#CodeQL

[tsqc]

AI-Powered Code Vulnerability Detection: CodeQL’s Role in Securing Modern Software

[3acesnews]

CodeQL 2.22.0 Enhances Go Coverage and Supports Swift 6.1.2

[hackernewsrobot]

GitHub CodeQL Actions Critical Supply Chain Vulnerability (CodeQLEAKED)

https://www.praetorian.com/blog/codeqleaked-public-secrets-exposure-leads-to-supply-chain-attack-on-github-codeql/

[generativeinai]

How Generative AI in IT Workspace is Revolutionizing Software Development

Generative AI is transforming various industries, and the IT workspace is no exception. One of its most profound impacts is in software development, where AI-driven tools are reshaping how applications are designed, coded, tested, and maintained. By automating repetitive tasks, enhancing creativity, and reducing human error, Generative AI in IT workspace is revolutionizing the way software developers work.

In this blog, we’ll explore the various ways generative AI is influencing software development, its benefits, challenges, and what the future holds for AI-powered coding.

What is Generative AI in Software Development?

Generative AI refers to artificial intelligence models that can create content, including code, text, images, and even complex algorithms. In the context of software development, generative AI is used to write code, detect errors, generate documentation, optimize software performance, and even suggest new functionalities.

AI-powered coding assistants such as GitHub Copilot, OpenAI Codex, and Google’s Codey are already proving their value by streamlining the software development lifecycle.

How Generative AI is Transforming Software Development

1. Automating Code Generation

One of the most significant ways generative AI is revolutionizing software development is by automating code writing. AI-powered tools can generate code snippets, functions, or even entire programs based on natural language instructions.

Example: A developer can simply type a prompt like "Generate a Python function to sort a list using quicksort", and AI-powered coding assistants can write the function in seconds.

Benefits:

Reduces manual coding effort

Speeds up development

Minimizes syntax and logical errors

2. Enhancing Code Quality and Debugging

Generative AI can analyze existing code to detect bugs, suggest fixes, and optimize performance. AI-powered debugging tools can automatically scan for vulnerabilities, ensuring that software remains secure and efficient.

Example: AI tools like DeepCode and CodeQL can analyze thousands of lines of code and highlight potential security flaws before deployment.

Benefits:

Faster bug detection and resolution

Improved security and reliability

Reduced manual debugging efforts

3. Accelerating Software Testing

Testing is a crucial phase in software development, but it is often time-consuming. Generative AI can automate test case generation, execute test scripts, and even predict potential failures.

Example: AI-powered tools like Testim and Applitools can generate automated test scripts based on user behavior, reducing the need for manual testing.

Benefits:

Reduces testing time

Improves software quality

Ensures better coverage of test scenarios

4. Boosting Developer Productivity

Generative AI allows developers to focus on high-level problem-solving rather than routine coding tasks. By automating repetitive work, developers can concentrate on more creative and strategic aspects of software development.

Example: A full-stack developer can leverage AI to generate frontend UI components, backend logic, and API integrations—saving significant time.

Benefits:

Faster project delivery

Reduced cognitive load for developers

Enhanced collaboration between teams

5. Simplifying Code Documentation and Knowledge Sharing

Writing documentation is a tedious task, but AI can automatically generate comprehensive documentation based on existing codebases. This makes it easier for developers to understand and maintain complex projects.

Example: AI tools like Mintlify and CodiumAI can generate meaningful docstrings, comments, and even full documentation pages based on the code structure.

Benefits:

Saves developers’ time

Improves code maintainability

Facilitates onboarding of new team members

Challenges of Using Generative AI in Software Development

While generative AI offers numerous benefits, it also comes with some challenges:

1. AI-Generated Code May Contain Errors

AI-generated code is not always perfect and may contain logical errors or inefficiencies.

Developers must review and validate AI-generated code to ensure its correctness.

2. Ethical and Security Concerns

AI models may generate biased or insecure code, leading to potential vulnerabilities.

Organizations need to establish AI governance policies to ensure ethical and secure AI usage.

3. Over-Reliance on AI

Developers must be careful not to become too dependent on AI tools.

While AI assists in coding, critical thinking and problem-solving skills remain essential.

The Future of Generative AI in Software Development

The future of Generative AI in IT workspace looks promising. Here are some key trends we can expect:

AI-Driven DevOps: AI will play a bigger role in automating CI/CD pipelines, monitoring software performance, and predicting failures.

AI-Assisted Collaboration: AI-powered chatbots and virtual coding assistants will enhance collaboration among developers by providing real-time coding suggestions.

More Advanced AI Code Review Systems: Future AI tools will not only generate code but also analyze entire projects to suggest architecture improvements.

Hybrid AI-Developer Workflows: AI will act as a co-pilot, working alongside developers rather than replacing them.

Conclusion

Generative AI in IT workspace is revolutionizing software development by automating code generation, improving debugging, accelerating testing, and enhancing productivity. While AI presents exciting opportunities, it also requires responsible usage to avoid security risks and ethical concerns.

As AI technology continues to evolve, software development will become more efficient, innovative, and collaborative. Developers who learn to work alongside AI will have a significant advantage in the future of IT.

[majornibos]

https://github.com/github/codeql-ctf-go-return/issues/9

[cebozcom]

GitHub Introduces Code Scanning Autofix: Enhancing Security with AI | CeBoz.com

GitHub's new code scanning autofix feature, powered by GitHub Copilot and CodeQL, helps developers remediate security vulnerabilities efficiently.

[the-hacker-news]

GitHub Launches AI-Powered Autofix Tool to Assist Devs in Patching Security Flaws

The Hacker News : GitHub on Wednesday announced that it's making available a feature called code scanning autofix in public beta for all Advanced Security customers to provide targeted recommendations in an effort to avoid introducing new security issues. "Powered by GitHub Copilot and CodeQL, code scanning autofix covers more than 90% of alert types in JavaScript, Typescript, Java, and http://dlvr.it/T4PT6G Posted by : Mohit Kumar ( Hacker )

[tamarovjo4]

GitHub releases code scanning autofix, powered by Copilot and CodeQL, in public beta for GitHub Advanced Security customers, to help them fix vulnerabilities (Frederic Lardinois/TechCrunch)

http://dlvr.it/T4MsYX

[nenamatic]

AI is becoming more and more popular in the software development world, and we think it's for a good reason. Developers are always looking for ways to make their workflows faster, more efficient, and more user-friendly. AI has changed the game for developers, but before you think it's going to take over, remember that developers are the ones who come up with the ideas. AI can write code and suggest improvements, but it's up to you to make them happen. So let's check out the 9 best AI tools for great developers to get more job done. Claude Claude, the HTML0 version of the AI chatbot and content generator, was created by the AI startup Anthropic, which is known for creating funny content. It's been praised by users for its security and personalisation, as well as its great comedy and creative content creation. It's got a great ability to get feedback and improve its communication skills, which makes it stand out from other chatbots. Unfortunately, dangerous requests can still pass through when they're put into an imaginary scenario. Unlike other chatbots, Claude doesn't come with a free version and can't connect to the internet, and it's only available in Europe and the US. GitHub Copilot GitHub Copilot was created in collaboration with Microsoft and costs just $10 a month. It provides code suggestions based on ML algorithms and open-source code, but developers have to make sure their suggestions are accurate due to its limitations. It's available in two versions: Copilot for people and Copilot for businesses. It can turn NLP prompts into code, provide multiple-line functions, handle corporate policies, and help with corporate proxy servers. It also offers code recommendations based on styles and context, giving users the option to customize and choose. TabNine TabNine is an artificial intelligence (AI) based code completion tool developed by Codota. TabNine uses machine learning algorithms to provide smart code suggestions for over 20 programming languages and 15 editors, including popular versions like JAVA, python, C++, vscode, intellijs, and androidstudio. One of TabNine’s main strengths is its ability to learn from your code base. It analyzes patterns in your code and provides you with personalized and contextual recommendations. The dual-engine nature of TabNine (local and cloud-based) makes it stand out from the crowd and allows it to operate without an internet connection. While TabNine isn’t a complete script generator, it significantly improves the speed of writing code by speeding up the development process and preserving the code’s privacy. However, TabNine is not without flaws, it’s free and is designed for smaller projects, and it can overload the interface with irrelevant suggestions. CodeQL CodeQL is a really powerful semantic analysis tool that was built by GitHub. It's different from other code analysis tools because it doesn't rely on pattern matching. Instead, it looks at the connections between code segments, data flows, and potential vulnerabilities. It's like trying to understand the language, not just looking for keywords. Your code snapshots capture not just the code itself, but all the dependencies, connections, and the system it's part of. Think of it like creating a virtual version of your app. Usually, these tools just find isolated issues, but they don't look at the bigger picture. CodeQL looks at how different code segments interact and can help you identify complex vulnerabilities that could be across different parts or files. CodeWP Isotropic has created CodeWP, an AI-powered WordPress code generator. It's specifically designed for WordPress developers, with features like Live collaboration, real-time code feedback, and easy version control. You can use it with JavaScript as well as PHP, and it's designed with popular WordPress plugins like WooCommerce in mind. Its main advantage is that it focuses on WordPress, giving you code recommendations and taking care of all the work that goes into WordPress development.

But it can be a bit unstable at times, and it's not great for big projects. AlphaCode AlphaCode is a powerful AI tool that can create code on a huge scale and use critical thinking based on experience. It has a huge transformer-based model with 41.4 billion parameters, and it offers training in Python and C++. It's free, but you should know that the learning process is up to you and can be a bit shaky. You can train using GitHub code repositories and refine it through CodeContests with techniques like generating samples, smart filtering, and clustering. It can tackle complex problems similar to what humans do, and its ability to create code on a big scale combined with smart filtering puts it in the same league as humans. Phind Phind is a search engine that's tailored to developers. It can provide accurate and useful answers to questions about programming, which sets it apart from other AI tools like ChatGPT. Plus, if you search for related websites, Phind can give you the most complete answer to what you're looking for. If you have any coding worries, Phind can help you out by giving you clear, precise, and scalable answers that are easy to do. Plus, the results of your search give you more info than just the AI-generated one, so you can get code examples and useful info all at once. Phind is free in July 2023 and it's a great resource for developers. AskCodi AskCodi is powered by OpenAI, a software that can generate code that answers questions about programming and provides useful code ideas. You can easily install AskCodi to your preferred IDE (Visual Studio Code), PyCharm, or IntelliJ IDE. It will help you improve your code. AskCodi can create SQL queries and DocStrings as well. As one of the best things about AskCodi, it can generate codes from easy-to-understand prompts. It can answer questions about coding easily, so you can understand even the most complicated code concepts. It can suggest code as you write, which helps you avoid errors and generate efficient code. It has been designed for users who want a user-friendly experience when coding by taking care of the tedious tasks like generating code and answering questions about coding. No-cost plans are available for AskCodi; premium plans start at $7. RegExGpt Creating RegEx phrases can be a real pain, especially if you're new to the game. But don't worry, RegExGPT makes it easy! You can create simple RegEx expressions using simple English prompts. This takes the guesswork out of creating complex RegEx patterns, so developers don't have to spend hours writing and testing them. All you have to do is enter the input string and the expected output, and then you can create a RegEx pattern that matches it. It's a great tool for automating things like filtering and analyzing text. Just make sure to double-check the expressions you create before you start. Conclusion These tools aren't perfect, but they're always improving and getting better, so they're more reliable and dependable for users. Things like how much computing power you have, how you handle your personal data, how much it costs, what language it supports, and if it's available can all affect how you choose to use it for different types of development. But the good news is that these AI tools have a lot of potential. As they get better, developers will be able to streamline their workflows, get more done, and solve complex problems more quickly. It's a bright future, and it'll be fun to see how they develop over the next few years. Also, you can read y other articles- Top 21 ChatGpt Plugins You Should Know Who Created ChatGpt? Who Is Happy To Owns It Now?

[demianblog]

GitHub lanza nuevas funciones de seguridad de SDLC que incluyen informes privados de vulnerabilidad

GitHub lanza nuevas funciones de seguridad de SDLC que incluyen informes privados de vulnerabilidad

GitHub ha anunciado nuevas funciones de seguridad en su plataforma para ayudar a proteger el ciclo de vida del desarrollo de software (SDLC). Estos incluyen informes de vulnerabilidad privados, soporte de escaneo de vulnerabilidad CodeQL para el lenguaje de programación Ruby y dos nuevas opciones de descripción general de seguridad. La plataforma de desarrollo líder en el mundo dijo que estas…

[hackgit]

​C0deVari4nt Variant analysis and visualisation tool that inspects codebases for similar...

​C0deVari4nt Variant analysis and visualisation tool that inspects codebases for similar vulnerabilities. It leverages CodeQL, a semantic code analysis engine, to query code based on user-controlled CodeQL query templates and passes the results to Neo4j for further exploration and visualisation. This enables quick and comprehensive variant analysis based on previous vulnerability reports. The Neo4j visualisation feature provides additional insight for developers into vulnerable code paths and allows them to effectively triage potential variants https://github.com/whitesquirrell/C0deVari4nt

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[tastydregs]

AI Can Write Code Like Humans—Bugs and All

Some software developers are now letting artificial intelligence help write their code. They’re finding that AI is just as flawed as humans.

Last June, GitHub, a subsidiary of Microsoft that provides tools for hosting and collaborating on code, released a beta version of a program that uses AI to assist programmers. Start typing a command, a database query, or a request to an API, and the program, called Copilot, will guess your intent and write the rest.

Alex Naka, a data scientist at a biotech firm who signed up to test Copilot, says the program can be very helpful, and it has changed the way he works. “It lets me spend less time jumping to the browser to look up API docs or examples on Stack Overflow,” he says. “It does feel a little like my work has shifted from being a generator of code to being a discriminator of it.”

But Naka has found that errors can creep into his code in different ways. “There have been times where I've missed some kind of subtle error when I accept one of its proposals,” he says. “And it can be really hard to track this down, perhaps because it seems like it makes errors that have a different flavor than the kind I would make.”

The risks of AI generating faulty code may be surprisingly high. Researchers at NYU recently analyzed code generated by Copilot and found that, for certain tasks where security is crucial, the code contains security flaws around 40 percent of the time.

The figure “is a little bit higher than I would have expected,” says Brendan Dolan-Gavitt, a professor at NYU involved with the analysis. “But the way Copilot was trained wasn’t actually to write good code—it was just to produce the kind of text that would follow a given prompt.”

Despite such flaws, Copilot and similar AI-powered tools may herald a sea change in the way software developers write code. There’s growing interest in using AI to help automate more mundane work. But Copilot also highlights some of the pitfalls of today’s AI techniques.

“It seems like it makes errors that have a different flavor than the kind I would make.”

Alex Naka, data scientist

While analyzing the code made available for a Copilot plugin, Dolan-Gavitt found that it included a list of restricted phrases. These were apparently introduced to prevent the system from blurting out offensive messages or copying well-known code written by someone else.

Oege de Moor, vice president of research at GitHub and one of the developers of Copilot, says security has been a concern from the start. He says the percentage of flawed code cited by the NYU researchers is only relevant for a subset of code where security flaws are more likely.

De Moor invented CodeQL, a tool used by the NYU researchers that automatically identifies bugs in code. He says GitHub recommends that developers use Copilot together with CodeQL to ensure their work is safe.

The GitHub program is built on top of an AI model developed by OpenAI, a prominent AI company doing cutting-edge work in machine learning. That model, called Codex, consists of a large artificial neural network trained to predict the next characters in both text and computer code. The algorithm ingested billions of lines of code stored on GitHub—not all of it perfect—in order to learn how to write code.

OpenAI has built its own AI coding tool on top of Codex that can perform some stunning coding tricks. It can turn a typed instruction, such as “Create an array of random variables between 1 and 100 and then return the largest of them,” into working code in several programming languages.

Another version of the same OpenAI program, called GPT-3, can generate coherent text on a given subject, but it can also regurgitate offensive or biased language learned from the darker corners of the web.

Copilot and Codex have led some developers to wonder if AI might automate them out of work. In fact, as Naka’s experience shows, developers need considerable skill to use the program, as they often must vet or tweak its suggestions.

[mrhackerco]

Microsoft release open-source CodeQL queries to hunt SolarWinds hacks | MrHacker.Co #breach #cyber-attack #hacking #malware #russia #hacker #hacking #cybersecurity #hackers #linux #ethicalhacking #programming #security #mrhacker