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Category: AI

  • Is ChatGPT Helping or Hurting Your Developers’ Productivity?

    Is ChatGPT Helping or Hurting Your Developers’ Productivity?

    ChatGPT’s ability to translate natural language into working code has sparked tremendous interest in the programming community. Developers are exploring ways to use ChatGPT to reduce redundant tasks, from creating code snippets to analyzing and debugging programs. This also allows them to focus on more complex tasks like data analysis and monitoring with the help of a strong full-stack observability platform. Despite ChatGPT’s impressive capabilities, developers and their employers are split on whether the AI chatbot significantly boosts developer productivity.

    We will explore how to use ChatGPT to benefit your developers’ productivity and go over the advantages and disadvantages of ChatGPT to help you make the right decision for your business.

    How to use ChatGPT for Better Productivity

    ChatGPT has quickly become the most popular intelligence chatbot, with over a million user signups in its first five days and over 100 million in the first two months. Here are a few ways ChatGPT, developed by OpenAI, can help boost productivity in your organization.

    Improve Their Code
    Sometimes manually written code snippets consume additional CPU resources. Using ChatGPT, developers can optimize their functions for better performance. Additionally, ChatGPT can also write test cases and spot security vulnerabilities in their code, helping developers work faster.

    Simplify Documentation
    ChatGPT can give a simple explanation for the layman, avoiding technical jargon to a large extent. For instance, a developer at Twitter fed ChatGPT their code and it responded with a pretty detailed explanation. Using ChatGPT to generate the first draft of a company’s internal documentation would make knowledge transfers easier. New hires could quickly get up to speed on the company’s codebase. Further, ChatGPT could also be used for external-facing documents where other developers can learn how to use the company’s APIs and frameworks.

    Test and Understand Code
    ChatGPT can write automated tests for code that is fed into it. Although many developers consider testing boring and stressful—and sometimes a waste of time—it’s a critical step in ensuring that published applications are bug-free.

    With ChatGPT, developers can output reliable, well-tested code much more quickly and easily. ChatGPT can also give high-level and line-by-line explanations of code and even teach developers how to use external modules and APIs in their own code, helping them build much faster.

    Debug Code
    ChatGPT makes a great debugging tool for developers. It can take in error messages and output information on the likely cause of the error and how to fix it. This is typically a lot more time efficient than browsing forums and blogs for a solution.

    The tool can also suggest helpful code alternatives. Many developers claimed that ChatGPT can be much faster and less mentally challenging than the conventional approach of searching the web for a solution.

    As the CEO of Replit, Amjad Masad, put it, “ChatGPT could be a good debugging companion; it not only explains the bug but fixes it and explains the fix.”

    Code Comprehension
    ChatGPT’s terabytes of training data come from various sources, including common crawl and WebText2. This training data makes it easy for ChatGPT to simplify explanations of complex pieces of code for developers. Therefore, developers can save time deciphering any code’s underlying logic and structure.

    For instance, Stanford instructor and well-known developer Matt Harrison used ChatGPT to build a simple graphic design tool. He explained, “I’m using ChatGPT to create a GUI program for drawing lines. I haven’t used QT [a development framework] in years, yet I can throw this together really quickly.”

    Generate Documentation
    Writing code documentation is often considered a mundane task by developers. With ChatGPT, developers can significantly cut down on the amount of time needed for documentation. For instance, they can expedite the creation of troubleshooting guides, tool user manuals and test cases by asking ChatGPT to perform code analysis and generate detailed technical reports. Accelerating this documentation process allows for easier onboarding of new developers and faster product release times.

    Writing Tests
    ChatGPT can analyze code snippets and automate testing for them, saving developers lots of time and helping them output reliable code. Test coverage is an essential metric for most enterprises, and ChatGPT could help streamline the process.

    ChatGPT Disadvantages

    While many developers have praised ChatGPT for increasing developer productivity, others have criticized it as unreliable. Let’s understand some key areas where the tool falls behind.

    Outputting Buggy or Unusable Code
    Developers have complained that the chatbot would sometimes output plausible-looking code that doesn’t work. This might result from misunderstanding the user’s requirements or simply being unable to come up with a correct answer—then faking it.

    Programming Q&A forum Stack Overflow even issued a temporary ban on ChatGPT-generated answers. The ban was because the chatbot generated an influx of answers whose average accuracy was pretty low. ChatGPT sometimes uses nonexistent commands and outdated modules in its code, so while they theoretically might make sense, they don’t work in practice.

    Writing Outdated Code (and Forgetting Previously Written Code)
    Since ChatGPT’s training data cut off in September 2021, the chatbot has no knowledge of the recent developments in programming languages and modules after that. As a result, ChatGPT often needs to use outdated commands, modules and paradigms in its code.

    ChatGPT also occasionally forgets the context within which it is working, affecting code output. Some users have reported that ChatGPT sometimes forgets about the task at hand and begins generating incompatible code.

    Security Concerns
    Security concerns are the biggest drawback to having developers work with ChatGPT. Open AI uses the data users input into ChatGPT to train the underlying AI model.

    Research by cybersecurity firm Cyberhaven has shown that 11% of the data employees input into ChatGPT is confidential. This presents a significant risk, as such data can be used in the chatbot’s next responses. Corporations like JPMorgan and Verizon have even gone as far as to block employees’ access to ChatGPT because of these concerns.

    Lacks Problem-Solving Skills
    A developer’s job is to understand the business problem and develop customized solutions to address it. Although AI/ML tools like ChatGPT help developers code faster, they can’t help in problem-solving or implementing a software application.

    Furthermore, when developers become overly reliant on AI-generated code, they’re discouraged from upskilling on new programming frameworks and languages. This can hurt the organization in ways that might not be evident immediately.

    To ChatGPT or not to ChatGPT

    As Google’s head of developer experience Bret McGowen said, “While AI will transform software development in many ways, human judgment, intuition and creativity will remain essential to building great products.”

    While ChatGPT may not be a perfect solution for every aspect of software development, it can be a useful addition when combined with human problem-solving skills. However, companies should be careful while allowing the usage of AI in day-to-day processes. Guidelines educating in-house developers on the fair usage of AI are a must to protect sensitive information and maintain a high-quality control process.

  • Forking RHEL! Oracle and SUSE Join the Fight ¦ Silverman Sues AI Firms

    Forking RHEL! Oracle and SUSE Join the Fight ¦ Silverman Sues AI Firms

    Welcome to The Long View—where we peruse the news of the week and strip it to the essentials. Let’s work out what really matters.

    This week: SUSE announces a Red Hat Enterprise Linux fork after Oracle said a similar thing, and Sarah Silverman says OpenAI and Meta have stolen her words. (more…)

  • Are LLMs Leading DevOps Into a Tech Debt Trap?

    Are LLMs Leading DevOps Into a Tech Debt Trap?

    Everyone thought AI would free us from toil. Unfortunately, that’s not true. What it will do, however, is lead to an explosion of technical debt. Some of the most alluring uses of LLMs have the potential to lead to some of the worst outcomes for enterprises and their DevOps and platform engineering teams.

    SK Ventures and Adrian Cockcroft raised the alarm bells in their warning posts about the tipping points LLM introduced into work and society. And in my own Gluecon 2023 presentation, I challenged just how powerful and disruptive the new generation of AI/ML will be for ITOps and DevOps efforts.

    Working Faster is not Working Smarter

    There is no doubt that LLMs are powerful and will change how we think about expertise and knowledge work.  While they are not truly innovating, their ability to draw on such a vast pool of human expertise makes it difficult to distinguish from true creative work. Since they are unbounded, they will cheerfully use the knowledge to churn out terabytes of functionally correct but bespoke code.

    It’s easy to imagine a future where LLMs crank out DevOps scripts 10x faster. We will be supercharging our ability to produce complex, untested automation at a pace never seen before! On the surface, this seems like a huge productivity boost because we (mistakenly) see our job as focused on producing scripts instead of working systems.

    But we already have an overabundance of duplicated and difficult-to-support automation. This ever-expanding surface of technical debt is one of the major reasons that ITOps teams are mired in complexity and are forever underwater. Generating even more scripts, even if they were better written, does not reverse the trend.

    Can AI/ML Avoid This Technical Debt Trap?

    It depends on how we use the expertise in the models. Instead of asking it to generate new code, we could ask it to interpret and modify existing code. For the first time, we have tools to take down the “not invented here” barriers we’ve created because of the high cognitive load of understanding code. If we can help people work more effectively with existing code, then we can actually converge and reuse our systems. By helping us expand and operate within our working systems base, LLMs could actually help us maintain less code.

    Imagine if the teams in your organization were invested in collaborating around shared systems! We haven’t done this well today because it takes significantly more time and effort. Today, LLMs have thrown out those calculations.

    Taking this just one more step, we can see how improved reuse paves the way for reduction of the number of architectural patterns. If we improve our collaboration and investment in sharing code, then there is increased ROI in making shared patterns and platforms work. I see that as a tremendous opportunity for LLMs to improve operations in a meaningful way.

    It comes down to whether we ignore complexity by replacing it or embrace complexity by understanding it. I see this as a choice between independence or collaboration, as illustrated by the table below.

    LLM Driving Independence

    LLM Driving Collaboration

    Create new code

    Interpret existing code

    Replace existing code

    Maintain existing code

    Design new architectures

    Be a guide and mentor

    Build new stacks

    Evaluate alternatives

    Bypass documentation

    Improve documentation

    Can AI Help us Collaborate?

    We keep taking the same hardware and software components and then building unique systems. That variation makes it impossible to leverage each other’s work. At its heart, the reusability problem lies in the difficulty of understanding systems, and that makes it nearly impossible for us to work together, collaborate, reuse and accelerate.

    Breaking this cycle has been our mission at RackN for nearly a decade. We’ve seen tremendous ROI from availability, reliability and speed when customers are able to leverage standardized infrastructure pipelines. We’ve seen firsthand that it is possible to guide teams into collaboration with sufficient guidance, mentoring and training. Collaboration creates 10x outcomes even without AI, but it takes building expertise, deliberate effort and leadership discipline to implement.

    Why does collaboration create 10x ROI?  It works because true innovation actually comes from understanding and building on top of systems rather than rebuilding them from scratch over and over again. The key is being able to leverage LLMs to better understand existing automation and platforms instead of creating yet another bespoke stack.

    Enterprises Should use LLMs to Drive Collaboration

    I believe that the most effective enterprises will use these tools to elevate their IT leaders and architects to focus on collaboration and reuse. They must actually direct them to produce less bespoke automation; instead, they can use expertise in these tools to find and maintain existing systems. LLM presents options, but collaboration requires new thinking and strong leadership.

  • BigPanda Extends AIOps Platform to Embrace Generative AI

    BigPanda Extends AIOps Platform to Embrace Generative AI

    BigPanda today added generative artificial intelligence (AI) capabilities to analyze incidents surfaced by its AI for IT operations (AIOps) platform.

    Blair Sibille, field CTO for BigPanda, said Generative AI for Automated Incident Analysis leverages generative AI platforms based on the general-purpose large language models (LLMs) created by OpenAI.

    The goal is to make it simpler for IT teams to discern the root cause of an issue by, for example, taking advantage of summaries of alerts enabled by generative AI to make it simpler for IT professionals of any skill level to understand what requires their most immediate attention, he added. Today, insights are lost in the sea of alerts that often inundate IT teams, Sibille noted.

    BigPanda has been at the forefront of applying machine learning algorithms to automate IT operations management. Now, the company is incorporating external general-purpose LLMs to further augment IT teams, said Sibille.

    As IT environments continue to be more complex, it’s becoming essentially impossible for IT teams to manage them at scale without relying more on automation. AI technologies take automation to another level, but they won’t eliminate the need for DevOps teams to manage IT environments, added Sibille.

    BigPanda claimed generative AI capabilities will make it possible to reduce the amount of time required to resolve tickets by seven minutes each, with an accuracy rate of 95% during beta testing. Over time, those capabilities not only reduce mean-time-to-resolution but also reduce the level of toil that often conspires to burn out DevOps teams, said Sibille.

    At the core of the BigPanda AIOps platform is a data lake capable of consuming data generated by multiple applications and systems. That data then provides the foundation on which machine learning algorithms are applied to surface insights.

    It’s not clear how much generative AI will automate DevOps workflows, but in effect, more IT teams will be able to take advantage of having the equivalent of a DevOps engineer that they can query any time an issue arises using natural language, noted Sibille.

    AI doesn’t mean that IT outages will be completely eliminated, but it does mean they will become a lot less stressful, he added.

    Naturally, there’s a lot of speculation concerning the impact AI will have on DevOps. Some contend it’s only a matter of time before the entire software engineering process is automated. As those advances are made, the cost of building applications will effectively drop to zero. Others argue that AI will simply augment DevOps teams in a way that makes it simpler to manage IT at scale using either the same number or possibly even fewer engineers.

    The one thing that is clear is that AI will soon be pervasive. DevOps teams should start evaluating today which tasks will be automated with an eye toward planning how DevOps roles will evolve as AI advances accelerate.

  • Key Insights From the Stack Overflow 2023 Developer Survey

    Key Insights From the Stack Overflow 2023 Developer Survey

    Stack Overflow has released its seminal yearly developer survey. The 2023 report, which surveyed over 90,000 developers, provides a comprehensive view of the current software developer experience.

    Below, I’ll highlight a few important findings, namely the significant programming language and tooling preferences, the use of AI in the development workflow and what these trends could mean for the DevOps field. I also gathered insights from Joy Liuzzo, vice president of product marketing at Stack Overflow, to dive deeper into some of the findings.

    Developer Technology Preferences in 2023

    Of note is that more and more developers are learning to code online. Learning to code using online resources increased from 70% in 2022 to 80% in 2023. Although many developers (47%) still have a Bachelor’s degree in computer science or the equivalent, these trends underscore the movement toward alternative knowledge solutions, especially for younger programmers. The topmost online resources include technical documentation, blogs, forums and how-to videos.

    JavaScript is still king—it’s now in its eleventh year as the most common programming language. But it’s worth highlighting that Python has overtaken SQL as the third top language. “SQL was solidly in the top three (JavaScript, HTML/CSS, SQL) since 2015, so it’s a big deal for it to have fallen below Python,” said Liuzzo. “We’ve seen Python’s popularity increase based on the number of questions asked on our public site, so we’ve been expecting some shifts.”

    TypeScript and Bash/Shell have also consistently grown in usage over the last few years. “These two languages are involved with the functions of other popular programming languages, explaining their prevalence among programmers,” Liuzzo said.

    PostgreSQL also overtook MySQL as the most common database type. In terms of web frameworks, Node.js and React.js are the most dominant. Other frameworks, like jQuery and ASP.NET, are showing signs of going out of style, likely because they are older web frameworks.

    Spotlight on AI

    The explosion of new AI innovations, such as large language models (LLMs) and chat-driven generative AI tools, had a major impact on this year’s technology findings. In fact, 83% of respondents have used ChatGPT over the past year. This is followed by Bing AI (20.6%), WolframAlpha (13.36%) and Google Bard AI (9.86%). GitHub Copilot was ranked as the top most-used AI developer tool.

    There was a significant spike in using AI tools amongst those learning to code—these folks often see benefits like speeding up learning, increasing productivity and greater efficiency. “Pair this with how they are currently using AI tools to debug and get help (68%) and learn about a codebase (50%), and we see that the commonality is that AI tools enable learning,” explained Liuzzo.

    That being said, there’s skepticism around AI’s accuracy, and only 13% see improved accuracy in coding as a benefit of using such tools. These pain points may resolve as LLMs evolve, but for now, there still needs to be human judgment to catch bugs and avoid hallucinations.

    “AI has a complexity cliff. Much like Helmsman’s complexity cliff found in project management, after a certain point, the ability for AI to handle all the nuances and interdependencies of a solution drops off,” said Liuzzo. “That’s when humans, their ability to apply judgment and have original thoughts, saves the day.”

    So what are some takeaways for those working in the DevOps field, namely DevOps tooling providers and platform engineers? Well, new tooling preferences and habits will continue to evolve in the DevOps field in new ways. Here are some potential examples:

    • More Markdown-based collaboration. Markdown is the most admired asynchronous tool and has increased in popularity this year. New programmers are adopting a blend of Markdown, GitHub Discussions and Notion for their asynchronous tooling. Flavors of Markdown are already being used for comments, merge requests, issues and more.
    • Continued Docker reliance. Docker remains the top-used tool, followed by npm and Pip. Despite the rise of managed container services, Docker is still a popular way to package and distribute software.
    • Challengers to the big three loom in the distance. Amazon Web Services (AWS) is still the most-used cloud platform at 48.62%. It’s followed by Microsoft Azure (26.03%) and Google Cloud (23.86%). Yet, newer developers want to work more with Hetzner and Vercel.
    • Online learning is paramount to knowledge sharing. Given the steady increase in new programmers and their reliance on online resources rather than traditional schooling, DevOps and open source communities should seek to enhance their online footprint by making knowledge like tooling documentation and tutorials easily consumable.

    Last but not least, generative AI is set to enhance programming in many areas and is already becoming embedded into many environments. Just as AI can be used for code generation and debugging, LLMs will likely assist the DevOps side of the equation, helping automate the CI/CD pipeline, optimize infrastructure, detect anomalies and provide more chat interfaces to initiate these capabilities.

    “AI tools are empowering and enabling learning,” said Liuzzo. “Generative AI will democratize coding and grow the developer community by several folds—and that growing number of developers will be the ones using the tools and also verifying and validating the outputs as they learn—bringing the power of the developer community and the technological power of AI together.”

  • Threads: Twitter Killer or Ad-Infested Hellscape?

    Threads: Twitter Killer or Ad-Infested Hellscape?

    Welcome to The Long View—where we peruse the news of the week and strip it to the essentials. Let’s work out what really matters.

    This week: Meta launches its much-leaked Threads microblogging app. A spinoff from Instagram, it’s already reached 30 million signups at the time of writing—even before it’s available in the EU. But how is Meta’s DevOps team making it scale? (more…)

  • Tabnine Unveils Beta of Generative AI Automated Testing Tool

    Tabnine Unveils Beta of Generative AI Automated Testing Tool

    Tabnine this week made available in beta a tool that enables developers to use natural language to interact with the artificial intelligence (AI) models embedded within its test automation platform.

    Brandon Jung, vice president of ecosystems for Tabnine, said Tabnine Chat extends generative AI capabilities the company developed to make it simpler for developers to invoke its test automation platform from within their integrated development environment (IDE).

    In addition to making it possible to search a codebase more efficiently, that capability can also be used to explain how a piece of code functions, said Jung.

    Developers can also generate new code via a natural language interface using Tabnine Chat in the same way other generative AI tools do, he added.

    In addition to developing its own LLMs, Tabnine previously announced it will be integrating LLMs provided by Google as part of a strategy to mix and match LLMs regardless of where they are hosted.

    Unlike general-purpose LLMs, the ones used by Tabnine were trained using a narrower base of testing code that the company curated using tests made freely available on the internet. No customer data was used to create any of those tests, so developers do not need to worry their code may inadvertently be accessed by anyone else, noted Jung.

    In general, as AI makes test creation simpler, tests will be run more frequently, said Jung. Over time, those AI capabilities and more frequent tests should significantly improve the overall quality of applications running in production environments, he added.

    It’s now only a matter of time before generative AI is applied across the entire software development life cycle (SDLC). Testing, however, may be one area where it will have the earliest impact.

    In the meantime, the debate over the impact generative AI will have on DevOps continues. Some proponents contend most of the software engineering process is about to become automated, while others suggest there will always be a need for a human to be in the loop of any process automated using AI models. Much of the current level of toil associated with DevOps today will be reduced, but the need for software engineers to manage the overall process will remain, they contend.

    The one thing that is certain is that the pace at which applications are built and deployed is about to be greatly accelerated. Prior to rise of generative AI, testing processes were barely keeping pace, so as more code is created with the help of AI, the need for similar advances in testing will become acute.

    There is as much irrational exuberance about AI as there is fear and loathing, but DevOps teams that committed early on to ruthless automation will naturally be at the forefront of adoption. The immediate challenge is assessing what functions will be automated and the impact those changes will have on the way DevOps teams are currently structured. Ultimately, the goal is to eliminate as many of the bottlenecks that conspire to slow application development and deployment while simultaneously improving the quality of the application experience.

  • Applitools Acquires Preflight to Further Apply AI to App Testing

    Applitools Acquires Preflight to Further Apply AI to App Testing

    Applitools this week revealed it has acquired Preflight, which provides a low-code automation platform infused with machine learning algorithms that makes it possible for developers to write their own tests.

    Applitools COO Moshe Milman said Preflight extends the company’s efforts to leverage artificial intelligence (AI) to shift more testing capabilities left toward application developers using its existing test automation platform. The Applitools platform is compatible with frameworks such as Selenium, Playwright and Cypress.

    One of the major challenges organizations encounter as they encourage developers to run more tests is that many of them don’t have the skills required to write them. Applitools’ AI capabilities reduce the cognitive load required to create and run tests, noted Milman.

    The goal isn’t so much to replace dedicated application testing teams but to enable developers to more easily test code as they build it, noted Milman. There will always be a need for dedicated testing teams to evaluate more complex use cases for applications, he added.

    The issue is, thanks to advances in DevOps automation, the pace of application development is outpacing the testing resources available, said Milman. IT teams need to invest in test automation to close what has become a quality gap because not enough code is being thoroughly tested, he noted.

    Test automation also serves to reduce the total cost of creating tests. That enables organizations to conduct more tests without slowing down application development, noted Milman.

    In the longer term, Applitools also plans to leverage large language models (LLMs) to provide generative AI capabilities alongside existing support for machine learning algorithms to further automate testing, noted Milman. Applitools is already making use of computer vision to automate app testing. Preflight adds a set of tools infused with AI to create tests.

    The reasons behind inadequate application testing aren’t entirely clear. Whether it’s because tests are too difficult to create or development teams simply run out of time because of scheduling delays, the impact is the same. Too often, developers convince themselves they will address any issues that might arise with the next update only to become distracted by other priorities. Regardless of the reason, however, there are more poorly-tested applications running in production environments than many DevOps teams would prefer. In fact, much of the technical debt IT organizations need to manage today can be traced back to inadequate testing.

    However, as testing tools become more automated, the overall quality of applications should improve and technical debt may become less of an issue. In the meantime, automated testing promises to improve overall developer productivity as it becomes simpler for developers to create code that has been, at the very least, vetted by algorithms before being added to a codebase. In effect, developers are running out of excuses for not testing applications before delivering them to unsuspecting end users.

  • Mendix Embraces DevOps for Next Era of Low-Code App Development

    Mendix Embraces DevOps for Next Era of Low-Code App Development

    Mendix, a subsidiary of Siemens, today updated its namesake low-code application development platform to add support for DevOps workflows and make it simpler for developers to compose applications and embed artificial intelligence (AI) capabilities.

    Sheryl Koenigsberg, vice president of product marketing for Mendix, said Mendix 10 makes it possible to use Git repositories for version control along with Mendix Pipelines, currently in beta. Mendix Pipelines is a continuous integration/continuous delivery (CI/CD) environment that will be embedded in the low-code platform. In addition, Mendix is adding support for Webhooks that can be used to integrate Mendix with existing CI/CD platforms.

    Mendix has also added a Solutions Kit, now generally available, to make it possible to customize applications in a way that ensures those changes will be preserved as application environments are upgraded.

    There is also now a Portfolio Manager tool to track projects and a connector for integration with the Jira project management software from Atlassian.

    Later this year, Mendix will also make generally available Adaptation Insights to provide insights into application extensions.

    In the future, developers will be able to leverage an extensibility framework to tailor the Mendix Studio Pro integrated development environment (IDE) by adding their own plugins, wizards, editors and third-party integrations. Later this year, Mendix 10 will also add, in beta, a version of its Studio Pro IDE for MacOS.

    The Mendix approach to composability is also being extended to incorporate AI. The company is adding Mendix Assist, a bot that generates validation logic for data entry. This module is the second AI capability that Mendix has developed after previously previewing Mendix Chat, a tool based on a large language model created using Mendix data that will be integrated into the Mendix integrated development environment (IDE) later this year. Mendix Assist will be incorporated into Mendix Chat.

    Mendix is also developing additional LLMs for creating applications and will as part of its commitment to composability plans to make it possible to integrate third-party AI services and LLMs into the Mendix Runtime to create “smart apps.” AI models will be converted to the Open Neural Network Exchange (ONNX) format, an open standard for machine learning models, to provide that capability.

    In addition, Mendix is adding support for event-driven architectures via Business Events and an Event Broker, to build more complex applications.

    Other capabilities being added to Mendix 10 include updated REST and database connectors, support for visual data filters, rich properties and design controls, a Start from Spreadsheet tool, end user survey tools, a PDF document generator and a revamped set of governance and risk assessment tools.

    Finally, Mendix is adding a Terraform module to make it simpler to deploy its low-code platform on the Amazon Elastic Kubernetes Service (Amazon EKS) in addition to providing a reference implementation for building private clouds to develop applications using the Mendix Cloud service.

    As AI-infused low-code applications are developed faster, it’s not clear to what degree organizations are prepared to embrace the inevitable disruption that will ensue. However, the one thing that is clear is that the DevOps workflows used to deploy these applications at scale will soon be put to the ultimate test.

  • OutSystems Embraces Generative AI to Speed App Dev

    OutSystems Embraces Generative AI to Speed App Dev

    OutSystems, via a Project Morpheus initiative, intends to leverage generative artificial intelligence (AI) technologies to make it possible to automatically generate an application that developers will then use low-code tools to customize for specific use cases.

    In the meantime, the company is extending an existing alliance with Microsoft to provide a connector that integrates its namesake low-code platform with instances of ChatGPT. These instances are made available via Microsoft’s Azure OpenAI cloud service to make it possible to build a low-code application in a matter of minutes.

    Rodrigo Coutinho, director of data science for OutSystems, said that as generative AI continues to evolve, it will become possible for developers to use a natural language interface to create an application. However, there will also be plenty of instances where end users still prefer to access applications via a graphical user interface that developers build using low-code platforms, he noted.

    An application editor will also provide ongoing suggestions for everything from how the user interface is designed to the way data is managed.

    Developers will also be able to verify how code functions using the OutSystems visual language. In addition, The OutSystem compiler will be able to deterministically detect threats and code patterns produced by generative AI platforms that will include Google, Amazon and others alongside Microsoft.

    Ultimately, citizen and junior developers will be able to build sophisticated applications that previously would have required the skills of a professional developer, noted Coutinho.

    It’s not clear how much developers will embrace a low-code platform to build software. However, the one thing that is certain is the whole process of defining requirements before an application can be built will soon become obsolete as the building of an application becomes more iterative and involves developers working alongside subject matter experts, added Coutinho.

    It may awhile before that vision of application development is realized, but the pace at which applications are built and deployed is clearly about to accelerate. The impact of all that code will naturally vary, but it’s already apparent that the cost of building many applications may soon drop to almost zero. Developers will soon be spending more time on making sure workflows are logical than writing actual code, noted Coutinho.

    In addition to relying more on AI to build software, organizations should also rely more on AI to deploy it at unprecedented levels of scale. The days when application development projects were measured in months, sometimes years, is coming to an end. AI platforms will enable organizations to experiment at will because the time and effort required to build and deploy a new iteration of an application will be virtually nil.

    Of course, no one can predict what impact all that code will have on society as we know it. But the only limit going forward may soon be what our collective imaginations can conceive than the level of developer expertise that can be marshalled at any given time.