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AI agents are writing more code. How can developers ensure that code is correct? Microsoft Research’s Proof-Driven Development (PDD) Kit explores a more rigorous approach: it translates a developer’s intent into a formal specification, then uses formal verification to prove that generated code meets it. PDD Kit works with... show more

Microsoft Research

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60,840 просмотров • 1 день назад •via X (Twitter)

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Фото профиля Microsoft Research

Microsoft Research1 день назад

Join @GitHubUniverse Oct 28–29 in SF or online. Explore AI, security, and open source with hands-on sessions, product updates, and devs from around the world.

Фото профиля Harry Nicholls

Harry Nicholls1 день назад

I think this is the future of software development until we solve program synthesis. Love the idea of bringing formal methods in by default, and making proofs legible to those who aren’t experts will be challenging.

Фото профиля kevin joo

kevin joo1 день назад

@VictorTaelin they are starting to catch on!

Фото профиля H.M Gowda

H.M Gowda1 день назад

simply call PRD.

Фото профиля EDDY VU

EDDY VU1 день назад

Bringing formal verification into the agent loop is huge, especially for catching subtle edge cases unit tests consistently miss.

Фото профиля Lachy

Lachy1 день назад

if the ai drafts the formal spec and i approve it, what catches a spec that is valid but quietly weaker than what i meant?

Фото профиля AxleMode

AxleMode1 день назад

The mutation check in the demo is the bit worth copying: show the deliberately broken versions failing beside the proof result.

Фото профиля Nur Hasan

Nur Hasan1 день назад

How is it different from PRD?

Фото профиля 安叫兽|Bird🕊️ 🔶 BNB

安叫兽|Bird🕊️ 🔶 BNB1 день назад

先把意图写清楚这步挺关键，不然验证再严也可能证明错东西。

Фото профиля Humanity Labs

Humanity Labs1 день назад

Formal verification is a strong answer for code. Business work needs its equivalent: explicit standards, logged decisions, exception routes, and a human outcome owner. “The agent completed it” is not the same as “the result is acceptable.”

Фото профиля NextSignal

NextSignal1 день назад

形式化验证过去最大的阻力，是写规范的人力成本比写代码还高。这套工具真正的考题在上一层：证明能保证代码符合规范，但规范本身写错了、或者根本没覆盖真实需求，证明照样成立。所以最难自动化的不是验证，而是把「到底想要什么」说清楚——这一层最后还是得人来守。

Фото профиля Kim Noël ⚡ 📖

Kim Noël ⚡ 📖1 день назад

@grok is this like Lauren tan 's pstack?

Фото профиля Diego Alejandro R

Diego Alejandro R1 день назад

We need more and better tools to review written code by AI

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24,256 просмотров • 3 месяцев назад

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Moonsong Labs 🌙

21,532 просмотров • 1 год назад

“In the last 4–5 months, we stopped typing code.” Cognition CEO Scott Wu says almost all coding at Cognition is really just done through prompts now: “The code that we check into GitHub, how much of it was typed by a human at this point? Almost none.” “Now it looks a lot more like: how do we optimize testing, review, and planning - not how do we make AI good at writing code based on the prompt you give. At this point, it’s frankly already done.”

“In the last 4–5 months, we stopped typing code.” Cognition CEO Scott Wu says almost all coding at Cognition is really just done through prompts now: “The code that we check into GitHub, how much of it was typed by a human at this point? Almost none.” “Now it looks a lot more like: how do we optimize testing, review, and planning - not how do we make AI good at writing code based on the prompt you give. At this point, it’s frankly already done.”

TBPN

30,438 просмотров • 8 месяцев назад

Nobody is writing 90% of their code using AI. Here's the uncomfortable truth: The real productivity gain from using AI to write code is closer to 10%, nowhere near the 90% people claim. Sundar Pichai said in 2024 that 30% of the new code at Google was AI-generated. However, he went on to admit, during Lex Friedman's podcast, that engineering velocity had only increased by about 10%. AI-generated code isn't free code. It still has to be reviewed, tested, and made production-ready. Optimizing a single step (code generation) doesn't boost output if bottlenecks shift elsewhere (code reviews). It doesn't matter how much code you generate if you can't keep up the review process. The solution: Automate as much as you can the review and verification of your code. I'm working with Sonar, who is sponsoring this post, and they will take care of the code quality and security analysis of your code: • They review over 300B lines of code every single day • They cover reliability, security, and maintainability for your code • You can integrate them into your CI/CD pipeline • You can install them in your IDE (I use their VSCode extension) • Support for more than 30 languages Here is a link so you can check them out:

Nobody is writing 90% of their code using AI. Here's the uncomfortable truth: The real productivity gain from using AI to write code is closer to 10%, nowhere near the 90% people claim. Sundar Pichai said in 2024 that 30% of the new code at Google was AI-generated. However, he went on to admit, during Lex Friedman's podcast, that engineering velocity had only increased by about 10%. AI-generated code isn't free code. It still has to be reviewed, tested, and made production-ready. Optimizing a single step (code generation) doesn't boost output if bottlenecks shift elsewhere (code reviews). It doesn't matter how much code you generate if you can't keep up the review process. The solution: Automate as much as you can the review and verification of your code. I'm working with Sonar, who is sponsoring this post, and they will take care of the code quality and security analysis of your code: • They review over 300B lines of code every single day • They cover reliability, security, and maintainability for your code • You can integrate them into your CI/CD pipeline • You can install them in your IDE (I use their VSCode extension) • Support for more than 30 languages Here is a link so you can check them out:

Santiago

296,750 просмотров • 1 год назад