The report, titled ‘The State of AI in Software Development,’ presents insights from over 1,000 global senior technology executives, developers, and security and operations professionals.

The report reveals a complex relationship between enthusiasm for AI adoption and concerns about data privacy, intellectual property, and security.

“Enterprises are seeking out platforms that allow them to harness the power of AI while addressing potential privacy and security risks,” said Alexander Johnston, Research Analyst in the Data, AI & Analytics Channel at 451 Research, a part of S&P Global Market Intelligence.

While 83 percent of the survey’s respondents view AI implementation as essential to stay competitive, a significant 79 percent expressed worries about AI tools accessing sensitive information and intellectual property.

Impact on developer productivity

AI is perceived as a boon for developer productivity, with 51 percent of all respondents citing it as a key benefit of AI implementation. However, security professionals are apprehensive that AI-generated code might lead to an increase in security vulnerabilities, potentially creating more work for them.

Only seven percent of developers’ time is currently spent identifying and mitigating security vulnerabilities, compared to 11 percent allocated to testing code. This raises questions about the widening gap between developers and security professionals in the AI era.

Privacy and intellectual property concerns

The survey underscores the paramount importance of data privacy and intellectual property protection when selecting AI tools. 95 percent of senior technology executives prioritise these aspects when choosing AI solutions.

Moreover, 32 percent of respondents admitted to being “very” or “extremely” concerned about introducing AI into the software development lifecycle. Within this group, 39 percent cited worries about AI-generated code introducing security vulnerabilities, and 48 percent expressed concerns that AI-generated code may not receive the same copyright protection as code produced by humans.

AI skills gap

Despite optimism about AI’s potential, the report identifies a disconnect between organisations’ provision of AI training resources and practitioners’ satisfaction with them. 

While 75 percent of respondents stated that their organisations offer training and resources for using AI, an equivalent proportion expressed the need to seek resources independently—suggesting that the available training may be insufficient.

A striking 81 percent of respondents said they require more training to effectively utilise AI in their daily work. Furthermore, 65 percent of those planning to use AI for software development indicated that their organsations plan to hire new talent to manage AI implementation.

David DeSanto, Chief Product Officer at GitLab, said:

“According to the GitLab Global DevSecOps Report, only 25 percent of developers’ time is spent on code generation, but the data shows AI can boost productivity and collaboration in nearly 60 percent of developers’ day-to-day work.

To realise AI’s full potential, it needs to be embedded across the software development lifecycle, allowing everyone involved in delivering secure software – not just developers – to benefit from the efficiency boost.” 

While AI holds immense promise for the software development industry, GitLab’s report makes it clear that addressing cybersecurity and privacy concerns, bridging the skills gap, and fostering collaboration between developers and security professionals are pivotal to successful AI adoption.

(Photo by Luca Bravo on Unsplash)

See also: UK government outlines AI Safety Summit plans

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Using the feature, developers can “brush” over their code to see it update in real-time.

Several different brushes are included to achieve various aims. For example, one brush makes code more readable—especially important when coding as part of a team or contributing to open-source projects.

Here are the other included brushes:

• Add types
• Fix bug
• Debug (adds debugging statements)
• Make robust (improves compatibility)

Code Brushes also supports the creation of custom brushes. One example is a brush to make a form “more accessible” automatically.

“As we explore enhancing developers’ workflows with machine learning, we’re focused on how to empower developers instead of automating them,” explained GitHub.

“This was one of many explorations we have in the works along those lines.”

Code Brushes is powered by the controversial GitHub Copilot. Copilot uses technology from OpenAI to help generate code and speed up software development.

GitHub-owner Microsoft and OpenAI were hit with a class-action lawsuit over Copilot last year. The case aims to investigate whether Copilot infringes on the rights of developers by scraping their code and not providing due attribution.

“Users likely face growing liability that only increases as Copilot improves,” explained Bradley M. Kuhn of Software Freedom Conservancy earlier this year.

“Users currently have no methods besides serendipity and educated guesses to know whether Copilot’s output is copyrighted by someone else.”

Code Brushes has been added to the Copilot Labs Visual Studio Code extension. The extension requires a Copilot license which costs $10/month or $100/year.

(Photo by Marcus Urbenz on Unsplash)

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The paper, titled ‘Do Users Write More Insecure Code with AI Assistants?’, examines developers’ use of AI coding assistants like the controversial GitHub Copilot.

“Participants with access to an AI assistant often produced more security vulnerabilities than those without access, with particularly significant results for string encryption and SQL injection,” the authors wrote.

The paper also found that developers using AI assistants have misguided confidence in the quality of their code.

“We also found that participants [that were] provided access to an AI assistant were more likely to believe that they wrote secure code than those without access to the AI assistant,” added the authors.

47 people were asked to write code in response to several prompts as part of the study. Some participants were given AI assistance while the rest did not.

The first prompt was to “Write two functions in Python where one encrypts and the other decrypts a given string using a given symmetric key.”

For that prompt, 79 percent of the coders without AI assistance gave a correct answer. That’s compared to 67 percent of the group with assistance.

In addition, the assisted group was determined to be “significantly more likely to provide an insecure solution (p < 0.05, using Welch’s unequal variances t-test), and also significantly more likely to use trivial ciphers, such as substitution ciphers (p < 0.01), and not conduct an authenticity check on the final returned value.”

One participant allegedly quipped that they hope AI assistance gets deployed because “it’s like [developer Q&A community] Stack Overflow but better, because it never tells you that your question was dumb.”

Last month, OpenAI and Microsoft were hit with a lawsuit over their GitHub Copilot assistant. Copilot is trained on “billions of lines of public code … written by others”.

The lawsuit alleges that Copilot infringes on the rights of developers by scraping their code and not providing due attribution. Developers that use code suggested by Copilot could unwittingly be infringing copyright.

“Copilot leaves copyleft compliance as an exercise for the user. Users likely face growing liability that only increases as Copilot improves,” wrote Bradley M. Kuhn of Software Freedom Conservancy earlier this year.

To summarise: Developers using current AI assistants risk producing buggier, less secure, and potentially litigable code.

(Photo by James Wainscoat on Unsplash)

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The company selected 10 contests that were hosted on Codeforces – a programming competition platform with thousands of participants – to evaluate the performance of AlphaCode.

Below is an example of a problem #AlphaCode can successfully solve, using the exact information seen on @codeforces, & the program that #AlphaCode writes.

By @liyuajia, David Choi, @junyoungch, @NateKushman, @Mononofu, @RemiLeblond, Tom Eccles, James Keeling, @FelixAxelGimeno 2/ pic.twitter.com/TJu0Va46OS

— DeepMind (@DeepMind) February 2, 2022

Following the simulations, AlphaCode ranked in the top 54 percent of competitors. That means it wasn’t yet able to beat leading human programmers but could rival the average.

Mike Mirzayanov, Founder of Codeforces, said:

“I can safely say the results of AlphaCode exceeded my expectations. I was sceptical because even in simple competitive problems it is often required not only to implement the algorithm, but also – and this is the most difficult part – to invent it.

AlphaCode managed to perform at the level of a promising new competitor. I can’t wait to see what lies ahead!”

AlphaCode uses transformer-based language models to generate code “at an unprecedented scale”. A preprint paper detailing AlphaCode is available here (PDF).

Petr Mitrichev, Software Engineer at Google, commented:

“Solving competitive programming problems is a really hard thing to do, requiring both good coding skills and problem-solving creativity in humans.

I was very impressed that AlphaCode could make progress in this area and excited to see how the model uses its statement understanding to produce code and guide its random exploration to create solutions.”

DeepMind has released its dataset of competitive programming problems and solutions on GitHub to help others build on their results.

Ian Funnell, Manager of Developer Relations at Matillion, said:

“Advancements like AlphaCode are welcomed and represent huge progress in designing algorithms more effectively. AI coding in general empowers developers to pursue innovation and creativity in setting the parameters and goals, leaving the AI to actually execute them.

Ultimately, this is a catalyst for innovation—helping humans rather than replacing them. Developers are extremely capable individuals, and businesses will continue to count on them to reap valuable insights from their data to differentiate and compete.”

DeepMind has set up an interactive site to view some of AlphaCode’s solutions and dive into the model at alphacode.deepmind.com.

(Image Credit: DeepMind)

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GitHub Copilot uses an AI system from OpenAI known as OpenAI Codex. The system claims to have a broad knowledge of how people use code and claims to be “significantly more capable than GPT-3” in generating code.

By drawing context from the code that a developer is working on, the system is able to suggest entire lines or functions.

Even veteran coders can benefit from GitHub Copilot by using the system to explore new APIs and discover alternative ways to solve problems without having to scour the web for answers.

GitHub Pilot supports a wide range of programming languages and frameworks but the company says the technical preview works best with Python, JavaScript, TypeScript, Ruby, and Go.

There are currently only a limited number of spots available for the technical preview.

Find out more about GitHub Copilot and how to get started here.

Find out more about Digital Transformation Week North America, taking place on November 9-10 2021, a virtual event and conference exploring advanced DTX strategies for a ‘digital everything’ world.

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Project CodeNet was announced at IBM’s Think conference this week and claims to be the largest open-source dataset for code (approximately 10 times the size of the closest.)

CodeNet features 500 million lines of code, 14 million examples, and spans 55 programming languages including Python, C++, Java, Go, COBOL, Pascal, and more.

Projects such as OpenAI’s GPT-3 are showing how AIs are becoming quite adept at penning the languages of us humans, but writing their own native code has been left to us. CodeNet aims to change that.

For at least the foreseeable future, projects like GPT-3 will be a tool for humans that can increase their productivity by providing a basic standard that will still require some editing to iron out errors and compensate for areas where humans still have an edge such as creativity, emotion, and compassion.

CodeNet will be similar, at least initially, in that it will lead to enhanced tools that help to speed up the writing and checking of code by humans by improving an AI’s own understanding of how to do such tasks.

“Given its wealth of programs written in a multitude of languages, we believe Project CodeNet can serve as a benchmark dataset for source-to-source translation and do for AI and code what the ImageNet dataset did years ago for computer vision,” says IBM.

US entrepreneur Marc Andreesen famously, and correctly, wrote in 2011 that “Software is eating the world”. Fast-forward to today and even cars now feature over 100 million lines of code (and growing rapidly, with the advent of autonomous vehicles.)

IBM says one of its large automotive clients recently approached the company to help update a $200 million asset consisting of 3,500, multi-generation Java files. These files contained over one million lines of code.

By applying its AI for Code stack, IBM reduced the client’s year-long ongoing code migration process down to just four weeks.

That example is sure to be the first of many in the years to come which have been greatly sped up, and improved, thanks to Project CodeNet.

You can find the full Project CodeNet dataset on GitHub here.

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The cloud-based solution aims to help accelerate the delivery of software in a rapidly-changing world while maintaining a high bar of quality.

Gareth Smith, CTO of Eggplant, said:

“The launch of our cloud platform is a significant milestone in our mission to rid the world of bad software. In our new normal, delivering speed and agility at scale has never been more critical.

Every business can easily tap into Eggplants’ AI-powered automation platform to accelerate the pace of delivery while ensuring a high-quality digital experience.” 

Enterprises have accelerated their shift to the cloud due to the pandemic and resulting increases in things such as home working.

Recent research from Centrify found that 51 percent of businesses which embraced a cloud-first model were able to handle the challenges presented by COVID-19 far more effectively.

Eggplant’s Digital Automation Intelligence (DAI) Platform features:

• Cloud-based end-to-end automation: The scalable fusion engine provides frictionless and efficient continuous and parallel end-to-end testing in the cloud, for any apps and websites, and on any target platforms. 
• Monitoring insights: The addition of advanced user experience (UX) data points and metrics, enables customers to benchmark their applications UX performance. These insights, added to the UX behaviour helps improve SEO. 
• Fully automated self-healing test assets: The use of AI identifies the tests needed and builds and runs them automatically, under full user control. These tests are self-healing, and automatically adapt as the system-under-test evolves.   

The solution helps to support the “citizen developer” movement—using AI to enable no-code/low-code development for people with minimal programming knowledge.

Both cloud and AI ranked highly in a recent study (PDF) by Deloitte of the most relevant technologies “to operate in the new normal”. Cloud and cybersecurity were joint first with 80 percent of respondents, followed by cognitive and AI tools (73%) and the IoT (65%).

Eggplant’s combination of AI and cloud technologies should help businesses to deal with COVID-19’s unique challenges and beyond.

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There is still fierce debate about the impact that artificial intelligence will have on jobs. Some believe that AI will replace many jobs and lead to the requirement of a Universal Basic Income (UBI), while others claim it will primarily offer assistance to help workers be more productive.

Dorsey is a respected technologist with a deep understanding of emerging technologies. Aside from creating Twitter, he also founded Square which is currently pushing the mass adoption of blockchain-based digital currencies such as Bitcoin and Ethereum.

Yang was seen as the presidential candidate for technologists before he suspended his campaign in February, with The New York Times calling him “The Internet’s Favorite Candidate” and his campaign was noted for its “tech-friendly” nature. The entrepreneur, lawyer, and philanthropist founded Venture for America, a non-profit which aimed to create jobs in cities most affected by the Great Recession. In March, Yang announced the creation of the Humanity Forward non-profit which is dedicated to promoting the ideas during his presidential campaign.

Jobs are now very much at threat once again; with the coronavirus wiping out all job gains since the Great Recession over a period of just four weeks. If emerging technologies such as AI do pose a risk to jobs, it could only compound the problem further.

In an episode of the Yang Speaks podcast, Dorsey warns that AI will pose a particular threat to entry-level programming jobs. However, even seasoned programmers will have their worth devalued.

“A lot of the goals of machine learning and deep learning is to write the software itself over time so a lot of entry-level programming jobs will just not be as relevant anymore,” Dorsey told Yang.

Yang is a proponent of a UBI. Dorsey said that such free cash payments could provide a “floor” for if people lose their jobs due to automation. Such free cash wouldn’t allow for luxurious items and holidays, but would ensure that people can keep a roof over their heads and food on the table.

UBI would provide workers with “peace of mind” that they can “feed their children while they are learning how to transition into this new world,” Dorsey explains.

Critics of UBI argue that such a permanent scheme would be expensive.

The UK is finding that out to some extent currently with its coronavirus furlough scheme. Under the scheme, the state will pay 80 percent of a worker’s salary to prevent job losses during the crisis. However, it’s costing approximately £14 billion per month and is expected to be wound down in the coming months due to being unsustainable.

However, some kind of UBI system is appearing increasingly needed.

In November, the Brookings Institute published a report (PDF) which highlights the risk AI poses to jobs. 

“Workers with graduate or professional degrees will be almost four times as exposed to AI as workers with just a high school degree. Holders of bachelor’s degrees will be the most exposed by education level, more than five times as exposed to AI than workers with just a high school degree,” the paper says.

In their analysis, the Brookings Institute ranked professions by their risk from AI exposure. Computer programmers ranked third, backing Dorsey’s prediction, just behind market research analysts and sales managers.

(Image Credit: Jack Dorsey by Thierry Ehrmann under CC BY 2.0 license)

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