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How G-Research Used TuringBots To Transform Software Development

Forrester IT

As we outlined in previous research, Generative AI assistants known as TuringBots can serve as powerful tools to address some of the broader software development challenges. Specifically they help to automate a wide range of tasks throughout the software development life cycle (SDLC).

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Predictions 2024: Software Development Adapts To TuringBots, Ajar Source, And Backstage

Forrester IT

In 2024, software developers will have to adapt to the rise of TuringBots, ajar source, and Backstage in the development cycle. Learn more in our 2024 predictions for software development.

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Top Software Development Challenges in 2023

IT Toolbox

Solutions to address the top challenges in software development. The post Top Software Development Challenges in 2023 appeared first on Spiceworks.

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The State Of AI And Generative AI In The Software Development Market

Forrester IT

TuringBots Forrester has branded AI and generative AI for software development as TuringBots since its inception in 2020. The good news is that generative AI (genAI) has advanced the maturity of TuringBots by at least five to 10 years. So how has genAI changed the state of TuringBots today in the market?

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Building Like Amazon

Speaker: Leo Zhadanovsky, Principal Solutions Architect, Amazon Web Services

Amazon's journey to its current modern architecture and processes provides insights for all software development leaders. To get there, Amazon focused on decomposing for agility, making critical cultural and operational changes, and creating tools for software delivery.

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How is Generative AI Forcing Software Development to Evolve?

IT Toolbox

The post How is Generative AI Forcing Software Development to Evolve? AI’s revolution is upon us. Here’s what it means for IT leaders. appeared first on Spiceworks.

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Predictions 2021: Software Developers Face Mounting Pressure

Forrester IT

As executive teams put more pressure on internal teams to speed digital transformation in 2021, how will software developers respond? Find out in our 2021 predictions for software development.

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How AI and ML Can Accelerate and Optimize Software Development and Testing

Speaker: Eran Kinsbruner, Best-Selling Author, TechBeacon Top 30 Test Automation Leader & the Chief Evangelist and Senior Director at Perforce Software

It's no secret that CTOs need to have a full understanding if they want to be successful, but does that make them responsible for developer productivity? While advancements in software development and testing have come a long way, there is still room for improvement.

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The Forrester Wave™: AI/ML Platforms: Vendor Strategy, Market Presence, and Capabilities Overview

As enterprises evolve their AI from pilot programs to an integral part of their tech strategy, the scope of AI expands from core data science teams to business, software development, enterprise architecture, and IT ops teams.

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Building Evolvable Architectures

Speaker: Dr. Rebecca Parsons, CTO of ThoughtWorks

The software development ecosystem exists in a state of dynamic equilibrium, where any new tool, framework, or technique leads to disruption and the establishment of a new equilibrium. Predictability is impossible when the foundation architects plan against is constantly changing in unexpected ways.

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Data-Driven Performance Feedback Helps Teams Improve Customer Outcomes

Speaker: Mickey Mantle, Founder and CEO at Wanderful Interactive Storybooks | Ron Lichty, Consultant: Interim VP Engineering, Author, Ron Lichty Consulting, Inc.

In order to be successful at delivering software, organizations need to become data-driven. And in the era of agile, which recognizes that software development is a team sport, performance reviews need to use data for programmer growth and to give focus to business results.

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An Adult Conversation About Estimates

Speaker: Beekey Cheung, Software Consultant, Professor Beekums, LLC

Estimates are a contentious topic in software development. Most of our development teams hate providing estimates and many managers are starting to view them as unnecessary. As senior software leaders, how can we determine whether estimates are helpful or harmful to our teams?