Programmers must be educated about strong coding practices

Forward-looking: Machine learning algorithms are all the rage now, as they are used to generate any kind of “original” content after being trained on enormous pre-existing datasets. Code-generating AIs, however, could pose a real issue for software security in the future.

AI systems like GitHub Copilot promise to make programmers’ lives easier by creating entire chunks of “new” code based on natural-language textual inputs and pre-existing context. But code-generating algorithms can also bring an insecurity factor to the table, as a new study involving several developers has recently found. Read More

Google has a secretive new project that teaches an AI to write code, fix bugs, and make code updates. The project is part of a broader push by Google into generative AI or GANS. The goal is to reduce the need for humans to write and update code, while maintaining code quality. The project started at X, where it was codenamed “Pitchfork,” later moving into Google Labs — a transition that signaled its increased importance to leaders, with Google Labs pursuing long-term bets. Read More

 … I have done a lot of writing recently about these many referents and the Agile 2 Academy, with whom I am currently working.  I’ve stressed the importance of attaining what I have been calling Business Agility and observed the many ways in which Agile and agile as they are currently practiced don’t necessarily contribute to it.

• Big-A Agile (the myriad commercial frameworks, such as Scrum, SAFe, LeSS, XP, Kanban and others) subverts real agility by layering process and ceremonies on development teams, often with little benefit for its costs. 
• Small-a agile is the goal for digital product development, but the OKRs associated with many efforts are not realized.  Why that might be is something worth exploring. 
• Agility is a goal, but it is an intermediate one.   Companies’ goals are to produce products and services that flourish in their markets.  Rapid development and deployment of competitive products and services is the true end goal; agility is a characteristic that successful companies must have to achieve it. 
• Business and Digital Agility are two sides of one coin.  True agility is characterized by rapid recognition of opportunities and threats, formulation of responses and execution at speed.  This cannot happen unless a company’s ability and willingness to transform its Business and Operating Models correlate with the speed at which it can build solutions and products. 

GitHub’s Copilot AI can write up to 40% of the code for programmers and is heading up to 80% within five years, says GitHub CEO Thomas Dohmke.

This rapid AI advance is letting coders get their work done in less than half the time it used to take and has implications across all industries where software development is now critical, Microsoft board member and venture capitalist Reid Hoffman recently told a gathering of tech executives.

Still, Dohmke says as artificial intelligence accelerates and is adopted more broadly across companies, innovation remains a skill only humans can dominate. Read More

Machine Learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed. Machine learning algorithms use historical data as input to predict new output values.

Technical Debt describes what results when development teams take conscious actions to expedite the delivery of a piece of functionality or a project which later needs to be remediated via refactoring. In other words, prioritizing speedy delivery over perfect code is the result.

This article will present a simple yet powerful Model of Technical Debt for Machine Learning Systems. The model is simple to remember, easier to extend, and provides a reliable means for reliable and maintainable Machine Learning Systems. This, in a nutshell, is the value proposition of this post. Read More

Part 2

At its re:Mars conference, Amazon today announced the launch of CodeWhisperer, an AI pair programming tool similar to GitHub’s Copilot that can autocomplete entire functions based on only a comment or a few keystrokes. The company trained the system, which currently supports Java, JavaScript and Python, on billions of lines of publicly available open source code and its own codebase, as well as publicly available documentation and code on public forums.

It’s now available in preview as part of the AWS IDE Toolkit, which means developers can immediately use it right inside their preferred IDEs, including Visual Studio Code, IntelliJ IDEA, PyCharm, WebStorm and Amazon’s own AWS Cloud 9. Support for the AWS Lambda Console is also coming soon. Read More

Last June, Microsoft-owned GitHub and OpenAI launched Copilot, a service that provides suggestions for whole lines of code inside development environments like Microsoft Visual Studio. Available as a downloadable extension, Copilot is powered by an AI model called Codex that’s trained on billions of lines of public code to suggest additional lines of code and functions given the context of existing code. Copilot can also surface an approach or solution in response to a description of what a developer wants to accomplish (e.g., “Say hello world”), drawing on its knowledge base and current context.

Copilot was previously only available in technical preview. But after signaling that the tool would reach generally availability this summer, GitHub today announced that Copilot is now available to all developers. As previously detailed, it’ll be free for students as well as “verified” open source contributors — starting with roughly 60,000 developers selected from the community and students in the GitHub Education program. Read More