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Author: Mike Vizard

  • Survey Surfaces AI Contest for the Hearts and Minds of DevOps Engineers

    Survey Surfaces AI Contest for the Hearts and Minds of DevOps Engineers

    A report published by the Futurum Group finds organizations are exploring multiple paths to employing artificial intelligence (AI) capabilities across the software development lifecycle (SDLC).

    Over the next 12 to 18 months organizations plan to increase spending on not only on AI code generation (83%) and agentic AI technologies (76%), but also existing familiar tools that have been augmented with AI, according to a survey of 855 IT leaders conducted by the Futurum Group.

    While many DevOps teams will undoubtedly employ a mix of these approaches, it’s clear there is a battle underway for the hearts and minds of software engineers, said Mitch Ashley, vice president and practice lead for DevOps and application development at the Futurum Group.

    Providers of application development tools and platforms have reached a pivotal AI moment as DevOps teams weigh the merits of switching platforms versus relying on incumbent vendors to bolt on additional AI capabilities, he added. Ultimately, success will hinge on lowering barriers to entry, go-to-market models and demonstrating tangible value at a time when the way software is being created is fundamentally changing, noted Ashley.

    It’s still early days so far as adoption of AI is concerned but application development teams are clearly at the forefront. Previously, research from the Futurum Group revealed that 41% of the respondents it surveyed expect generative AI tools and platforms will be used to generate, review and test code.

    Less clear is how much faith application development teams will have in those tools and platforms. It’s one thing to rely on a generative AI tool to explain how an existing code base is structured, but the quality of the code that might be generated can vary substantially. In some instances, such as generating a script for invoking an application programming interface (API), the quality of the code might not matter. However, the quality of the code being used to create business logic is critical, especially if that code might contain known vulnerabilities that were never properly scanned.

    Of course, depending on the experience of the application developer, there are also plenty of instances where the code being generated might be better than the code that a human developer might have created on their own. As a result, the return on investment in AI tools and platforms will tend to vary widely from one DevOps team to the next.

    Ultimately, advances in AI should bring about more positive than negative changes, especially as more AI agents that have been narrowly trained to automate a specific set of tasks become more widely employed. The simple fact is that there are still far too many manual tasks that make building and deploying software more tedious than anyone really enjoys, which only leads to higher turnover rates as software engineers inevitably become burnt out.

    Conversely, however, DevOps teams should also be wary of becoming overly dependent on AI. After all, the one golden rule of application development that still applies in the age of AI is that no code should be deployed in a production environment that one or more humans don’t thoroughly understand.

  • Survey: Software Quality Continues to Be Sacrificed in the Name of Speed

    Survey: Software Quality Continues to Be Sacrificed in the Name of Speed

    A global survey of 2,750 IT, DevOps leaders and software developers finds nearly half of respondents (45%) admit their organization prioritizes delivery speed over software quality, with 63% acknowledging their organization deploys code without fully testing it. Only 13% of respondents said their organization is focused on improving software quality.

    Conducted by Censuswide on behalf of Tricentis, a provider of an application testing automation platform, more than one-third (34%) of respondents say ongoing maintenance and unresolved technical debt are the largest obstacles to delivering high-quality software. Other issues include poor communication between software development and quality assurance teams (33%) and a disconnect between leadership and development teams (29%).

    The primary reasons cited for not testing code are the need to expedite release cycles (46%) and accidental slips (40%). Just under a quarter (24%) also admitted they are not confident in their test coverage.

    More troubling still, 81% said software quality issues cost their business between $500,000 and $5 million every year, with nearly 40% estimating losses of more than $1 million. Two-thirds (66%) of respondents also conceded their organization is likely to suffer an outage or major disruption in the next year, with nearly a quarter (23%) deeming their organization to be at extreme risk.

    Many respondents noted there is a lack of clear quality metrics provided by the organization (29%), a disconnect between leadership and development teams over what defines quality (28%), and budget constraints (25%). The top five benefits they seek from better software quality include improved team efficiency (17%), stronger customer satisfaction and retention (15%), reduced security and compliance risk (14%), increased revenue (14%), and faster delivery (14%).

    Lee McClendon, chief digital and technology officer for Tricentis, said when it comes to software engineering there continues to be too much emphasis on speed over quality. The irony is that addressing quality issues in the latter stages of a DevOps workflow only serves to slow organizations down further when work needs to be redone, he added.

    There is simply too much focus on metrics that track how fast software is developed versus how well, noted McClendon.

    On the plus side, a total of 80% of respondents said they believe that advances in artificial intelligence (AI) will enable them to improve software quality without adversely impacting the rate at which software is being developed. Nearly 90% also said they can effectively measure the return on investment on generative AI tools and platforms. However, two-thirds (66%) noted it takes investments in software development and delivery at least three years to pay off, with over one-third (34%) anticipating AI technology investments will take even longer.

    The majority (82%) also said they are excited about AI agents taking on repetitive tasks in the software development lifecycle, while 84% believe AI will help teams deliver under increasingly tight deadlines. A full 89% said they trust AI agents to make decisions that impact software delivery. At the same time, bias remains a key concern for 38% of respondents, and the same percentage are wary of over-reliance on AI tools.

    Finally, nearly all respondents expect autonomous testing will be useful for quality assurance (QA) testing, with the biggest impact being improving software performance (28%), overall quality (28%), analyzing test results (25%) and test case maintenance (23%).

    Hopefully, the quality of applications will improve in the age of AI, but the one thing that is certain is that when it comes to achieving that goal there is still a lot of room for improvement.