An anonymous reader quotes a report from Ars Technica: Debugging a faulty program can be frustrating, so why not let AI do it for you? That's what a developer that goes by "BioBootloader" did by creating Wolverine, a program that can give Python programs "regenerative healing abilities," reports Hackaday. (Yep, just like the Marvel superhero.) "Run your scripts with it and when they crash, GPT-4 edits them and explains what went wrong," wrote BioBootloader in a tweet that accompanied a demonstration video. "Even if you have many bugs it'll repeatedly rerun until everything is fixed."

In the demo video for Wolverine, BioBootloader shows a side-by-side window display, with Python code on the left and Wolverine results on the right in a terminal. He loads a custom calculator script in which he adds a few bugs on purpose, then executes it. "It runs it, it sees the crash, but then it goes and talks to GPT-4 to try to figure out how to fix it," he says. GPT-4 returns an explanation for the program's errors, shows the changes that it tries to make, then re-runs the program. Upon seeing new errors, GPT-4 fixes the code again, and then it runs correctly. In the end, the original Python file contains the changes added by GPT-4.

An anonymous reader shares this report from Tom's Hardware: When we think about Raspberry Pi, we normally picture single-board computers, but the Raspberry Pi Foundation was started to help kids learn about computers and it wants to help whether or not you own its hardware. The non-profit arm of Raspberry Pi this week released its new, browser-based code editor that's designed for young people (or any people) who are learning.

The Raspberry Pi Code Editor, which is considered to be in beta, is available to everyone for free right now at editor.raspberrypi.org. The editor is currently designed to work with Python only, but the organization says that support for other languages such as HTML, JavaScript and CSS is coming....

The Raspberry Pi Foundation already had a nice set of Python tutorials on its site, but it has adapted some of them to open sample code directly in the online editor....The Pi Foundation says that it plans to add a number of features to the Code Editor, including sharing and collaboration. The organization also plans to release the editor as an open-source project so anyone can modify it.
There's a pane showing your code's output when you click the "Run" button (plus a smaller pane for adding additional files to a project).

Tom's Hardware notes that "Since the entire programming experience takes place online, there's no way (at least right now) to use Python to control local hardware on your PC or your Raspberry Pi." But on the plus side, "If you create a free account on raspberrypi.org, which I did, the system will save all of your projects in the cloud and you can reload them any time you want. You can also download all the files in a project as a .zip file."

InfoWorld reports: Zig, a general purpose programming language that interacts with C/C++ programs and promises to be a modern alternative to C, has made an appearance in the Tiobe index of programming language popularity. Zig entered the top 50 in the April edition of the Tiobe Programming Community Index, ranking 46th, albeit with a rating of just 0.19%. By contrast, the Google-promoted Carbon language, positioned as an experimental successor to C++, ranked just 168th.
Tiobe CEO Paul Jansen argues that high-performance languages "are booming due to the vast amounts of data that needs to be processed nowadays. As a result, C and C++ are doing well in the top 10 and Rust seems to be a keeper in the top 20." Zig has all the nice features of C and C++ (such as explicit memory management enhanced with option types) and has abandoned the not-so-nice features (such as the dreadful preprocessing). Entering the top 50 is no guarantee to become a success, but it is at least a first noteworthy step. Good luck Zig!
Tiobe bases its monthly ranking of programming language popularity on search engine results for courses, third party vendors, and engineers. Here's what they's calculated for the most popular programming languages in April of 2023:

• Python
• C
• Java
• C++
• C#
• Visual Basic
• JavaScript
• SQL
• PHP
• Go

April's top 10 was nearly identical to the rankings a year ago, but assembly language fell from 2022's #8 position to #12 in 2023. SQL and PHP rose one rank (into 2023's #8 and #9 positions) — and as in March, the rankings now shows Go as the 10th most popular programming language.

theodp writes: Are you ready to rock it with #datascience?" asks a tweet from Club for the Future, the tax-exempt foundation founded and funded by Jeff Bezos's Blue Origin, which is partnering with Microsoft's Hacking STEM to show how data science is used to determine a Go/No-Go launch of a Blue Origin New Shepard rocket. Interestingly, while Amazon founder Bezos and Microsoft CEO Satya Nadella are big backers of nonprofit Code.org and joined other tech CEOs for CS last fall to get the nation's Governors to "update the K-12 curriculum, for every student in every school to have the opportunity to learn computer science," Microsoft and Blue Origin have opted to teach kids aged 11-15 good old-fashioned Excel skills in their Introduction to the Data Science Process mini-course, not Python or R.

"Excel is a tool used around the world to work with data," Microsoft explains to teachers who have been living under a rock since 1985. "In these activities, students learn how to use Excel and complete all steps of a mission by engaging in the data science process. In this mission, students analyze key weather data in determining flight safety parameters for a New Shepard rocket and ultimately make a Go/No-Go decision for launch. Students learn how to use Excel while engaging in this dynamic Data Science Process activity [which is not unlike PLATO 'data science' activities of 50 years ago]." Blue Origin last September pledged to inspire youth to pursue space STEM careers as part of the Biden Administration's efforts to increase the space industry's capacity to meet the rising demand for the skilled technical workforce.

IEEE Spectrum reports on Codon, a Python compiler specifically developed to, as they put it, "turbocharge Python's Notoriously slow compiler."

"We do type checking during the compilation process, which lets us avoid all of that expensive type manipulation at runtime," says Ariya Shajii, an MIT CSAIL graduate student and lead author on a recent paper about Codon. Without any unnecessary data or type checking during runtime, Codon results in zero overhead, according to Shajii. And when it comes to performance, "Codon is typically on par with C++. Versus Python, what we usually see is 10 to 100x improvement," he says. But Codon's approach comes with its trade-offs. "We do this static type checking, and we disallow some of the dynamic features of Python, like changing types at runtime dynamically," says Shajii. "There are also some Python libraries we haven't implemented yet...."

Codon was initially designed for use in genomics and bioinformatics. "Data sets are getting really big in these fields, and high-level languages like Python and R are too slow to handle terabytes per set of sequencing data," says Shajii. "That was the gap we wanted to fill — to give domain experts who are not necessarily computer scientists or programmers by training a way to tackle large data without having to write C or C++ code." Aside from genomics, Codon could also be applied to similar applications that process massive data sets, as well as areas such as GPU programming and parallel programming, which the Python-based compiler supports. In fact, Codon is now being used commercially in the bioinformatics, deep learning, and quantitative finance sectors through the startup Exaloop, which Shajii founded to shift Codon from an academic project to an industry application.

To enable Codon to work with these different domains, the team developed a plug-in system. "It's like an extensible compiler," Shajii says. "You can write a plug-in for genomics or another domain, and those plug-ins can have new libraries and new compiler optimizations...." In terms of what's next for Codon, Shajii and his team are currently working on native implementations of widely used Python libraries, as well as library-specific optimizations to get much better performance out of these libraries. They also plan to create a widely requested feature: a WebAssembly back end for Codon to enable running code on a Web browser.

A YouTuber has published a video where he tricks ChatGPT into generating usable Windows 95 activation keys. Tom's Hardware reports: After asking Open AI's chatbot directly for Windows 95 keys, he received an expected reasoned refusal. YouTuber Enderman then asked the same thing but from a different angle. The result was a success which was somewhat limited by ChatGPT's ability to process natural language requests into formulas. [...] Some of the tested results were checked by attempting to activate a fresh Windows 95 install in a virtual machine. While the keys passed a casual inspection, it turns out that only about 1-in-30 keys seem to work as expected.

So what is the problem with these keys? Enderman complains that "the only issue keeping ChatGPT from successfully generating valid Windows 95 keys almost every attempt is the fact that it can't count the sum of digits and it doesn't know divisibility." In the five-digit string divisible by seven section, the AI appears to provide a stream of random numbers that don't pass this simple mathematical test. The report adds: "[W]hile quizzing ChatGPT about key generating may be fun, it would have probably been more productive to manipulate the AI into writing a Python script to generate a conforming key or to DIY it."

"With the recent attention on ChatGPT and OpenAI's release of their APIs, many developers have developed clients for modern platforms to talk to this super smart AI chatbot," writes maker/retro coding enthusiast yeokm1 . "However I'm pretty sure almost nobody has written one for a vintage platform like MS-DOS."

They share a blog post with all the details — including footage of their client ultimately running on a vintage IBM PC from 1984 (with a black and orange monitor and those big, boxy keys). "3.5 years ago, I wrote a Slack client to run on Windows 3.1," the blog post explains. "I thought to try something different this time and develop for an even older platform as a challenge."

One challenge was just finding a networking API for DOS. But everything came together, with the ChatGPT-for-DOS app written using Visual Studio Code text editor (testing on a virtual machine running DOS 6.22), parsing the JSON output from OpenAI's Chat Completion API. "And before you ask, I did not use ChatGPT for help to code this app in any way," the blog post concludes. But after the app was working, he used it to ask ChatGPT how one would build such an app — and ChatGPT erroneously suggested breezily that he just try accessing OpenAI's Python API from the DOS command line.

"What is the AI smoking...?"

Microsoft's GitHub unit created one of the first widely deployed programs using OpenAI's language-generation tools -- an app called Copilot that helped software developers write computer code. Now GitHub is adding a chat and voice feature that will let programmers ask how to accomplish certain coding tasks. From a report: The new version announced Wednesday is called Copilot X, which GitHub Chief Executive Officer Thomas Dohmke said he demonstrated to one of his children by asking it how to program a snake game in Python. The chat window can provide explanations of what segments of code are meant to do, create ways to test the code and propose fixes for bugs. Developers can also give instructions or ask questions using their voice.

GitHub first previewed Copilot in 2021 and widely released it last year. The initial product contained a completion tool that suggested snippets of programming code as a software developer typed. It attracted hundreds of thousands of developers by November and its product name had become short-hand for Microsoft's strategy to deploy these kinds of assistive technologies to a wide array of its products, from Office software to security programs. Now that OpenAI's ChatGPT chatbot has made a splash in popular culture, companies are trying to follow Microsoft in embedding the research lab's tools into products and business strategies. At the same time, rivals such as Alphabet's Google are releasing chatbot competitors.

Codon is a new "high-performance Python compiler that compiles Python code to native machine code without any runtime overhead," according to its README file on GitHub. Typical speedups over Python are on the order of 10-100x or more, on a single thread. Codon's performance is typically on par with (and sometimes better than) that of C/C++. Unlike Python, Codon supports native multithreading, which can lead to speedups many times higher still.
Its development team includes researchers from MIT's Computer Science and Artificial Intelligence lab, according to this announcement from MIT shared by long-time Slashdot reader Futurepower(R): The compiler lets developers create new domain-specific languages (DSLs) within Python — which is typically orders of magnitude slower than languages like C or C++ — while still getting the performance benefits of those other languages. "We realized that people don't necessarily want to learn a new language, or a new tool, especially those who are nontechnical. So we thought, let's take Python syntax, semantics, and libraries and incorporate them into a new system built from the ground up," says Ariya Shajii SM '18, PhD '21, lead author on a new paper about the team's new system, Codon. "The user simply writes Python like they're used to, without having to worry about data types or performance, which we handle automatically — and the result is that their code runs 10 to 100 times faster than regular Python. Codon is already being used commercially in fields like quantitative finance, bioinformatics, and deep learning."

The team put Codon through some rigorous testing, and it punched above its weight. Specifically, they took roughly 10 commonly used genomics applications written in Python and compiled them using Codon, and achieved five to 10 times speedups over the original hand-optimized implementations.... The Codon platform also has a parallel backend that lets users write Python code that can be explicitly compiled for GPUs or multiple cores, tasks which have traditionally required low-level programming expertise.... Part of the innovation with Codon is that the tool does type checking before running the program. That lets the compiler convert the code to native machine code, which avoids all of the overhead that Python has in dealing with data types at runtime.

"Python is the language of choice for domain experts that are not programming experts. If they write a program that gets popular, and many people start using it and run larger and larger datasets, then the lack of performance of Python becomes a critical barrier to success," says Saman Amarasinghe, MIT professor of electrical engineering and computer science and CSAIL principal investigator. "Instead of needing to rewrite the program using a C-implemented library like NumPy or totally rewrite in a language like C, Codon can use the same Python implementation and give the same performance you'll get by rewriting in C. Thus, I believe Codon is the easiest path forward for successful Python applications that have hit a limit due to lack of performance."

The other piece of the puzzle is the optimizations in the compiler. Working with the genomics plugin, for example, will perform its own set of optimizations that are specific to that computing domain, which involves working with genomic sequences and other biological data, for example. The result is an executable file that runs at the speed of C or C++, or even faster once domain-specific optimizations are applied.

"Google's Go language has re-entered the top 10 of the Tiobe index of programming language popularity, after a nearly six-year absence," reports InfoWorld: Go ranks 10th in the March edition of the index, after placing 11th the previous month. The language last appeared in the top 10 in July 2017.

The re-emergence of Go in the March 2023 index is being attributed to its popularity with software engineers and its strength in combining the right features, namely built-in concurrency, garbage collection, static typing, and good performance. Google's backing also helps, improving long-term trust in the language, Tiobe said.
The languages Go beat out include "assembly language" at #11, followed by MATLAB, Delphi/Object Pascal, Scratch, and Classic Visual Basic.

Here's the complete top-ten most popular programming languages, according to TIOBE:

• Python
• C
• Java
• C++
• C#
• Visual Basic
• JavaScript
• SQL
• PHP
• Go

Long-time Slashdot reader destinyland writes: They say "always be learning" — but do podcasts actually help? I've been trying to find podcasts that discuss programming, and I've enjoyed Lex Fridman's interviews with language creators like Guido van Rossum, Chris Lattner, and Brendan Eich (plus his long interviews with Donald Knuth). Then I discovered that GitHub, Red Hat, Stack Overflow, and the Linux Foundation all have their own podcast.

There's a developer podcast called "Corecursive" that I like with the tagline "the stories behind the code," plus a whole slew of (sometimes language-specific) podcasts at Changelog (including an interview with Brian Kernighan). And it seems like there's an entirely different universe of content on YouTube — like the retired Microsoft engineer doing "Dave's Garage," Software Engineering Daily, and the various documentaries by Honeypot.io. Computerphile has also scored various interviews with Brian Kernighan, and if you search YouTube enough you'll find stray interviews with Steve Wozniak.

But I wanted to ask Slashdot's readers: Do you listen to podcasts about computer science? And if so, which ones? (Because I'm always stumbling across new programming podcasts, which makes me worry about what else I've been missing out on.) Maybe I should also ask if you ever watch coding livestreams on Twitch — although that gets into the more general question of just how much content we consume that's related to our profession.
Fascinating discussions, or continuing work-related education? (And do podcasts really help keep your skills fresh? Are coding livestreams on Twitch just a waste of time?) Most importantly, does anyone have a favorite geek podcast that they're listening to? Share your own experience and opinions in the comments...

What's the best podcast about computer science?

Three years after Rails was introduced in 2005, InfoWorld asked whether it might the successor to Java.

That didn't happen. So this week InfoWorld "spoke to current and former Ruby programmers to try to trace the language's rise and fall." Some responses: "Rails came along at the cusp of a period of transformation and growth for the web," says Matthew Boeh, a Ruby developer since 2006. "It both benefited from and fueled that growth, but it was a foregone conclusion that it wasn't going to be the only success story." Boeh recently took a job as a senior staff software engineer at Lattice, a TypeScript shop. "You could say that Ruby has been a victim of its own success, in that its community was a major driving force in the command-line renaissance of recent years," he says. "In the early '00s it was introducing REPL-driven development to people who had never heard of Lisp, package management to people who would have been scared off by Perl's CPAN, test-driven development to people outside the highly corporate Java world, and so on. This is all stuff that is considered table stakes today. Ruby didn't originate any of it, but it was all popularized and made accessible by Rubyists...."

"The JavaScript ecosystem in its current form would have been unimaginable in 2004 — it needed both the command line renaissance and the takeoff of the web platform," adds Lattice's Boeh. "Did you know it took a full decade, 1999 to 2009, to release a single new version of the JavaScript standard? We get one yearly now. Rails became a big deal in the very last time period where it was possible to be a full-stack developer without knowing JavaScript...."

[W]hen it comes to data science, Python has a leg up because of the ready availability of libraries like TensorFlow and Keras. "These frameworks make it easy for coders to build data visualizations and write programs for machine learning," says Pulkit Bhardwaj, e-commerce coach at BoutiqueSetup.net. JavaScript, meanwhile, has spawned seemingly endless libraries that developers can easily download and adapt for just about any purpose. "As a technologist, you can go on your own hero's journey following whatever niche thing you think is the right way to go," says Trowbridge. But when it comes to JavaScript, "these libraries are excellent. Why ignore all of that?"

Many of those libraries were developed by community members, which inspired others to contribute in a snowball effect familiar to anyone involved in open source. But one big player has had an outsized influence here. Python's TensorFlow, which Bhardwaj called a "game-changer," was released by Google, which has followed academia's lead and made Python its internal scripting language. Google, as the maker of the dominant web browser, also has an obvious interest in boosting JavaScript, and Trowbridge gives Google much of the credit for making JavaScript much faster and more memory efficient than it once was: "In some ways it feels almost like a low level language," he says. Meanwhile, Ruby is widely acknowledged to be lagging in performance, in part because it lacks the same sort of corporate sponsor with resources for improving it.

More than 400 malicious packages were recently uploaded to PyPI (Python Package Index), the official code repository for the Python programming language, in the latest indication that the targeting of software developers using this form of attack isn't a passing fad. From a report: All 451 packages found recently by security firm Phylum contained almost identical malicious payloads and were uploaded in bursts that came in quick succession. Once installed, the packages create a malicious JavaScript extension that loads each time a browser is opened on the infected device, a trick that gives the malware persistence over reboots. The JavaScript monitors the infected developer's clipboard for any cryptocurrency addresses that may be copied to it. When an address is found, the malware replaces it with an address belonging to the attacker. The objective: intercept payments the developer intended to make to a different party.

An anonymous reader quotes a report from MIT Technology Review: Many software projects emerge because -- somewhere out there -- a programmer had a personal problem to solve. That's more or less what happened to Graydon Hoare. In 2006, Hoare was a 29-year-old computer programmer working for Mozilla, the open-source browser company. Returning home to his apartment in Vancouver, he found that the elevator was out of order; its software had crashed. This wasn't the first time it had happened, either. Hoare lived on the 21st floor, and as he climbed the stairs, he got annoyed. "It's ridiculous," he thought, "that we computer people couldn't even make an elevator that works without crashing!" Many such crashes, Hoare knew, are due to problems with how a program uses memory. The software inside devices like elevators is often written in languages like C++ or C, which are famous for allowing programmers to write code that runs very quickly and is quite compact. The problem is those languages also make it easy to accidentally introduce memory bugs -- errors that will cause a crash. Microsoft estimates that 70% of the vulnerabilities in its code are due to memory errors from code written in these languages.

Most of us, if we found ourselves trudging up 21 flights of stairs, would just get pissed off and leave it there. But Hoare decided to do something about it. He opened his laptop and began designing a new computer language, one that he hoped would make it possible to write small, fast code without memory bugs. He named it Rust, after a group of remarkably hardy fungi that are, he says, "over-engineered for survival." Seventeen years later, Rust has become one of the hottest new languages on the planet -- maybe the hottest. There are 2.8 million coders writing in Rust, and companies from Microsoft to Amazon regard it as key to their future. The chat platform Discord used Rust to speed up its system, Dropbox uses it to sync files to your computer, and Cloudflare uses it to process more than 20% of all internet traffic.

When the coder discussion board Stack Overflow conducts its annual poll of developers around the world, Rust has been rated the most "loved" programming language for seven years running. Even the US government is avidly promoting software in Rust as a way to make its processes more secure. The language has become, like many successful open-source projects, a barn-raising: there are now hundreds of die-hard contributors, many of them volunteers. Hoare himself stepped aside from the project in 2013, happy to turn it over to those other engineers, including a core team at Mozilla. It isn't unusual for someone to make a new computer language. Plenty of coders create little ones as side projects all the time. But it's meteor-strike rare for one to take hold and become part of the pantheon of well-known languages alongside, say, JavaScript or Python or Java. How did Rust do it?

Last week Microsoft announced 10,000 layoffs — and a multibillion-dollar investment in OpenAI, the company that created ChatGPT.

But OpenAI also released a tool called Codex in August of 2021 "designed to translate natural language into code," reports Semafor. And now OpenAI "has ramped up its hiring around the world, bringing on roughly 1,000 remote contractors over the past six months in regions like Latin America and Eastern Europe, according to people familiar with the matter."

The article points out that roughly 40% of those contractors "are computer programmers who are creating data for OpenAI's models to learn software engineering tasks." "A well-established company, which is determined to provide world-class AI technology to make the world a better and more efficient place, is looking for a Python Developer," reads one OpenAI job listing in Spanish, which was posted by an outsourcing agency....

OpenAI appears to be building a dataset that includes not just lines of code, but also the human explanations behind them written in natural language. A software developer in South America who completed a five-hour unpaid coding test for OpenAI told Semafor he was asked to tackle a series of two-part assignments. First, he was given a coding problem and asked to explain in written English how he would approach it. Then, the developer was asked to provide a solution. If he found a bug, OpenAI told him to detail what the problem was and how it should be corrected, instead of simply fixing it.

"They most likely want to feed this model with a very specific kind of training data, where the human provides a step-by-step layout of their thought-process," said the developer, who asked to remain anonymous to avoid jeopardizing future work opportunities.

An anonymous reader quotes a report from 9to5Linux: Ubuntu maker Canonical announced Thursday the general availability of its Ubuntu Pro comprehensive subscription for Ubuntu users who want to expand the security updates and compliance of their systems.

First released in a beta version in October 2022 with free subscriptions for personal and small-scale commercial use on up to 5 machines, Ubuntu Pro is only available for Ubuntu LTS (Long-Term Support) releases, starting with Ubuntu 16.04, and promises up to 10 years of security updates, as well as access to exclusive tools. These include Ansible, Apache Tomcat, Apache Zookeeper, Docker, Drupal, Nagios, Node.js, phpMyAdmin, Puppet, PowerDNS, Python 2, Redis, Rust, WordPress, ROS, and many others.

The Ubuntu Pro subscription promises patches for critical CVEs in less than 24 hours and expands the optional technical support to an additional 23,000 open-source packages and toolchains beyond the main operating system, not just for Ubuntu's main software repository....

Canonical says that if you need Ubuntu Pro for more than five PCs, you will have to purchase a paid plan, which is currently priced at $25 USD per year for workstations or $500 USD per year for servers with a 30-day free trial. Official Ubuntu Community members get free support for up to 50 machines.

74.48% of developers use Microsoft's Visual Studio Code, according to one survey conducted by StackOverflow. And besides GitHub Copilot, there's over 40,000 other extensions in the VSCode Marketplace.

Unfortunately, InfoWorld reports, "Researchers at Aqua Nautilus say they have found that attackers could easily impersonate popular extensions and trick unknowing developers into downloading them." It can be challenging to distinguish between malicious and benign extensions, and the lack of sandbox capabilities means that extensions could install ransomware, wipers, and other malicious code, Aqua security researcher Ilay Goldman wrote in a January 6 blog post. ["In fact, it can access and even alter all the code that you have locally and even use your SSH key to change the code in all your organization's repositories."] VS Code extensions, which provide capabilities ranging from Python language support to JSON file editing, can be downloaded from Microsoft's Visual Studio Code Marketplace.

Aqua Nautilus uploaded an extension masquerading as the Prettier code formatter and saw more than 1,000 installs in less than 48 hours, from around the world. The spoof extension has been removed.

Goldman noted that the Visual Studio Code Marketplace runs a virus scan for each new extension and subsequent updates, and removes malicious extensions when it finds them. Users can report suspicious-looking extensions via a Report Abuse link.
"While the media is full of stories about malicious packages that have been uploaded to popular package managers such as NPM and PyPI, there is very little information about malicious VSCode extension," the blog post notes. Yet it points out that a blue checkmark on a VSCode extension "merely means that whoever the publisher is has proven the ownership of a domain. That means any domain."

And even Microsoft acknowledged to InfoWorld that social engineering techniques have been used to persuade victims to download malicious extensions — though they point out that Microsoft confirms that each extension has a Marketplace certificate and verifiable signature before being installed. "To help make informed decisions, we recommend consumers review information, such as domain verification, ratings and feedback to prevent unwanted downloads."

InfoWorld reports: JavaScript, Java, and Python skills are most in-demand by recruiters, according to a report published this week by tech hiring platforms CodinGame and CoderPad. But while the supply of those skills exceeds demand, the demand for TypeScript, Swift, Scala, Kotlin, and Go skills all exceed supply.

The State of Tech Hiring in 2023, a CodinGame-CoderPad report published January 10, draws on a survey of 14,000 professionals and offers insights into what 2023 may hold for tech industry recruiters and job seekers. The demand for JavaScript, Java, and Python skills is consistent with previous years, the report notes.

Among development frameworks, Node.js, React, and .NET Core proved to be the best-known and most in-demand.
InfoWorld summarizes some other interesting findings:

• "59% of developers do not have a university degree in computer science. Nearly one-third consider themselves primarily self-taught."

• "Developers' main challenges at work include unplanned changes to their schedule, unclear direction, and a lack of technical knowledge by team members."

• "Most teams are now hybrid between remote and on-site work. Only 15% work onsite 100% of the time."

"The Tiobe index gauges language popularity using a formula that assesses searches on programming languages in Google, Bing, Yahoo, Wikipedia, and other search engines," writes InfoWorld. And they add that this year the "vaunted" C++ programming language was the index's biggest gainer in 2022.

TIOBE's announcement includes their calculation that C++ rose 4.62% in popularity in 2022: Runners up are C (+3.82%) and Python (+2.78%). Interestingly, C++ surpassed Java to become the number 3 of the TIOBE index in November 2022. The reason for C++'s popularity is its excellent performance while being a high level object-oriented language. Because of this, it is possible to develop fast and vast software systems (over millions of lines of code) in C++ without necessarily ending up in a maintenance nightmare.
So which programming languages are most popular now? For what it's worth, here's TIOBE's latest ranking:


- Python
- C
- C++
- Java
- C#
- Visual Basic
- JavaScript
- SQL
- Assembly Language
- PHP


InfoWorld adds that "Helping C++ popularity was the publication of new language standards with interesting features, such as C++ 11 and C++ 20."

More from TIOBE: What else happened in 2022? Performance seemed to be important. C++ competitor Rust entered the top 20 again (being at position #26 one year ago), but this time it seems to be for real. Lua, which is known for its easy interfacing with C, jumped from position #30 to #24. F# is another language that made an interesting move: from position #74 to position #33 in one years' time. Promising languages such as Kotlin (from #29 to #25), Julia (from #28 to #29) and Dart (from #37 to #38) still have a long way to go before they reach the top 20. Let's see what happens in 2023.

An anonymous reader quotes a report from TechCrunch: A recent study finds that software engineers who use code-generating AI systems are more likely to cause security vulnerabilities in the apps they develop. The paper, co-authored by a team of researchers affiliated with Stanford, highlights the potential pitfalls of code-generating systems as vendors like GitHub start marketing them in earnest. The Stanford study looked specifically at Codex, the AI code-generating system developed by San Francisco-based research lab OpenAI. (Codex powers Copilot.) The researchers recruited 47 developers -- ranging from undergraduate students to industry professionals with decades of programming experience -- to use Codex to complete security-related problems across programming languages including Python, JavaScript and C.

Codex was trained on billions of lines of public code to suggest additional lines of code and functions given the context of existing code. The system surfaces a programming approach or solution in response to a description of what a developer wants to accomplish (e.g. "Say hello world"), drawing on both its knowledge base and the current context. According to the researchers, the study participants who had access to Codex were more likely to write incorrect and "insecure" (in the cybersecurity sense) solutions to programming problems compared to a control group. Even more concerningly, they were more likely to say that their insecure answers were secure compared to the people in the control.

Megha Srivastava, a postgraduate student at Stanford and the second co-author on the study, stressed that the findings aren't a complete condemnation of Codex and other code-generating systems. The study participants didn't have security expertise that might've enabled them to better spot code vulnerabilities, for one. That aside, Srivastava believes that code-generating systems are reliably helpful for tasks that aren't high risk, like exploratory research code, and could with fine-tuning improve in their coding suggestions. "Companies that develop their own [systems], perhaps further trained on their in-house source code, may be better off as the model may be encouraged to generate outputs more in-line with their coding and security practices," Srivastava said. The co-authors suggest vendors use a mechanism to "refine" users' prompts to be more secure -- "akin to a supervisor looking over and revising rough drafts of code," reports TechCrunch. "They also suggest that developers of cryptography libraries ensure their default settings are secure, as code-generating systems tend to stick to default values that aren't always free of exploits."