devopsy.kinsta.cloud

Tag: test automation

Why Automation Fails Without the Right QA Mindset
In the DevOps world, with its faster release cycles and continuous delivery, quality assurance (QA) cannot be sidelined until the end of the process. Companies that actively use automation achieve up to 60% faster release cycles compared to those relying solely on manual testing. However, automation alone is insufficient. Without the right QA mindset, moving faster can easily backfire if quality is not built into every stage. QA teams must evolve to become quality enablers throughout the software delivery cycle. Rather than joining at the tail end of the lifecycle, they must work alongside product owners to understand functional requirements, collaborate with architects and developers to grasp solution design and ensure visibility into end-to-end solution delivery.

Getting involved more deeply from day one means QA can help build quality from the outset through deployment. In response to this shift, QA professionals need to step up by:
- Designing and managing automated tests in continuous integration/continuous delivery (CI/CD) pipelines
- Focusing on risk-based testing
- Collaborating with teams to ensure quality is integrated into every step of the delivery process
In a fully automated pipeline, QA teams add the most value when they go beyond just testing and contribute as collaborative partners, strategic advisors and critical thinkers throughout the entire development process.

The Importance of the Right QA Mindset

Automation is key to fast, continuous delivery, but its real impact depends on the mindset driving it. In fact, 63% of organizations deploy code without thoughtful testing in pursuit of faster delivery timelines. The so-called developmental mindset focuses solely on speed, code coverage and scripting mechanics — often resulting in a check-the-box mentality, where tests verify expected outcomes but overlook real-world usage, potential risks and user experience.

The QA mindset, by contrast, offers a broader, more critical perspective. It is rooted in exploration, skepticism and curiosity. As QA professionals, we don’t just ask whether the software works — we ask how it might fail, where it could break and whether it fulfills the user’s ultimate needs. This mindset is essential for prioritizing automated tests based on business or user risk, rather than simply following functional decomposition.

With this strategic mindset, we design automated tests that anticipate edge cases, test assumptions and reflect actual user workflows. This leads to earlier detection of defects, fewer production incidents and, ultimately, a smoother user experience. In the long run, it also improves team productivity and reduces rework and reactive firefighting by catching issues in advance.

Automation alone doesn’t guarantee quality — it is only as effective as the tests it is scripted to run. If the requirements are misunderstood, automated tests may pass while critical issues remain undetected. I have seen failures where teams relied solely on automation without involving proper QA practices, leading to tests that validated incorrect behavior. Automation frequently fails to detect new or unexpected issues introduced by system upgrades. It often misses critical problems such as faulty data mapping, incomplete user interface (UI) testing and gaps in test coverage due to outdated scripts.

Lack of adaptability is another common obstacle that I’ve repeatedly seen undermine automation testing efforts. When UI elements are tightly coupled, even minor changes can disrupt test cases. With the right QA mindset, this challenge is anticipated — promoting modular, maintainable automation strategies capable of adapting to frequent UI and logic changes.

Automation lacks the critical analysis required to validate business logic and perform true end-to-end testing. From my experience, the human QA mindset proved essential during the testing of a mortgage loan calculation system. While automation handled standard calculations and data validation, it could not assess whether the logic aligned with real-world lending rules. Here is where human insight was crucial: Automated tests confirmed data processing and standard calculations, but couldn’t verify whether the outputs complied with evolving lending regulations and complex business requirements. QA ensures that test scenarios are aligned with business goals and remain adaptable to change.

As QA professionals, we often serve as a bridge between developers and business teams — translating business requirements into actionable testing scenarios and ensuring the team’s efforts focus on what truly matters.

Why Human QA Thinking is Irreplaceable

Automated scripts can only validate what they have been explicitly designed to check. That is why human-driven testing remains essential, even in highly automated pipelines. Human-driven testing is especially valuable for UI and user experience (UX) validation, exploratory testing and identifying edge cases that automation may miss.

For example, in a human capital management payroll system upgrade project with zero downtime, we applied a hybrid testing strategy that combined automated and manual efforts. Automated regression scripts ensured core functionality remained stable, while human-driven tests were used to validate new scenarios and feature upgrades. This ensured proper integration with other modules and subsystems. The result was a fast, stable CI/CD pipeline that still allowed space for creativity.

Human insight remains a critical part of both DevOps and QA. Without a mindset grounded in strategy, risk awareness and a deep understanding of how real users interact with software, automation can mislead teams. Quality teams don’t just bring tools — they bring perspective. By asking the hard questions, they ensure testing efforts stay aligned with what truly matters to both the business and the end user.

Key Tips to Strengthen Your QA Mindset

In conclusion, here are the key QA mindset principles for successful test automation:
- Integrate QA from the start. Involve QA early in the process — don’t wait until the end.
- Adopt a risk-based, user-focused approach. Prioritize testing based on business impact and user risk. Go beyond validating expected outcomes — explore how the software might fail in real-world scenarios.
- Design maintainable and adaptable test automation. Avoid brittle scripts that break with every minor change.
- Blend automation with manual testing. Let automation handle data and calculations, and human testers cover new features, UI/UX and edge cases through exploratory testing.
October 2, 2025
How Engineers are Automating More with Less: Trends in DevOps Tooling

As the software delivery cycle becomes more complex, engineering teams face increased pressure to achieve more with fewer resources. DevOps tooling is undergoing a silent revolution to meet these quick wins. The old way of building complex and heavyweight pipelines and workflows is constantly changing, giving room for a more streamlined and seamless automation-first approach. This is where simplicity, modularity, and cost matter as much as functionality.

According to GitLab’s 2024 Global DevSecOps Survey, 64% of DevOps professionals say they need to consolidate their toolchains due to integration challenges, monitoring issues, and deployment delays. This toolchain is pushing engineering teams to adopt easy, integrated workflows that reduce friction and improve velocity.

At the heart of this, automation shouldn’t require massive overhead. Today, teams are adopting tools that give instant value, which include enabling faster iterations, reduced maintenance overhead, and scaling team capabilities. In this article, we’ll explore the key trends redefining DevOps automation from modular pipelines and developer-first tools to toolchain consolidation and the growing influence of AI, showing how teams are achieving more with less complexity.

The Rise of Efficiency-Driven Automation

In the past, DevOps automation often meant cobbling together CLI scripts, sprawling YAML files, and fragile CI/CD pipelines. While this DIY approach offered flexibility, it came at a cost, making onboarding difficult, building brittle, and tool upgrades a constant source of firefighting.

Today, automation is moving from complexity to clarity. Teams now search and adopt solutions that reduce friction from the start by providing logical defaults, user-friendly interfaces, and plug-and-play components that scale, rather than wiring tools together. This focus has moved from automating everything to automating the right things efficiently and effectively.

Here are four main trends that are driving this efficiency-driven automation mindset

1. Composable Pipelines Over Monolithic Workflows

Modern DevOps teams are currently moving away from the traditional way of implementing a monolithic CI/CD setup. Instead, they’re adopting more composable pipelines built with tools such as GitHub Actions, Dagger, and Temporal. With these tools, teams break down automation into simple and purpose-specific units, such as jobs for running tests in the staging environment, another for pushing to development, and one for sending out notifications.

For example, instead of managing a complex Jenkinsfile with over 500 lines, teams can create smaller steps that are easier to troubleshoot, reuse across repositories, and scale independently. This shift enhances maintainability and allows engineers to focus on shipping code instead of managing complex YAML.

2. Developer Experience Takes Center Stage

Modern automation tools are shifting towards enhancing developers’ experience, making the automation of engineering tasks simple, intuitive, user-friendly, and less error-prone.

In a non-complex environment, instead of logging into several servers individually or battling with Kubernetes for minor maintenance tasks, engineers can use CloudRay to centrally manage and execute Bash scripts across servers using predefined templates and schedules through a clean UI. For example, a team managing several VMs can automate daily security patches or log rotations across several servers without the need to SSH into each server.

Additionally, platforms like Railway and Qovery offer similar attributes that reduce repetitive configurations while still maintaining their flexibility and control. As a result, engineers spend less time troubleshooting CLI scripts and navigating YAML files. Rather, they focus on solving meaningful problems.

4. Event‑Driven and Pull‑Based Models

Engineers are shifting automation towards an event-driven and pull-based model. Tools like ArgoCD and Flux are getting adopted because they apply the GitOps principle to manage infrastructure. Engineers no longer rely on manual triggers or constant pulling. Instead, they’re adopting tools that react to Git event, which treats GitHub repositories as the single source of truth.

For example, using these tools, merging a PR can automatically trigger a deployment with the exact change history preserved in Git. Additionally, it can simplify rollback and collaboration by linking infrastructure updates to version control, thereby reducing drift and production errors.

5. Cost-Conscious and On-Demand Automation

Cost optimization is one of the key selling points in automation in modern times. With the rise in serverless and edge platforms, automation has become more cost-efficient. Tools such as AWS Lambda, Vercel Functions, and Cloudflare Workers enable the engineering team to trigger tasks only when necessary. This eliminates the need to keep CI/CD runners on all the time, thereby reducing costs while maintaining performance. This is mostly used for lightweight workflows, scheduled jobs, or bursty tasks where automation can scale down to zero when not in use, and spin up instantly when triggered.

The evolution of DevOps tooling is transforming how teams approach automation. Automation is not seen as a complex practice to maintain, but a lightweight system that works.

Consolidation of DevOps Tooling

As engineering teams become more aware of the overhead associated with tool expansion and integration, many of them are reconsidering their DevOps stacks. The trend is not about choosing a “do it all” tool, but about consolidating what matters and reducing cognitive load.

Platforms like GitHub Actions and GitLab CI are becoming the go-to for workflows orchestration, handling everything from integration to deployment. However, this alone doesn’t eliminate the need for niche tools. This “best-of-breed meets platform” strategy allows teams to simplify existing toolchains despite fulfilling unique operational requirements. By reducing unnecessary overlap, teams can reduce the number of dashboards to track, secrets to manage, and workflows to troubleshoot.

For example, some organizations can replace several tools with a combination of Terraform for infrastructure provisioning, GitHub Actions for CI/CD, or CloudRay for server maintenance automation tasks across VMs. This approach results in a smaller, more focused stack that’s easier to operate, onboard new team members, and maintain over time. Conclusively, tool adoption for automations is not about reducing the count; it’s about what is working and suitable for the current organizational needs.

AI and the Next Frontier

AI is re-shaping how teams approach DevOps. AI is not replacing engineers, but rather improving how they work. With the adoption of AI into various automation tools, we’re seeing early wins in areas of anomaly detection, test automation, and observability. Tools are now giving insights, generating test cases that previously took hours of manual effort to achieve.

Platforms are beginning to embed AI into their workflows. For example, GitHub Copilot can suggest YAML configurations for CI pipelines. Datadog and Dynatrace use machine learning to detect anomalous patterns before they become an incident. Even in infrastructure, AI is helping teams in predicting consumption, optimizing the use of resources, and reducing false alerts.

Aside from the traditional workflows, low-code and no-code automation is also gaining traction. Engineers are using drag-and-drop tools or simpler configuration files to trigger multi-step deployments, onboard services, or manage cloud resources. This allows non-DevOps teams to engage in automation without needing to write any code or use CLI tools.

Looking at the future, the trajectory is autonomous operations. We’re moving toward systems that not only watch but also act, self-healing infrastructure, pipelines that tune themselves, and bots that offer the next best step based on deployment history or mistake trends. While we’re not there yet, each step, whether through AI ideas or rule-based automation, pushes DevOps closer to that goal.

Wrapping Up

DevOps automation is heading into a fresh phase, focusing on keeping things simple, modular, and genuinely helpful for developers. Instead of struggling with overly complex pipelines or inflexible toolsets, teams are now creating leaner workflows that fit their specific requirements. With AI and event-driven approaches becoming more advanced, automation is getting not only quicker but also smarter. The future isn’t about doing more tasks. It’s about doing fewer things, but doing them much better.

August 28, 2025
The Promise and Perils of Generative AI in Software Testing
The advent of generative AI (GenAI) is ushering in a new era in software testing. Acting as an AI test engineer, GenAI uses a test recorder designed to perform equivalent work to that of a human test automation engineer. It interprets plain-language instructions to autonomously generate test automation code. This capability democratizes testing, allowing individuals without coding expertise to directly interact with testing frameworks. It simplifies the process of test automation by enabling users to effortlessly generate code by recording their interactions with a website, with no manual coding required. GenAI-driven testers seamlessly integrate into CI/CD pipelines, autonomously detecting bugs and alerting teams about potential issues.

Transforming QA with AI

The journey from human automation tester to AI test automation engineer is transformative. Traditionally, transitioning to test automation required significant time and resources, including learning to code and understanding automation frameworks. AI removes these barriers and accelerates development cycles, dramatically reducing time-to-market and improving accuracy, all while decreasing the level of admin tasks for software testers.

AI-powered tools can interpret test scenarios written in plain language, automatically generate the necessary code for test automation, and execute tests across various platforms and languages. This dramatically reduces the enablement time, allowing QA professionals to focus on strategic tasks instead of coding complexities. This also addresses the severe shortage in test automation, helping companies achieve quality across their digital offerings in no time, for any number of scenarios in parallel, and in a matter of minutes. It creates test automation for multilingual websites and apps, as well as for any screen size, including computers, tablets and mobiles.

The productivity gains from AI in testing are substantial. While traditional test automation might be limited to a single platform or language and the capacity of one person, AI-enhanced testing breaks these limitations. Testers can now create and execute tests on any platform (web, mobile, desktop) using multiple languages, with the capacity of multiple testers. This not only amplifies testing capabilities but also introduces a new level of flexibility and efficiency.

The AI Advantage: Multilingual and 24/7 Testing

Upskilling QA teams with AI offers the significant advantages of multilingual testing and 24/7 operation. In today’s global market, software products often need to cater to diverse users, necessitating testing in multiple languages. AI facilitates this process without requiring testers to know each language, thereby expanding the reach and usability of software products.

The ability to test around the clock ensures that testing does not become a bottleneck in fast-paced development. Additionally, AI’s autonomous maintenance capabilities further reduce the time and effort needed for updating test cases, ensuring tests remain relevant and effective.

Navigating the Challenges

While GenAI offers significant advantages, it also presents operational and security challenges that organizations must address:
- Hallucinations: AI may generate inaccurate or fabricated outputs during testing, leading to incorrect results and potentially overlooking critical issues.
- Bias: AI systems can inherit biases from their training data, impacting testing outcomes and resulting in unfair testing scenarios and overlooked edge cases.
- Data Privacy: The risk of mishandling or leaking sensitive data used during testing raises significant privacy concerns.
- Lack of Transparency: The ‘black box’ nature of many AI systems makes it difficult to trace decision-making processes, hindering debugging and trust in the system.
- Security Vulnerabilities: GenAI systems are susceptible to adversarial attacks that could exploit system weaknesses, potentially compromising the entire testing process.
- Inconsistent Outputs: AI might produce erratic or irrelevant results, affecting test reliability and making it challenging to maintain consistent testing standards.
Strategies for Risk Mitigation

To harness the power of GenAI while mitigating these risks, organizations can implement several strategies:
- Human-in-the-Loop (HITL) Supervision: Incorporating human oversight ensures that AI-generated outputs are rigorously validated for accuracy and reliability. Human supervisors can review and approve AI-generated test cases, ensuring they meet necessary standards before implementation.
- Restricting AI Autonomy: Limiting the AI’s creative freedom prevents the system from making unwarranted assumptions or actions. Setting clear boundaries and guidelines ensures that the AI operates within acceptable parameters, maintaining a predictable and reliable testing process.
- Requiring Reasoning for Actions: Enforcing a policy where AI must explain its decisions promotes transparency and builds trust in AI-generated results. By demanding reasoning for each action, developers can gain valuable insights into the AI’s thought process and make informed adjustments.
- Secure Data Management Practices: Implementing robust data management policies safeguards sensitive information from being misused during AI training. Encryption, anonymization and access controls are critical measures in protecting data privacy.
- Utilizing Diverse Training Data: Training AI on a wide-ranging dataset minimizes biases and enhances the AI system’s robustness. Diverse data exposure helps the AI generalize better and reduce the risk of biased outcomes. Regularly updating the training data to reflect current and comprehensive scenarios ensures that the AI remains effective and fair.
Harness the Power of GenAI-Driven Testing Practices

As GenAI becomes increasingly integrated into software development life cycles, understanding its capabilities and limitations is paramount. By effectively managing these dynamics, development teams can leverage GenAI’s potential to enhance their testing practices while ensuring the integrity of their software products. With careful consideration of the outlined challenges and mitigation strategies, organizations can harness the full power of GenAI to drive innovation in software testing and deliver high-quality software products.
October 2, 2024
How Much Testing is Too Much?

We have reached an interesting point in the automation of IT in general and testing specifically. Our tooling now allows us to generate a list of all of the possible permutations of a given test set. This is interesting because we can generate all of the possible permutations of a given test. We can also automatically run all of the tests so generated. And we can automatically filter those results, only raising what needs greater attention to a human for evaluation.

And that’s not necessarily a good thing.

Nothing is free. Nothing. In the case of pervasive testing that goes beyond the wildest dreams of the most stringent QA manager, it costs a lot. The CPU cycles and storage space to create, execute, evaluate and notify the volume of tests we are talking about is exorbitant and unlikely to be adopted by your average organization. Truth is, it is unlikely to be adopted by any organization, but there is always some corner case, so I’m being a little vague to keep the conversation focused on the technical issues.

Testing vendors are already working on filtering tools to test only what has changed in the product, but this implies that, at a minimum, this level of testing should not be applied when testing is most needed–during initial development. I might be bringing my baggage along, but I personally prefer focused and curated testing to a firehose that is always on. It doesn’t take much imagination to see a scenario where a full-on automated test filtering tool is off just a little bit, and we get flooded with alerts that are not necessarily false positives but that include information we don’t want to see and consume man-hours needlessly.

Like everything in our recent past, this process will be rolled out too early and then will get tweaked and fixed on the fly, with you and I paying for the privilege of being beta testers, in fact, if not in name. So, I strongly advise caution in adopting new testing automation. I’m super excited by the possibility that all application testing can get the attention it deserves. Still, I am not at all interested in being the experiment on the way to that panacea. So watch the space, listen to other tech folks, and pay attention to how it is working out for them. We have had a tiny percentage of test coverage for so long that waiting for the early adopters to work out the kinks is not going to cause too much additional risk.

As a couple of glaring scenarios have recently reiterated, high-tech vendors are more than happy to do to you in the age of recurring revenue rather than for you. Use caution; make sure that any test automation tool you use aligns with your corporate goals and that the ones you hold subscriptions to are not rolling out massive test automation behind your back. Know your product, and pay attention to any changes in release or license—now more than ever.

And keep rocking it. Soon, we will have effective 100% or near 100% coverage in testing; just don’t rush it. Let the work mature and keep your eyes focused on serving your customers the best product you can without adopting bleeding-edge ideas that might create liability via technology or implementation immaturity. Your customers should thank you, but far too many won’t, so I will for them. Be they internal or external, those who use your systems owe you big thanks for the quality and massive uptime they take for granted.

February 28, 2024
5 Time-Saving and Problem-Solving Technologies for DevOps Teams

DevOps has transformed almost every aspect of software development, taking us from the dark ages of big-bang deployments every 12 months to the reality of releasing software multiple times per day. However, DevOps professionals have to overcome several hurdles to successfully implement and sustain the DevOps culture within the organization. Let’s review some of these challenges—and five technologies that can help.

Cultural and Organizational Differences

One persistent challenge for DevOps teams in a mature setting is navigating cultural and organizational differences. These differences often manifest in varied working styles, communication methods and priority management across teams. For instance, development teams might prioritize rapid innovation, while operations teams focus on stability and security, leading to conflicts. Managing these diverging priorities requires constant negotiation and alignment, which can be time-consuming and complex.

Furthermore, as organizations grow and incorporate diverse teams, including remote and international members, ensuring cohesive communication and understanding becomes even more critical. This diversity, while beneficial, brings challenges in terms of aligning on common goals and working practices, often leading to delays and misunderstandings that impede efficient workflow.

Complexity of Automation

In mature DevOps environments, the complexity of automation can become a significant day-to-day challenge. As the infrastructure and application landscape grows, the automation scripts and tools that were once efficient can become unwieldy and difficult to manage. Keeping these automation processes up-to-date with the constantly evolving technology stack is a never-ending task, requiring significant time and resources.

Additionally, the integration of new tools or updates to existing ones often introduces complexity, leading to potential errors and downtime. This complexity not only slows down the deployment process but also requires DevOps teams to invest time continuously in learning and adapting to new tools and methodologies. Ensuring that automation is both robust and flexible enough to handle a dynamic environment is a delicate balance that DevOps teams must constantly maintain.

Cross-Team Collaboration

Effective cross-team collaboration is vital for DevOps teams but can be challenging in day-to-day operations. The interdependence between various teams–such as development, operations, QA and security–requires a high level of coordination, often hindered by differing goals, tools and terminologies. Communication barriers can lead to misalignment on project objectives, causing delays and inefficiencies.

Moreover, in a fast-paced DevOps environment, keeping everyone on the same page regarding updates, changes and issues requires meticulous attention. This is compounded when teams are distributed geographically, as time zone differences and cultural nuances add another layer of complexity. Regular synchronization meetings, clear communication channels and shared tools are necessary, but managing these effectively without causing meeting fatigue or information overload is a continuous challenge.

Consistency and Standardization

Maintaining consistency and standardization across various projects and teams is a significant challenge for mature DevOps teams. As the number of services and microservices increases, ensuring that each adheres to the same set of standards for coding, security and deployment becomes increasingly difficult. Inconsistencies can lead to significant problems in integration, testing, and deployment, slowing down the entire delivery process.

Furthermore, as different teams may prefer different tools and practices, creating a standardized environment that accommodates everyone’s needs without compromising on efficiency or quality is a complex task. Regular audits, effective governance and a balance between flexibility and standardization are essential to manage this challenge, but they require constant effort and vigilance.

5 Lesser-Known Technologies That Can Help

While there is no one technology that can completely alleviate the above challenges, there are several tools that can help. Let’s review five technologies that can make the life of a DevOps team just a little bit easier.

Chaos Engineering Platforms

If you’re part of a DevOps team, you’re probably all too familiar with the panic that ensues when a system fails unexpectedly. Chaos engineering platforms aim to save you from such unforeseen surprises by embracing the philosophy of ‘breaking things on purpose.’

Chaos engineering involves intentionally introducing failures into systems to understand how they respond and to identify potential weaknesses. By doing so, DevOps teams can proactively find and fix issues before they manifest in production, thereby saving significant time and preventing potential downtime.

Platforms like Gremlin and Chaos Monkey offer controlled environments for running these chaos experiments, providing teams with valuable insights and recommendations. In the long term, this can not only save time but also instill a sense of confidence and resilience in your systems.

Application Dependency Mapping

Application dependency mapping (ADM) is a process that helps DevOps teams visualize and understand their applications and their dependencies. It is a crucial technology that aids in quickly diagnosing and rectifying application performance issues, thereby saving time.

ADM provides a holistic view of the application architecture, showing how different components interact with each other. This comprehensive visibility is invaluable during troubleshooting as teams can easily pinpoint problematic areas and rectify them without having to sift through mountains of code.

Furthermore, ADM is crucial when planning for application changes or upgrades. Understanding how changes in one component may affect others helps in preventing potential disruptions, leading to smoother and faster software delivery.

Policy-as-Code Solutions

Policy-as-code is a practice where policy definitions are codified and managed as code files within version control systems. This approach offers a host of benefits that can save time for DevOps teams.

Policy-as-code solutions enable automatic enforcement of policies, eliminating the need for manual checks. This significantly reduces the time taken to ensure compliance and allows teams to focus more on developing and delivering quality software.

Moreover, since policies are managed just like any other code, they can be versioned, tested and reviewed before being deployed. This allows for a more streamlined and error-free policy management process, saving valuable time and effort. Tools like Open Policy Agent and HashiCorp Sentinel are commonly used to implement policy-as-code in DevOps environments.

Service Mesh Technologies

Next on the list is service mesh, a dedicated infrastructure layer for managing service-to-service communication in a microservices architecture. Service mesh technologies offer a range of features that can save time for DevOps teams.

One of the primary benefits of a service mesh is its ability to offload common communication concerns from individual services to the mesh itself. This includes functionalities like load balancing, service discovery, and traffic management. By doing so, it saves DevOps teams the time and effort required to implement these functionalities within each service.

Additionally, service mesh technologies provide detailed observability of the service communication, making it easier for teams to monitor, troubleshoot and optimize their applications. Istio and Linkerd are two common open source options for implementing service mesh.

Codeless Test Automation Tools

Codeless test automation tools represent a significant shift in the way DevOps teams approach software testing, offering a more efficient and less time-consuming method. These tools allow the creation of automated tests without the need to write extensive code, making test automation accessible to team members who may not have deep coding expertise.

Using a graphical user interface, codeless test automation tools let users design tests by simply dragging and dropping elements into a workflow. This approach not only accelerates the test creation process but also simplifies the maintenance and updating of tests as applications evolve. Additionally, these tools often integrate seamlessly with various CI/CD pipelines, ensuring that automated tests can be easily incorporated into existing DevOps workflows.

With the speed at which technology is advancing, it’s crucial for DevOps teams to stay updated on the latest tools and technologies that can help them optimize their processes. The technologies listed in this article can significantly save time for DevOps team and make complex DevOps workflows a little smoother.

January 8, 2024
Generative AI and Software Testing Tools to Manage Technical Debt

It is easy to take a pessimistic view of generative AI and large language models (LLMs), especially when a growing list of AI experts and public figures have voiced concern about their potential to lead to dire consequences—even to human extinction. But we must also consider how these same technologies might mitigate these risks. There is no doubt that we are living in an accelerated cycle of technological progress and repercussions. However, the intelligence, speed and agility of AI—and particularly machine learning (ML)—may be just what we need to roll with the punches.

As the speed of development outpaces our ability to mitigate potential risks posed by the systems we build, we are set on a path to accruing massive technical debt—the ever-increasing costs associated with modernizing code later rather than sooner. Too often, organizations in the private and public sectors are reluctant to pay off this debt, choosing an “if it ain’t broke, don’t fix it” mentality toward software maintenance and security. The problem is that as technology outgrows and outpaces our critical infrastructure systems, the potential for catastrophe escalates.

Machine Learning: A Tool for the New “Space Race”

Automated testing has proven superior in regression testing, especially in visually recognizing potential issues at the pixel level. If, for example, an important element of your interface is missing from the screen after an update, a visual test script can identify that faster than a human can. AI (and in particular, machine learning) generates test plans that provide more comprehensive coverage of the system under test and more practical coverage.

Traditional testing methods usually begin with a broad and hypothetical analysis of user interactions. Test engineers begin by casting a wide net to cover the many possible user engagement scenarios they imagine. In contrast, AI-assisted systems learn from actual user interaction, building script libraries that focus on known points of weakness and vulnerability. It also does this at speed and scale. This technology is going to be essential to software test engineers as they inevitably find themselves unable to keep up with the abundance of code generated from LLMs using traditional testing methods.

Technical Debt is Inevitable but not Unsolvable

Every new iteration of software has the potential to introduce unforeseen problems ranging from glitchy bugs to catastrophic failures. The risks of releasing new software vary immensely between applications: It’s one thing if the latest release of your music streaming app crashes and quite another if the system that runs the regional power grid fails. One will result in unhappy customers; the other, the potential loss of life.

The most critical systems (e.g., financial, transportation, safety, medical) are more likely to get frozen in time. Once a stable iteration is released, companies are generally not keen to make changes unless absolutely necessary—beginning the cycle of technical debt accumulation that grows until that debt is paid off by modernizing the code.

Deciding when to pay off technical debt has always been a matter of weighing pros, cons, costs and benefits. But sometimes, such as in the case of an epic failure, that decision is forced. Both the risk of breaking the system and the cost of modernization compel developers to leave well enough alone. Meanwhile, the longer a system remains unchanged, the more isolated, outdated, and obsolete it becomes, setting the stage for failure.

By introducing generative AI technology to the development stack along with other tools for authoring, compiling and testing software, developers will soon find themselves driving in the fast lane. With the security of knowing that code can be produced rapidly and released safely, owners of aging legacy systems will be more likely to modernize software sooner, helping them to pay off technical debt more quickly. That, in turn, could lead to increasingly rapid software development and release cycles, more work for developers, and more innovation. Such a workflow could very well become the embodiment of accelerating change, taking us from an “if it ain’t broke, don’t fix it” mentality to one where we anticipate the need for new code and avoid future catastrophic failures, or for that matter, human extinction.

September 12, 2023
Low-Code/No-Code Testing: Prioritizing AI-Enhanced Test Automation

As organizations strive for faster application delivery and uncompromising software quality, traditional approaches to test automation fall short. With the emergence of low-code/no-code powered by Agile methodology and AI-enhanced tools, a new era of efficient and effective QA automation services has dawned. The expectations of the modern digital landscape require test automation services to undergo a profound transformation.

Low-code/no-code QA automation services represent a paradigm shift in the software world. Its simplicity, agility and AI-driven capabilities enable organizations to embrace a more efficient and effective approach to testing.

What Low-Code/No-Code Brings to the Test Automation Landscape

● Hassle-free test automation: Low-code/no-code platforms empower testers to automate regression and functional testing with remarkable ease, reducing the reliance on complex scripting and coding. This simplicity accelerates the testing process, enabling quicker iterations and faster time-to-market.
● Agile alignment and unparalleled speed: Low-code/no-code test automation seamlessly integrates with Agile methodology and ensures that testing keeps pace with rapid development cycles. This alignment enables organizations to deliver high-quality software within shorter timeframes, gaining a significant competitive edge.
● AI-enhanced QA automation tools: By harnessing the power of artificial intelligence, low-code/no-code test automation tools can intelligently analyze vast amounts of test data, identify patterns and make informed decisions. This enhances test coverage, improves efficiency and minimizes false positives, resulting in more reliable and accurate test results.
● Cost savings and optimal resource utilization: By significantly reducing the need for extensive scripting and coding expertise, low-code/no-code quality assurance testing reduces maintenance costs and frees up valuable resources. Organizations can allocate their testing budgets more effectively, achieving higher ROI on their quality assurance investments.
● Uncompromising Software Quality: Through comprehensive and automated testing, low-code/no-code test automation ensures robust software quality. By minimizing the risk of human errors and identifying defects early in the development life cycle, organizations can deliver superior products that consistently meet or exceed customer expectations. This focus on quality helps build trust, enhance brand reputation and drive customer loyalty.

Why You Should Prioritize Low-Code/No-Code Test Automation Services

DevOps teams that recognize the potential of low-code/no-code QA automation services and their ability to drive innovation will be well-positioned to lead their organizations to success.

Here’s what AI-enhanced low-code/no-code test automation specifically brings to the table for DevOps:

● Agility for competitive advantage: Agile methodology combined with low-code/no-code QA automation allows organizations to respond quickly to market changes, customer feedback and emerging opportunities. DevOps teams can ensure that they embrace agility, enabling them to outperform competitors and capture new markets swiftly.
● Unleashing the power of AI: AI-enhanced quality assurance testing platforms leverage the capabilities of artificial intelligence to drive intelligent testing. DevOps teams can harness the power of AI to enhance test coverage, identify complex patterns and make data-driven decisions that optimize testing processes and ensure high-quality software.
● Resource optimization and cost efficiency: By adopting low-code/no-code test automation practices, teams can optimize resource allocation and testing budgets. The reduced reliance on specialized coding skills allows organizations to leverage the full potential of their testing teams, saving costs and achieving more efficient testing outcomes.
● Meeting the demands of software quality: In today’s competitive market, software quality is non-negotiable. DevOps teams must prioritize Agile methodology and AI-enhanced test automation to ensure that their organizations consistently deliver high-quality software that meets or exceeds customer expectations. This commitment to quality helps build trust, enhance brand reputation and drive customer loyalty.

August 4, 2023
In DevOps, Chatbots are a People Problem

By now, you are undoubtedly aware that you can call up a chatbot or install an AI assistant into your IDE and generate entry-level code and deployment scripts.

Indeed, if you just go to a site like ChatGPT and enter “generate Python code to do X” where X is something you need to be done–or an example like “read a file and print out the contents one line at a time,” it will do it. And if you keep it simple, it will do it well. If you say “again, in Java,” it will comply and do it well.

Then say “new chat,” and tell it to “write a bash script to deploy an app named X written in Java and using Gradle to a cluster named Y,” it will do that also, informing you of the names of the deployment and service YAML files. Then you can say, “generate the deployment file mentioned,” and it will. And then you can say, “parameterize the Java version,” and it will do that, too. This iterative revision can continue until the deployment code, containers and deployment files are complete. If you have not yet, I urge you to spend the half hour going through these steps once required, so the potential is obvious.

It is not yet to the point where I would (or you should) trust it with complex systems, but all of that busy work we’ve been trying to overcome with templates and preconfigured containers? We can actually get around the bulk of it now. The same is true for testing of all kinds. There are tools that will auto-generate test scripts for a given set of source code. They will also generate test execution code. That means that the only step left—and the one that has held testing back because of the volume of work—is reviewing results … But wait, there’s more! In security testing, AI can help eliminate false positives. In performance or QA testing, it can filter out all but the most egregious issues (as defined by the org or by a larger pool of apps).

The problem we need to address today is the people issue. What we have traditionally used entry-level people for—and trained them in Dev/Ops/DevOps with—is no longer necessary. Because the AI can do it. That is stunning and really very cool, if you think about it.

But it heralds a danger that every org needs to be aware of. What we do and how we do it is changing. For the better, but change always brings uncertainty, and the foreseeable bit we need to confront is, “Now that we don’t need entry-level people for these steps, how do we recruit and train them?” I do not yet have a definitive answer for this question, because I am a product of my time, so I see things in terms of learning the bits that make up the whole—advising people interested in DevOps to learn at least one Linux command shell, to understand how networking works in containers, to write the scripts to get containers (and/or public cloud instances) up and running, etc.

I do think that every org should start using these tools at a pace that makes them comfortable because it allows us to continue our “more with less” growth without significant pain. But we need to make certain we are training the next generation of DevOps engineers in the process. For the foreseeable future, we’ll still be the ones bug fixing and troubleshooting, so we need to understand the environment and the applications. We are starting to get some sweet tools to help us at a higher level, though. Anyone who has inherited complex systems knows that the learning curve of tons of source files that make up many subsystems and all of the spaghetti code that these can be comprised of is painful. I don’t normally mention brand names here, so I’ll just say there is at least one tool out there that you can ask to “describe what this code does” and it will run with it and give you an answer in plain English. This might be the training tool of the future, with the ability to compare what you see in code with what the AI describes in English.

But for now, even if you don’t hunt down app description AIs, having entry-level people review the files that the AI generates will serve two purposes: First, to validate what the growing AI space is capable of and second, to get entry-level engineers into the entry-level bits of the systems.

And keep rocking it. Make sure you have a path for DevOps in the AI future, but remember that the creation of the calculator did not statistically change the number of mathematicians, just made the bulk of them more productive. That’s all we’re doing here, just a bit more flexibly … But in the end, like a calculator, the AI has an equation that it fills and reports back with.

March 1, 2023
The Time for Automated Testing is Now

Skilled software testers continue to be in high demand, and it appears that trend won’t change any time soon. The current labor shortage and a looming recession is giving software development leaders good cause to think about how to attract and keep qualified personnel and, more importantly, enable them to work as efficiently as possible. Added to this scenario is a trend in the software development cycle toward shift left testing, which has put increased pressure on developers to take on testing responsibilities that often require taking on new perspectives and using new tools. Automated testing solves these problems by making it easier to learn how to build a consistent, reliable testing program in less time, requiring less maintenance.

Add to this the future promise of artificial intelligence or AI-augmented testing platforms and current machine learning (ML) capabilities, such as self-repairing of test scripts, and you have a platform that both levels the learning curve and accelerates efficiency. The sooner organizations can transition from manual to automated testing, the better they can withstand internal and external market fluctuations.

A Developer and a Tester Walk Into a Bar …

Traditional software testing is a manual process in which testers are employed to replicate users and are expected to bring that perspective to their work. As such, software testers have a very different mindset than software developers. Where developers are protective of their creative output, software testers are driven by a curiosity to probe the limitations of a system. It stands to reason that the qualities that make someone excel at either of these roles is, likewise, not the same. And yet, with the trend toward shift left testing and continuous integration/continuous development (CI/CD), the industry has blurred the distinction between coder and tester to the dismay of both sides.

Automated testing removes much of the monotonous work associated with repetitious test plans. It allows testers to focus on writing better scripts, extending the scope of their testing plans and scheduling more powerful load tests. From the organization’s perspective, once you have established a library of reusable test scripts, your testing program will be protected from future turbulence in the employment market, including recession and attrition.

Low-Code/No-Code

Regardless of the tool you use, code will be created if you are creating software — even test scripts. Ideally, however, a low-code/no-code tool can hide some of the complexity and promote best practices. Using a low-code/no-code tool can relieve a tester from creating test scripts from scratch by recording manual interactions and offering suggestions. In the case of shift left testing, a low-code tool can eliminate the need to learn entire testing frameworks while still enabling a developer to access code when desired. Automating testing also eliminates the human error inherent in repetitive manual tests. In short, test automation does not diminish the value of skilled software testers and developers; it increases it.

Placed in the hands of skilled testers, automated testing will only become more powerful, more effective and more critical to your development cycle. It elevates the skills of testers and makes them more productive, accurate and thorough. Test automation also makes for a smoother transition to shift left testing, allowing developers to spend less time testing their software and more time doing the creative work that makes them thrive. In a market where attracting and keeping top talent is only going to get more difficult, anything you can do to lighten the burden on your employees and make them more productive will be a big win for your business. So, the question is not whether you should transition from manual to automated testing but when.

January 9, 2023
Adopting Shift Left Testing in Software QA

I am often asked to recommend best practices for building software testing programs. The problem is that it depends on your definition of “best.” What works for an innovative startup developing software and deploying agile development sprints is not necessarily going to suit an established public company launching a new web product. The software development industry is so broad and diverse that there is no one-size-fits-all set of practices I can recommend. Put simply, the “best practices” are those that produce consistent results over time, meaning that they can be standardized. There has been a trend toward shift left testing in the past decade, and I have a few suggestions for getting the most out of development teams taking on this new responsibility.

Shifting Left Testing Supports Agile Software Development

“Shift left testing” means testing software for functional quality during the development phase, rather than waiting for the QA testing phase within the traditional software development cycle. Back when software was packaged for sale and deployment rather than delivered as a service, there was time in the development cycle for a dedicated QA phase. Developers started with a concept, analyzed their requirements and came up with a design before beginning to write the code. Once written, the software code would be integrated within existing systems (if needed) and then the software would be passed on to the QA team. Any bugs were sent back to development to be fixed and sent through the cycle again. All of these phases represented a conceptual move from left to right.

Shift left is a departure from this cycle in which developers test their own software in an effort to speed up the process. This kind of development works particularly well for dev teams engaged in agile development. By having developers test their software as they go, bugs can be identified earlier in the development process, which saves time and resources and enables more thorough testing.

Shift Left is Also Problematic

There are some significant problems with the Shift left method that can’t be ignored. Specifically, developers are being asked to perform functions they were never properly trained to do—and often have no real interest in doing.

Software developers are creative people by nature. Whether self-taught or formally trained, the majority of them have come to their positions in pursuit of creating something. Quality assurance testing, by contrast, is a destructive process. The goal of the QA engineer is to break things—kicking the tires and carefully documenting their destruction. And when you ask your creators to become destroyers, it doesn’t always go well.

We can’t fault developers for not wanting to find faults in their code. Just as the best builders are not the best demolition experts, software developers want to create and then move on from one project to the next. So their reluctance to engage in testing is understandable. It’s part of who they are. The truth is that most developers find testing a monotonous chore. We need to learn how to encourage and incentivize them to become good testers of their own code.

Provide an Encouraging Environment

The first step toward making developers better testers is to educate them on the importance of testing as part of the development process. They may initially think that you’re only interested in saving money on hiring QA testers, or that they are being asked to perform the work of two employees. Introduce shift left testing as an integral part of the development process, explaining that continuous early testing is key to building high-quality software. By putting testing within the context of elevating software quality, developers will become more engaged in the process, raising the stakes and their involvement in projects.

Make Software Testing Easier for Your Developers

Since testing is not likely to be the primary interest of your developers, simplifying the task is going to make it at least more palatable to them. Test automation tools can be a big part of this strategy. Automated test scripts can be run flawlessly, frequently, and with much greater load capacity than with manual testing. You’ll be able to scale your tests to mimic a single user, or thousands of concurrent users, to discover how your software holds up under high demand. Pixel-for-pixel aberrations and errors in your graphic user interface (GUI) can be analyzed more quickly and effectively than with manual testing.

Automated testing also takes much of the monotony out of testing, preventing burnout and resentment among your employees. Writing scripts also has the benefit of being, inherently, a creative, not destructive, process. By integrating the running of scripts into the normal workday of the developer, and emphasizing their value in building the best software possible, testing becomes a valued task, rather than a mindless chore.

Providing Other Incentives

When you adopt shift left testing, you just might be asking your developers to take on a role that they don’t enjoy as much as coding. And they may never enjoy it as much. But that doesn’t mean there aren’t actions you can take. If you want to keep your most valued developers happy and encourage them to stay with you, think about providing other incentives that make the entire job package irresistible. Make your company a great company to work for. From the working environment to other perks and compensation, any way you make your team happy can help them manage integrating new responsibilities.

Testing Tools Lighten the Load

As test automation technology gets more sophisticated, testing tools that incorporate aspects of artificial intelligence technologies to assist in test script generation and self-repair will play a larger role in enabling your developers to focus on the more creative aspects of the job — addressing design requirements and functionality (the “what” and “why” factors) in novel ways, and leaving more analytical, procedural tasks (the “how” factor) up to the tools. As testing tools perform more of the monotonous tasks for us, and with greater accuracy, developers will have more time for the creative tasks that keep them happy. These technologies are evolving as we speak and will address many of the issues described above. But you might still want to consider buying some doughnuts for the break room.

October 27, 2022