2024 DevOps Trends For QA

I’ve been reading many of the articles about trends and predictions within IT for the new year. I have been…

2024 DevOps Trends
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By Scott Moore,

I’ve been reading many of the articles about trends and predictions within IT for the new year. I have been particularly interested in those talking about DevOps. I noticed that most of them are Developer focused. This is very typical in DevOps – to leave out testing. QA is still an integral part of the “Dev” in DevOps, as well as the overall software release cycle. I decided to make my own list of DevOps trends this year, but I want to include how Quality Assurance might be impacted by these trends. Here is my take on the top five I hope to see:

Shift-Left Testing Goes Mainstream

Up until now, there has been a lot (and I mean a LOT) of talk about shifting testing left. Some have attempted it, and fewer are doing it with great results. However, I believe in 2024 we will see testing moving even earlier in the development lifecycle. This includes unit, integration, and performance testing integrated into CI/CD pipelines. There will be more emphasis on it, and advancement in tooling with AI will make it easier. We all imagine catching bugs before they even begin. That’s the promise of shift-left testing. However, it requires commitment to make that happen. It requires a continuous “everything” mindset, which is harder than many organizations admit to. 

QA Impact: Increased automation and earlier bug detection, reducing costs and improving software quality. We will see faster, more efficient workflows because of AI. Early bug detection will lead to lower costs and smoother releases. 

Hyperautomation and AI-powered Testing Enhances QA Capabilities

Last year we witnessed the hype machine at work over Generative AI, Large Language Models, and OpenAI’s ChatGPT. In 2024 we will begin to see the real beginning stages of what it can do. Here are some of the things we are already seeing in the market:

  • Intelligent test generation: Automatically creating comprehensive test cases based on code analysis and user behavior, reducing manual effort and increasing test coverage.
  • Real-time defect detection: Analyzing test results and logs in real-time to identify potential issues before they impact users, enhancing responsiveness and minimizing downtime.
  • Predictive maintenance: Proactively suggesting improvements to test suites and workflows, preventing regressions and ensuring continuous quality improvement.

Tricents Testim and TTM for Jira are great examples of what’s currently in the market. 

There are three challenges I see to implementing this:

  • Initial investment: Implementing hyperautomation and AI-powered testing tools requires initial investment in technology and training. Implementing internal LLM’s are expensive and resource hungry, especially the data storage components. Many (or most) companies are not ready for this.
  • Explainability and trust: Ensuring transparency and understanding of AI-driven decisions is crucial for building trust and acceptance among QA teams and stakeholders. Many companies are pulling back from AI because of various concerns over privacy, security, and accuracy. Early adopters will feel the most pain.
  • Skill gap: Bridging the skills gap in areas like data science and automation might require training programs and talent acquisition strategies. If you thought skilling up QA beyond manual testing to automation was a challenge, this will be a bigger gap to fill at first. However, I believe those who commit to it will see the biggest gains.

Despite these challenges, AI-powered tools will be able to automate repetitive tasks like test data generation and analysis, freeing up QA professionals for more strategic work. Think of AI-powered automation as tireless assistants, freeing up your time for strategic thinking. While it still requires domain knowledge, context, and some tweaking of the output, this is a huge boost to productivity for QA teams. 

QA Impact: AI-enhanced hyperautomation will bring improved test coverage, faster feedback loops, and increased efficiency to QA teams. They will get a major productivity boost. With AI handling the mundane, QA will begin to focus on strategic test design and analyzing data to predict and prevent problems. It allows for QA professionals to upskill into the areas of data science, advanced automation, and advanced analysis. Teams can handle larger projects and complete them faster. This could be the beginning of bringing testing to a whole new level. 

No-Code/Low-Code Testing Tools

Easy-to-use tools will allow developers and non-technical personnel to participate in testing.

There is a stigma about testing and deep, technical coverage being reserved only for the tech-savvy that can write code. Over the last couple of years, No-code/low-code tools have made advancement that shatter this idea. I believe we will see additional features from these products that will empower developers, business analysts, and “non-technical” roles and allow them to contribute equally to the testing process. I think of it as democratizing quality assurance, where everyone has a voice.

While leveling the field is great, there are challenges or considerations when implementing these kinds of solutions. Some argue that it brings with it less flexibility and limits the capabilities when you cannot get into the internal workings of the tools for complex testing situations. Some are concerned about the security and transparency when you can’t do security scans of many of the low-code products. Others feel that it leads to vendor lock-in situations that will lead to a higher cost in switching later on.

The biggest misnomer I see about these products is that it does not get rid of the need for testing expertise. It will not do everything, and companies can’t do away with the QA team with the push of a button. Understanding testing principles, methodologies, and best practices is still crucial for effective test design and execution. Some organizations have paid a heavy price assuming that a tool can replace the expertise and experience of QA professionals. This may be why there is some cultural resistance to adopting these tools. I think we will see this diminish as the tools become more mature, and I see 2024 as a year we can see some serious advancement on this.

QA Impact: Democratization of testing, but focus on maintaining test quality and expertise. Roles will shift towards ensuring test quality and expertise across the IT landscape as the testing pool expands to more people. Tools will make advancements that will cause less resistance to adoption for low-code solutions. 

Infrastructure as Code (IaC) for Testing Environments

It seems that JSON and YAML are not going anywhere anytime soon. We now see IaC everywhere for containerized applications, Docker, and Kubernetes. I believe we will start seeing more usage in QA to manage and provision testing environments quickly and efficiently. Imagine spinning up testing environments as effortlessly as flipping a switch. That’s the power of Infrastructure as Code (IaC). These tools will automate the provisioning and management of testing environments, saving you precious time and resources. Think of it as having a magic wand that conjures up the perfect testing sandbox, whenever you need it.

While IaC offers many advantages for managing testing environments, it also comes with some challenges and disadvantages. Here are a few:

  • Complexity and learning curve – IaC tools require specific syntax and understanding of infrastructure concepts, presenting a learning curve for testers and developers not familiar with it. This complexity may require dedicated specialists, adding overhead to the team. Debugging and troubleshooting IaC issues can be challenging, further contributing to delays and inefficiencies.
  • Limited Capability – While IaC can handle basic infrastructure provisioning, it might not directly address specific testing needs like data seeding, network configuration, or application deployment.  
  • Lack of Standards – The absence of industry-wide standards for IaC practices can lead to inconsistencies and inefficiencies across different teams and projects.

These challenges highlight the importance of careful planning, training, and tooling selection when using IaC for testing environments. I believe these are fairly easy to navigate around for most organizations. 

QA Impact: Reduced setup time and increased consistency in testing environments, freeing up QA resources for other tasks. IaC will allow for dynamic infrastructure, solving some of the environment issues traditionally faced in prior years. It will give QA more time to play in the testing playground, which means more experimenting to detect more defects. It also means more time for innovating over constantly wrangling with infrastructure.

Agile and DevOps Culture Shift

There will be a continued emphasis on collaboration and shared responsibility for Quality across DevOps teams. 2024 could be the year of a collaborative environment where Agile and DevOps principles intertwine. This isn’t just about developers and operations. QA plays a crucial role in making this happen. Agile and DevOps culture instills a sense of collective responsibility for quality, with everyone contributing to robust software delivery. There must be an unwavering commitment to quality in software if DevOps is going to be successful in any organization. 

One way we are seeing DevOps evolve is a renewed emphasis on the Developer Experience (DX). Platform Engineering emerged as a way to relieve some of the cognitive overload that developers are experiencing trying to do everything, everywhere, all at once. A positive DX goes hand-in-hand with improved QA. I believe QA products will also have to adapt so that the Developer experience is considered.  When developers have access to user-friendly tools, streamlined workflows, and clear documentation, they’re more likely to write high-quality code and actively participate in testing. 

Here’s how QA tools can contribute:

  • Seamless integration with developer workflows: QA tools should seamlessly integrate with existing developer tools and IDEs to minimize context switching for developers and make testing as effortless as possible.
  • Intuitive and user-friendly interfaces: QA tools built with intuitive interfaces and clear instructions will reduce the learning curve and encourage developers to embrace testing.
  • Real-time feedback and actionable insights: QA tools that provide real-time feedback on code quality and information about potential defects will empower developers to fix problems early, preventing them from reaching production.

I personally believe there is a strong tendency in people to build siloes, especially for previous generations because this is “the way we have always done it”. That does not mean it was wrong, and some organizations have had raving success with silos. Sometimes these are “soft” silos where there may be an emphasis towards one discipline or a lack of something – usually because they don’t have all the right players on all the teams. Sometimes it’s just that they don’t know what they don’t know. I think we will see more conversations about how to overcome the silo problem as DevOps starts to mature.  

QA Impact: QA professionals will need to adapt to collaborative work environments and be more open to feedback and improvement. QA starts to become an embedded member of cross-functional teams, actively influencing design, development, and deployment decisions. QA tools and techniques will begin to evolve to keep pace with evolving practices, like making the developer experience easier for tester and developer collaboration.


Obviously, there are a lot more areas of DevOps that will be impacted this year than I have listed. This might include implementing security and performance into the continuous development lifecycle, cloud-native testing, or solving the big data issues with testing. I felt these were the areas we might see the biggest changes in 2024.

These are just my predictions, and the actual impact on QA and testing may vary depending on the specific technologies and practices adopted by individual organizations. I don’t claim to be a prophet, but I have watch the IT landscape shifting for over 30 years now. Take it for what it’s worth to you. 

Have a great 2024, everyone!
Share your predictions & trends to keep an eye on 👁️


The following references were used in my own research. I encourage you to check these out and do even more investigation: