DEVOPSdigest

As part of DEVOPSdigest's annual list of DevOps predictions, industry experts — from analysts and consultants to the top vendors — offer thoughtful, insightful, and often controversial predictions on how DevOps and development technologies will evolve in 2025. Part 3 covers predictions about software quality and testing, as well as some challenges developers will face in 2025.

SOFTWARE AS A CRITICAL ASSET

Next year, we will see more executives and boards of directors put "software as a critical business asset" to the top of their agenda. When bad code costs organizations $2.41 trillion in the US alone, it shouldn't be a question anymore of how important software is to business, but how do we ensure it is a competitive differentiator and doesn't put our business at risk? Organizations strive to protect their codebase against risks, yet often, the focus on code security tends to emerge later in the development lifecycle rather than as an initial investment in secure-by-design practices. I believe we will see the C-suite mindset shift to see software in a new strategic light and build software quality into the fabric of the way business is done. Especially as AI-generated software development continues to pick up steam, it is the responsibility of CEOs and boards to put mechanisms in place that uphold and maintain code quality and security during development. The future of digital business depends on it.
Tariq Shaukat
CEO, Sonar

IMPROVED CODE QUALITY

Code generation will improve in quality as developers learn how to create better prompts and provide additional documentation to ensure compliance with corporate coding and architectural standards. The reliance on AI will make it even more critical to establish automated compliance and security processes in the process.
David Brooks
SVP of Evangelism, Copado

QUALITY VS. SPEED

In 2025, we will start to see the fallout of prioritizing speed over quality. Constantly delivering new features may temporarily garner user interest, but it's ultimately quality that makes them stay. Unfortunately, we're going to start to see a noticeable decline in the quality of digital experiences hitting the market, as a result of many years of misplaced focus on speed of delivery over digital quality. Though many organizations didn't see a choice given the pace of competitive innovation, 2025 will reveal the costly impact of rushing QA. Next year, the tide will turn in favor of those companies that prioritize usability, accessibility, localization and other testing requirements over those that prioritize novelty.
Rob Mason
CTO, Applause

QUALITY VS. ARCHITECTURAL TRENDS

Software success will hinge on quality, not architectural trends. 2025 will bring a fundamental shift in how organizations think about software architecture. You can have good software that is microservices-based, and you can have really bad software that's microservices-based. You can have really bad software that is a monolith and excellent software running in a monolith. It's not about the architectural patterns, it's about building good software.
Amir Rapson
CTO, CCSO and Co-Founder, vFunction

AUTOMATED TESTING

Testing will become a strategic priority for IT leaders, with automation being critical for success. Engineering leaders are finally starting to recognize that testing is fundamental to delivering quality software. Particularly as AI-driven development becomes more widely adopted, automated testing will be imperative and provide a competitive advantage.
Trisha Gee
Lead Developer Advocate, Gradle

ACCESSIBILITY TESTING

There will be a major shift toward accessibility testing, particularly in Europe where people will scramble to stay ahead of the new regulations. There will be a small panic and a bunch of activity around mid-year, and there's a risk of a few charlatans emerging alongside the real solutions. One or two companies will experience seismic growth as providers in this area.
Marcus Merrell
Principal Test Strategist, Sauce Labs

TOOLCHAIN OBSERVABILITY

Toolchain observability will become a critical competitive advantage. As distributed systems, microservices, and AI-driven code generation make development environments more complex, pinpointing issues like bottlenecks, test failures, and errors will become more challenging without robust observability tools. Greater visibility into these processes will be integral for maintaining efficiency and delivering quality software. We'll see an increased focus on this from IT leadership in 2025.
Brian Demers
Developer Advocate, Gradle

CHALLENGE: TOOL SPRAWL

The tool sprawl for managing Kubernetes environments will continue, with organizations using multiple overlapping tools for observability, security, and automation. Managing these tools will require dedicated personnel and increased budget allocations. Operations teams will prioritize centralizing management and observability platforms to reduce complexity and cost. Developers may find their workflows disrupted by tool-related inefficiencies, slowing down innovation.
Itiel Shwartz
CTO and Co-Founder, Komodor

CHALLENGE: MICROSERVICES SPRAWL

AI will drive microservices sprawl, requiring teams to implement development guardrail: The speed of AI code generation will make it significantly easier to create new microservices, potentially leading to microservices sprawl and increased complexity. AI makes writing single-function services, which is the typical microservices approach, much easier. In the past when developers had to write services manually, they would spend more time researching whether similar functionality already existed elsewhere in the system. But with AI tools rapidly generating code, teams can quickly spin up new services without thoroughly checking for duplicates. Software teams will lean into development guardrails like architecture governance to combat microservices sprawl and ensure system integrity and manageability. Architecture governance, which involves setting rules for individual services and service groups, will allow teams to move fast while upholding design intent and data access principles.
Moti Rafalin
CEO and Co-Founder, vFunction

CHALLENGE: THIRD-PARTY API

The operational bill comes due after a decade of third-party API-fueled innovation: A decade of rapid innovation fueled by third-party APIs and services has transformed how modern production applications are built and delivered. These critical integrations unlocked new features and enabled faster time to market, but they came with operational trade-offs: lack of visibility, reliability impacts, cost implications and security risks. Now, in an AI-driven era of increased scrutiny, these hidden challenges have accrued to a breaking point, demanding immediate attention. In 2025, platform teams and SREs will be forced to reckon with the sprawl of external dependencies embedded in their stacks. Many will uncover untracked APIs, single points of failure and inefficiencies driving up cloud costs. Troubleshooting time and unmet SLAs will reveal the hidden operational burden of these integrations, while reliance on institutional knowledge leaves critical systems vulnerable.
Tyler Flint
CEO and Co-Founder, Qpoint

CHALLENGE: KUBERNETES ADD-ONS

Add-Ons Complexity will Become Unmanageable: The growing reliance on Kubernetes add-ons for functionality such as service mesh, CI/CD pipelines, and security will lead to unmanageable complexity. Organizations will struggle to keep up with upgrades, interdependencies, and troubleshooting multiple add-ons. Platform engineers and DevOps teams will need to invest in more sophisticated management tools to maintain control. Additionally, roles will evolve to require deeper expertise in managing add-on ecosystems to prevent cascading failures.
Itiel Shwartz
CTO and Co-Founder, Komodor

CHALLENGE: AI/ML WORKLOADS ON KUBERNETES

As more organizations deploy AI/ML workloads on Kubernetes, inefficiencies in resource allocation (e.g., underutilized GPUs or memory bottlenecks) will become more pronounced, causing operational and financial strain. AI/ML engineers will need to collaborate closely with Kubernetes administrators to set up guardrails that optimize resource use while preventing overprovisioning. Continuous performance tuning will become essential to ensure that workloads are running efficiently.
Itiel Shwartz
CTO and Co-Founder, Komodor

CHALLENGE: AI-GENERATED CODE

In 2025, AI-generated code will significantly increase developer toil and leave organizations exposed to a far greater security risk. While research has shown that generative AI can halve the time it takes developers to complete coding tasks, the increased volume of code being produced must still be tested for vulnerabilities and errors. This will increase security risk and lead to additional toil in the later stages of delivery, wiping out any efficiency gains from AI-generated code. To overcome this, we will see more organizations putting robust guardrails in place and using AI to enforce them, enabling developers to automate quality and security testing. This will reduce the time spent on downstream workloads that drive toil, so developers can ship code to production faster and more confidently. Ultimately, this will reduce operational overheads while improving organizations' security posture.
Nick Durkin
Field CTO, Harness

CHALLENGE: AI EMBEDDED IN HARDWARE

AI capabilities are increasingly being embedded at the hardware level. This shift means that applications will need to be AI-capable to fully leverage these advanced hardware features. Without AI integration, apps won't be able to take advantage of the local processing power of these edge devices. As the hardware refresh cycle progresses over the next 18-24 months, there's a corresponding need for an application refresh. To stay ahead and meet customer demands, developers will need to update their applications sooner to align with the evolving hardware capabilities.
Kamal Srinivasan
SVP of Product, Parallels (part of Alludo)

CHALLENGE: GENAI QUALITY

QA teams will struggle to guarantee the quality of GenAI products unless managers invest in retraining. As more businesses adopt and customize GenAI services, quality assurance (QA) teams are facing novel challenges. The nondeterministic nature of GenAI adds complexity into testing that is new territory for many QA professionals, as some responses may pass quality checks while others fail. QA teams must also learn new techniques for mitigating bias through AI training and testing, such as red teaming. Given the potential harm that poorly trained AI applications can cause, managers must prioritize upskilling their teams in 2025 or risk losing market share and reputational damage. In the world of AI, QA is more integral to business success than ever before.
Rob Mason
CTO, Applause

GENAIOPS

People will start to realize that they need to optimize CICD pipelines for their delivery of GenAI apps or enhance existing products with AI, resulting in the establishment of the emerging category of GenAIOps solutions.
Helen Beal
CEO and Chair, Value Stream Management Consortium

Check back after the Holidays for more predictions about Low Code/No Code, Open Source and DevSecOps.