#github copilot

[saurabhshukladigitalcreator]

A collection of IT resources. #tumblr#google

[kazifatagar]

DataStax Enhances GitHub Copilot Extension to Streamline GenAI App Development

DataStax has expanded its GitHub Copilot extension to integrate with its AI Platform-as-a-Service (AI PaaS) solution, aiming to streamline the development of generative AI applications for developers. The enhanced Astra DB extension allows developers to manage databases (vector and serverless) and create Langflow AI flows directly from GitHub Copilot in VS Code using natural language commands.…

[laurentgiret]

GitHub Copilot will soon let developers leverage Anthropic’s Claude 3.5 Sonnet, Google’s Gemini 1.5 Pro, and OpenAI’s o1-preview.

[prcg]

Copilot de GitHub llega a Xcode de Apple

En su conferencia Universe, GitHub anunció hoy una serie de nuevos productos importantes, incluido el proyecto Spark para escribir aplicaciones completamente con IA, así como soporte multimodelo para su servicio Copilot. Pero el propio Copilot también está recibiendo bastantes actualizaciones. Con este lanzamiento, GitHub, propiedad de Microsoft, lleva Copilot al entorno Xcode de Apple por…

[knowledgegraphs]

[stxalq]

if ai is here to refactor regex expressions, i'm all for it

[fullyautomated]

AI Code Assistants, Developer Happiness at all costs?

I took a glance at a white paper and associated research that my boss pointed me to regarding AI assistants for code generation. It was not only an eye-opening moment for me (as I had not thought about the implications of coding assistants - most likely because I've been focused on the good ways AI can help test and quality engineers), but it was also very scary.

The white paper and accompanying research were commentary about a blog post from June 2023 posted to the GitHub blog written by Thomas Dohmke. The full article can be found here: https://bit.ly/3u5yMA6 with all the gory details including a link to download the PDF of the full research conducted. There are three main findings that are being reported as a result of this research. I will cover 2 of those here along with my opinion on them.

Finding #1

Less than a year after its general availability, GitHub Copilot is turbocharging developers writing software.

This finding is based on an analysis of a sample of current GitHub Copilot users of around 934,533. The claim is that, on average, the users of Copilot accept nearly 30% of code suggestions thereby reporting an increase in productivity. When comparing senior to junior developers, the latter has greater benefits.

And therein lies the problem I perceive. Junior developers will undoubtedly accept recommendations presented by an Intelisense-type prompt potentially without thinking about the ramifications downstream of their actions. Another problem is that we seem to focus on "speed" instead of "quality". Speed is but one aspect of the Iron Triangle (faster, better, cheaper). With the advent of AI assistance for actually producing the code, we are taking care of "faster" and "cheaper" (two birds, one stone), but let's not neglect better! Invest the time and money we are saving by paying close attention to the quality of the code being generated as well as ensuring that it is in line with our organization's code quality guidelines (DRY principles, for example).

Finding #2

We estimate these generative AI developer productivity benefits could boost global GDP by over $1.5 trillion USD by 2030 by helping to meet growing demand for software.

The main reason being touted for this $1.5 trillion boost in GDP by 2030 is again linked to "productivity gains" based on the estimation that approximately 30% of code being developed is being contributed by Copilot. Further, based on their data, productivity increases will be greater based on developers getting over the learning curve along with projected Copilot improvements.

The Copilot improvements are the key here, as well as the part that is not being covered by the data or the research. The analysis only covered productivity over an initial period of time and seems to ignore what happens after the code is developed and checked in. Based on the GitClear analysis of over 153 million lines of code aimed at answering the questions:

a. Are there measurable side effects to committing AI-generated code?

b. What are the implications of the widespread adoption of AI programming assistants?

They concluded that based on the 6 metrics they tracked and analyzed, "the output quality of AI-generated code resembles that of a developer unfamiliar with the projects they are altering. Just like a developer assigned to a brand new repository, code generation tools are prone to corrupting the DRY-ness of the project". I'm not surprised by this at all, and it points to some potentially serious risks in the realm of Quality.

The rationale behind this is that the suggestions from Copilot seemed to be biased toward adding code (like a junior developer might do) as opposed to activities related to refactoring (as a senior developer might do), like moving, updating, or deleting existing code.

From my perspective, adding more code faster without thinking about future maintenance can create more tech debt as time goes on. I reckon that using Copilot for MVPs, POCs, and other green field experiments where normally net new code is always added is a good use case for it. It would be interesting to see how Copilot behaves when dealing with existing and legacy code bases.

Conclusion/Suggestions

It remains to be seen if the power of Copilot can be harnessed via the deployment of organizational policies and guidelines that drive the incorporation of Copilot into the development landscape. Some examples of this:

Junior developers can use AI assistants for their own learning and upskilling but should pair with more seasoned developers when dealing with production code.

Senior developers are exposed to the guidelines and encouraged to provide code faster and maintain the engineering organization's coding standards.

Limit the use of Copilot and other assistants for greenfield projects like POCs, MVPs, and limited-scope low-risk projects aimed at getting products into customers' hands faster to facilitate rapid feedback.

Encourage the initial use of AI assistants to create lower-risk code that can be utilized for in-sprint test automation.

Encourage the use of AI assistants for infrastructure as code where the scope is more limited and can be used as a learning platform for all software developers as they go through "the learning curve" to get to know the tool.

Create a Community of Practice within the organization where users of GitHub Copilot and other AI assistants can share lessons learned and good practices.

The above community can also be harnessed to collect feedback that can be aggregated and categorized to provide feedback to GitHub Copilot for product improvements and feature suggestions.

In general, I feel optimistic about GitHub Copilot, not because it generates code faster, but because (just to name a few):

It is a new tool that has harnessed broad support from the development community.

It has the capability, via this captive audience that has embraced it, to include all of the Code Quality features that are currently lacking and rely on experience.

It can help the fellows in the testing community or other adjacent communities to learn proper coding as well as participate in the numerous automation initiatives throughout an organization. Pipelines, test automation, etc.

What other suggestions do you have? Feel free to leave them in the comments or ping me directly if you'd like to chat about it.

[it-system-engineer]

Copilot Nedir? Nasıl Kullanılır?

Merhaba, bu yazımda size Copilot nedir? Nasıl kullanılır? konusundan kısaca bahsedeceğim. GitHub Copilot, yapay zeka destekli bir programlama asistanıdır. Bu araç, yazılım geliştiricilere kod yazarken yardımcı olmak için tasarlanmıştır. Özellikle, Visual Studio gibi geliştirme ortamlarında entegre şekilde çalışır. Copilot’un arkasındaki yapay zeka, gigabaytlarca kaynak kod verisi üzerinde…

[willcodehtmlforfood]

Microsoft reportedly is losing lots of money per user on GitHub Copilot - Neowin

[yuzu-balignon]

Perks of working for a big company :)

[spindlecrank]

It's a dying art when it comes to communicating with anyone or anything

In the world of generative AI, the answer is only as good as the question. It all comes down to the art of asking the right, well-thought-out questions or queries to obtain useful and relevant results. Communicating effectively with AI requires focus and attention to detail. Here are some valuable tips on how to optimize your interactions with AI and make the most of this powerful technology.1.…

[davidcreator]

GitHub Copilot: Criação de Código Eficiente

Você está em: Início > Artigos > Inteligência Artificial > GitHub Copilot: Criação de Código Eficiente Olá! Caro leitor, este artigo é para quem esta procurando tecnologias de inteligência artificial para ajudar nos trabalhos do dia a dia Introdução A codificação é a espinha dorsal da revolução tecnológica, mas frequentemente pode ser um processo desafiador e complexo. Felizmente, a…

[laurentgiret]

GitHub Copilot, the AI-based tool designed to help developers write code is adding a new code referencing tool in private beta.

[biglisbonnews]

The future of business is here: How industries are unlocking AI innovation and greater value with the Microsoft Cloud Over the past six months, I have witnessed the staggering speed and scale of generative AI technology adoption, and how it has opened doors for organizations to imagine new ways to solve business, societal, and sustainability challenges. For many with modernized data estates fortified with the Microsoft Cloud, advanced AI technology is already unlocking innovation... The post The future of business is here: How industries are unlocking AI innovation and greater value with the Microsoft Cloud appeared first on The Official Microsoft Blog. https://blogs.microsoft.com/blog/2023/07/24/the-future-of-business-is-here-how-industries-are-unlocking-ai-innovation-and-greater-value-with-the-microsoft-cloud/

[knowledgegraphs]

[eggman-is-fat-mkay]

copilot (n.)

a method of rapid editing for programmers who flatly refuse to learn any method of rapid editing (vim keybindings, snippet engines, Emmet for HTML...) besides tab completion