Windows 10: New Intel Machine Programming Tool Detects Bugs in Code

Brink · Dec 3, 2020

Whatâs New: Today, Intel unveiled ControlFlag â a machine programming research system that can autonomously detect errors in code. Even in its infancy, this novel, self-supervised system shows promise as a powerful productivity tool to assist software developers with the labor-intensive task of debugging. In preliminary tests, ControlFlag trained and learned novel defects on over 1 billion unlabeled lines of production-quality code.
âWe think ControlFlag is a powerful new tool that could dramatically reduce the time and money required to evaluate and debug code. According to studies, software developers spend approximately 50% of the time debugging. With ControlFlag, and systems like it, I imagine a world where programmers spend notably less time debugging and more time on what I believe human programmers do best â expressing creative, new ideas to machines.â
âJustin Gottschlich, principal scientist and director/founder of Machine Programming Research at Intel Labs
Why It Matters: In a world increasingly run by software, developers continue to spend a disproportionate amount of time fixing bugs rather than coding. Itâs estimated that of the $1.25 trillion that software development costs the IT industry every year, 50 percent is spent debugging code.

Debugging is expected to take an even bigger toll on developers and the industry at large. As we progress into an era of heterogenous architectures â one defined by a mix of purpose-built processors to manage the massive sea of data available today â the software required to manage these systems becomes increasingly complex, creating a higher likelihood for bugs. In addition, it is becoming difficult to find software programmers who have the expertise to correctly, efficiently and securely program across diverse hardware, which introduces another opportunity for new and harder-to-spot errors in code.

When fully realized, ControlFlag could help alleviate this challenge by automating the tedious parts of software development, such as testing, monitoring and debugging. This would not only enable developers to do their jobs more efficiently and free up more time for creativity, but it would also address one of the biggest price tags in software development today.

How It Works: ControlFlagâs bug detection capabilities are enabled by machine programming, a fusion of machine learning, formal methods, programming languages, compilers and computer systems.

ControlFlag specifically operates through a capability known as anomaly detection. As humans existing in the natural world, there are certain patterns we learn to consider ânormalâ through observation. Similarly, ControlFlag learns from verified examples to detect normal coding patterns, identifying anomalies in code that are likely to cause a bug. Moreover, ControlFlag can detect these anomalies regardless of programming language.

A key benefit of ControlFlagâs unsupervised approach to pattern recognition is that it can intrinsically learn to adapt to a developerâs style. With limited inputs for the control tools that the program should be evaluating, ControlFlag can identify stylistic variations in programming language, similar to the way that readers recognize the differences between full words or using contractions in English.

The tool learns to identify and tag these stylistic choices and can customize error identification and solution recommendations based on its insights, which minimizes ControlFlagâs characterizations of code in error that may simply be a stylistic deviation between two developer teams.

Intel has even started evaluating using ControlFlag internally to identify bugs in its own software and firmware product development. It is a key element of Intelâs Rapid Analysis for Developers project, which aims to accelerate velocity by providing expert assistance.

More Context: Intel Labs Day (Press Kit) | MISM: An End-to-End Neural Code Similarity System | Why More Software Development Needs to Go to the Machines | Intel Labs (Press Kit) | Three Pillars of Machine Programming
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Source: Intel Machine Programming Tool Detects Bugs in Code | Intel Newsroom

:)

Brink · Dec 3, 2020

Intel on The Future of Machine Programming

The Future of Machine Programming: Justin Gottschlich, Leader of Machine Programming Research at Intel Labs, Explains Machine Programming

Justin Gottschlich of Intel Labs leads a team of machine programming researchers. Their aim is to automate software development to reduce coding errors and address a shortage of trained expert programmers. (Credit: Walden Kirsch/Intel Corporation)

Our expert: Justin Gottschlich leads the Machine Programming Research (MPR) team in the Systems and Software Research Lab. Justin’s newly-formed research group focuses on the pioneering promise of machine programming, which is a fusion of machine learning, formal methods, programming languages, compilers and computer systems.

His simple explanation of machine programming: MPR uses forms of machine learning and other automatic methods to create software capable of creating its own software. It’s called machine programming and is fundamentally about automating software development and maintenance. When fully realized, machine programming will enable everyone to express their creativity and develop their own software without writing a single line of code.

More: See all of the Future of Technology series

Machine programming’s promise: In today’s technological landscape, software is integrated into almost everything we do. It controls many aspects of our mobile devices – laptops, tablets, phones. It connects us to the internet and drives our social media feeds. It virtualizes our data centers and makes our homes more intelligent. But developing and maintaining software is a time-consuming and error-prone process, Justin says. “I believe we can create a society where everyone can create software, but machines will handle the ‘programming’ part,” he says. “Thus, ‘machine programming.’”

A shortage of human programmers: A core problem for Intel and other leading tech companies, according to Justin, is that they are running low on senior developers – a shortage that crimps the amount of programming across all industries. According to code.org, there are 500,000 open programming positions available in the U.S. alone — compared with an annual crop of 50,000 graduating computer science majors. A similar shortage can be found across the European Union. In the programming jobs market, Justin says, at best only 10% of the people filling those jobs have the computer science training to become top-level advanced developers. With today’s heterogeneous hardware — CPUs, GPUs, FPGAs, ASICs, neuromorphic and, soon, quantum chips — it will become difficult, perhaps impossible, to find developers who can correctly, efficiently, and securely program across all of that hardware.

Now is the time: Machine programming is a fusion of different fields. It uses automatic programming technique, from precise (e.g., formal program synthesis) to probabilistic (e.g., differentiable programming) methods. It also uses and learns from everything we’ve built in hardware and software to date. Researchers have dabbled in machine programming since the 1950s, Justin says. “But today is different. We’re at an inflection point with new machine learning algorithms, new and improved hardware, and rich and dense programming data. These are the three essential ingredients that we believe enable machine programming.” One example is illustrated by recent genetic algorithm (GA) research from Justin’s team, which illustrates how the fitness function of a genetic algorithm – a complicated machine learning heuristic developed by expert programmers – can be automated. Justin says this work likely wouldn’t have been possible just a few years ago.

Refusing to accept bugs: Almost all large-scale software today (e.g., operating systems, browsers, social media platforms) includes accuracy, performance or security bugs. “Our latest NeurIPS ’19 paper provides early evidence that certain types of bugs that have historically evaded even expert programmer detection can be automatically detected with machine programming, requiring zero human intervention,” Justin says. “The next step is to automatically fix them.”

From 500,000 lines of code to 500: Justin points to a well-known example of machine programming’s benefits. Google Translate, a service that automatically translates among languages, was built by engineers who hand-coded around 500,000 lines using classical programming techniques. With the advent of machine programming, Google rewrote its code, partially using differentiable programming (one small slice of the overall machine programming pie). That rewrite decreased the code base from 500,000 lines to 500 lines, a 1,000x reduction. “Not only did the code size shrink by 1,000 times,” Justin says, “the accuracy of the system actually improved – it’s incredible.”

More programming jobs, not fewer: Machine programming will not eliminate jobs, Justin contends, but instead create them – possibly millions of them. The more menial aspects of programming will be automated, he says, which is the goal. With machine programming, he adds, “our blue-sky vision is so long as you can express your ideas (as we call it – intention) in some way that the machine can recognize – be it natural language, visual diagrams or gestures – machine programming builds a path for you to create your own software.” To begin building these advanced machine programming systems, Justin says, we’ll rely heavily on a community of programmers and scientists – those who can work across platforms, machine learning and formal techniques, heterogeneous hardware, and many programming languages. Justin and team outline their future vision of machine programming in a paper jointly published with MIT researchers, “The Three Pillars of Machine Programming.”

Justin will deliver the keynote address at the University of Pennsylvania Industry Day event on Friday, Oct. 25: Future of Safe Autonomy.
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Source: Why More Software Development Needs to Go to the Machines | Intel Newsroom

MxPhenom 216 · Dec 3, 2020

Coding language for BIOS?

My guess is it is specific to America Megatrends or Phoenix BIOS (probably C or assembly). For EFI (looks like the tools use C), see: http://x86asm.net/articles/uefi-programming-first-steps/
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That is what I was figuring. C or Assembly. I really like coding in C, so far more then C++.

Reading over that UEFI Programming steps website. Really cool stuff.

Brink · Dec 3, 2020

Intel Expanding Bug Bounty Program

At Intel, we believe that working with security researchers is a crucial part of identifying and mitigating potential security issues in our products. Similar to other companies, one of the ways we’ve made this part of our operating model is through a bug bounty program. The Intel® Bug Bounty Program was launched in March 2017 to incentivize security researchers to collaborate with us to find and report potential vulnerabilities. This, in turn, helps us strengthen the security of our products, while also enabling a responsible and coordinated disclosure process.

More: Security Exploits and Intel Products (Press Kit) | Security Research Findings (Intel.com)

Coordinated disclosure is widely regarded as the best way to responsibly protect customers from security exploits. It minimizes the risk that exploitable information becomes publicly known before mitigations are available. Working closely with our industry partners and our customers, we encourage responsible and coordinated disclosure to improve the likelihood that users will have solutions available when security issues are first published. Our Bug Bounty Program supports this objective by creating a process whereby the security research community can inform us, directly and in a timely fashion, about potential exploits that its members discover.

In support of our recent security-first pledge, we’ve made several updates to our program. We believe these changes will enable us to more broadly engage the security research community, and provide better incentives for coordinated response and disclosure that help protect our customers and their data.
Updates to our program include:

Shifting from an invitation-only program to a program that is open to all security researchers, significantly expanding the pool of eligible researchers.

Offering a new program focused specifically on side channel vulnerabilities through Dec. 31, 2018. The award for disclosures under this program is up to $250,000.

Raising bounty awards across the board, with awards of up to $100,000 for other areas.

More details on the program, including these new updates, can be found online on the Intel security site or our HackerOne page.

We will continue to evolve the program as needed to make it as effective as possible and to help us fulfill our security-first pledge. Thank you, in advance, to all of those across the industry who choose to participate.

Rick Echevarria,
Vice president and general manager of Platform Security at Intel Corporation.
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Source: https://newsroom.intel.com/news/expa...ounty-program/

Windows 10: New Intel Machine Programming Tool Detects Bugs in Code

New Intel Machine Programming Tool Detects Bugs in Code

New Intel Machine Programming Tool Detects Bugs in Code

New Intel Machine Programming Tool Detects Bugs in Code

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