely foundational technology for TuringBots. But various other AI foundational technologies are strong candidates, too: from deep learning models to GPT-3 to neuro-symbolic reasoning (and most likely a mix of all these).
We do know TuringBots will have to work based on the following core operating principles:
TuringBots will change forever the way we build apps for the enterprise
With TuringBots becoming available, roles, tools, and technologies for building enterprise apps will change forever. Here are some of our initial ideas and thoughts on the future software development lifecycle with TuringBots:
This article, by Forrester’s vice presidents and principal analysts Diego Lo Giudice, Mike Gualtieri, Jeffrey Hammond, is here. It part of a three-article series, and the above was adapted from the second one.
The views and opinions expressed in this article are those of the author and do not necessarily reflect those of CDOTrends. Image credit: iStockphoto/monsitj