Mentioned in 16 AI use cases across 2 industries
This is like giving your development team a super-smart intern that reads your code and automatically writes lots of unit tests for it, including for weird edge cases that humans often forget. Then it checks how much of your code those tests actually exercise (code coverage) and how well they cover unusual behaviors.
This is like giving your existing code to a very smart assistant and asking it to write the unit tests for you. The large language model reads the code, guesses what it should do, and then writes test cases to check that behavior automatically.
Think of AI code assistants as a smart co‑pilot sitting next to every developer: they read what you’re typing, suggest the next few lines or whole functions, explain confusing code, and help spot bugs — much like autocomplete on steroids for programming.
This is like having a very smart senior engineer automatically review every code change for your team — inside your IDE, GitHub, GitLab, or the command line — and point out bugs, security issues, and style problems before they hit production.
This is like giving your developers a smart co-pilot inside JetBrains IDEs that can read and write code, explain it, and help with everyday tasks without leaving their usual tools.
Think of AI code assistants as smart copilots for programmers. As you type, they guess what you’re trying to build and suggest code, explain errors, write tests, and help you understand unfamiliar code — like an always‑available senior engineer sitting next to every developer.
This is like giving your software developers a smart robot pair‑programmer that lives inside VS Code. You tell it what you want built or changed, and it can read your code, plan the work, and automatically edit files, run commands, and iterate with you inside the IDE.
Think of this as a smart co-pilot for programmers: it reads what you’re writing and the surrounding code, then suggests code, tests, and fixes—similar to autocorrect and autocomplete, but for entire software features.
This is like giving every software developer a smart co-pilot that suggests code as they type, understands your codebase, and can help write, refactor, or explain code—while staying under your company’s control instead of sending everything to a public cloud AI.
This is about putting guardrails around code written by AI assistants (like GitHub Copilot or ChatGPT) so that insecure code doesn’t sneak into your products. Think of it as a security scanner and policy engine that constantly checks and enforces rules on everything AI is allowed to contribute to your software.
This is like giving every software developer a smart pair-programmer that lives inside VS Code: it reads the code you’re writing, suggests the next lines, helps refactor, and explains unfamiliar code or errors in plain language.
Think of this as building ‘co-pilot’ assistants for programmers that can read and write code, help with designs, find bugs, and keep big software projects on track—like giving every developer a smart, tireless junior engineer who has read all your code and documentation.
This is a guide showing how to plug ‘AI helpers’ into every step of your software development process so your developers have smart assistants that can plan, write, review, and maintain code alongside them.
This is a playbook for getting your software teams ready to use AI as a smart co‑pilot—helping them write, review, and test code faster—rather than replacing them.
This is like giving every software engineer a smart co-pilot that reads their whole codebase, remembers how things work, and helps write, review, and understand code directly in their workflow.
This is like having Google’s Gemini AI sitting inside your code editor, suggesting code, explaining errors, and helping you write and fix software faster as you type.