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Cloud Native Ecosystem / Software Testing

Cloud-Based Automated Testing Increases Speed in a Flash

Making the switch from manual to automated testing in the cloud is like the lightning strike that gives superheroes their powers.
Dec 7th, 2023 7:11am by
Featued image for: Cloud-Based Automated Testing Increases Speed in a Flash
Featured image by Joel Muniz on Unsplash.

Speed is power. It does not matter whether your favorite speedster is the Flash, Max Mercury or Johnny Quick, the nature of their speed is a key to unlocking the agility and efficiency they use to save lives and protect people.

Speed is also a superpower in the world of automated continuous testing. Public demand for flawlessly performing web and mobile applications is practically insatiable as organizations rush to accelerate and scale their software development and delivery to keep customers happy. Manual testing is not enough: To bring applications to market on time and at scale, development must be iterative and testing continuous. That is why so many organizations are moving away from manual techniques and adopting cloud-based automated testing.

The difference between the two is like the lightning bolt that gave the Flash his powers. It can change the testing process overnight, adding speed and reliability that results in higher-quality web and mobile applications.

The Growing Challenges of Manual Testing

There will always be a place for manual testing, but as organizations become more customer-centric as part of their digital transformation, it is simply not scalable enough to meet organizational needs.

The challenges in manual testing are still as relevant as ever:

  • Slow-paced and inefficient: As it relies on human testers to execute test cases one by one, manual testing is inherently time-consuming and leads to release delays.
  • Labor-intensive and costly: It costs a lot to maintain human effort, and as web and mobile applications grow in complexity, the cost grows too.
  • Human error-prone: Since manual testers make human errors, defects are missed and results can be inconsistent, compromising app quality and increasing risk.
  • Unscalable: When app complexity increases, the workload for manual testers becomes too much to handle, thereby weakening test coverage.
  • Unstable test coverage: Manual testing cannot keep pace with software development speeds, leading to gaps in test coverage and undiscovered defects.
  • Poor agile adoption: Agile methods make software development more iterative, but manual processes slow the speed, leading to bottlenecks and delays.

These scenarios are about to get a lot scarier and more challenging as the proliferation of generative artificial intelligence (AI) is increasing developer speeds by 30%-50%. This huge increase in code and the scalability in testing it necessitates will further increase the massive pressure already on manual testers. Manual testing may always have a space in the world of web and mobile app testing, but cloud-based test automation is helping testing teams scale with ease.

Slowing down Before Speeding up

Making the move to cloud-based automated testing from a purely manual process requires a structured approach that includes planning, implementation and continuous improvement. Let’s take it step-by-step.

  1. Assessment and planning: Organizations should evaluate their current testing practices, define their automated continuous testing objectives and identify the areas that are suitable for automation. It is crucial to lay this foundation as the transition from manual to automation begins.
    • Evaluate current testing practices, starting with the manual testing process, and look at their strengths, weaknesses and areas of improvement.
    • Define test automation goals and objectives, such as reducing testing times, scaling testing, increasing test coverage and improving quality assurance (QA).
    • To see what areas are testable requires investigation into where you can introduce automation, taking complexity, frequency and impact into account.
  2. Tool selection: Conduct a comprehensive evaluation of the tools that are available on the market. There is no such thing as “the perfect tool.” But the perfect tool for you does exist when you consider functionality, compatibility and ease of use.
    • Choosing an automation framework is based on a lot of common factors like the programming language, how it integrates with the testing environment and the types of tests it supports.
    • Setting up the cloud testing environment requires infrastructure and hardware to accommodate the new automation tool and have the proper test execution resources. Sometimes these clouds will be built on-premises, but there are device farms available that offer a wide range of devices and browsers that organizations can test on. This frees them from managing their own cloud, which takes time and is costly.
    • Establish test data management because continuous automated testing generates a ton of data. Test data can be managed with an analytics platform that combines your data and data from third-party products into one data lake, making information easier to find, group and analyze.
  3. Test case development and automation: Create and automate test scripts to evaluate software quality and functionality. The process includes translating manual test scripts into automated tests that are then executed by the automated testing tool.
    • When creating automation scripts, it is important to make sure the test cases accurately replicate the manual testing process.
    • By integrating the tools you choose with the cloud testing environment, the technical sprawl will come together, including automation tools, test environments and defect-tracking systems.
    • Automated test cases must be clear and include test steps, expected results and dependencies. Clarity, consistency and ease of understanding are critical.
  4. Test execution and monitoring: Next is executing and overseeing automated test scripts to evaluate quality and functionality.
    • Scheduling and execution of automated test cases while also capturing results and generating reports is central to this step. This is where organizations integrate with CI/CD pipelines to further automate testing as part of the software development life cycle (SDLC).
    • Monitor test executions, identifying issues or failures along the way. Once discovered, timely resolution is essential to prevent any bottlenecks in the process.
    • Adopt a test analytics tool to generate insights and make data-driven decisions as you continuously improve your testing process.
  5. Continuous improvement and maintenance: Everything culminates with the ongoing process of refinement, maintenance and optimization of the automated continuous testing process. It is a proactive step that identifies areas of improvement and keeps the process aligned with the broader business strategy.
    • Review and refine automated tests regularly to stay relevant and aligned with changing requirements.
    • Maintain the cloud infrastructure so that it is secure and can scale with the needs of the testing process.
    • Evaluation is a continuous task to make the entire process run smoothly, identify areas for improvement and align all processes with the broader methodologies used in the software development process.

Introducing AI and ML into the Process

The proliferation of AI and machine learning (ML) techniques are having a huge impact on the cloud-based automated continuous testing process. Developers are increasing their output with generative AI code that is created at a far faster rate, and automated testing must be able to pick up any slack so that the development process can run without interruptions or bottlenecks.

There are tools that can take a lot of the hard work out of the process to help increase test coverage and overall scalability.

  • AI-powered test creation is a novel approach that puts test creation abilities in the hands of non-coding team members. This is a great way to get manual testers involved in automated test creation, as they can access the devices in their cloud and use natural language processing to input their test scripts. What results is that the tests they create can then be exported in their chosen language to whatever test repository they use and then be integrated into the automated testing process. It makes the process of converting manual scripts to automated tests much simpler.
  • Sometimes automated tests can get flaky or error-prone. With an AI-powered self-healing tool, failed tests are examined by ML using data from previous test executions. It gives the capability to heal a failed test when the failure’s cause was the test itself. This works wonders in an iterative environment where a small user interface (UI) change can fail a whole execution. By making tests more resilient, teams can focus on functional defects and not get bogged down trying to differentiate between functional defects and test errors.
  • With an AI-powered analytics tool, you can train your ML models on that data to give your teams a full view of their development and delivery health, helping them make data-driven decisions and generate business value. These analytic lenses help predict software delivery flow to reduce cycle times, achieve more consistent quality and increase transparency across the organization.

“The fastest tester alive” can be a matter of debate. Without the enhanced brain power of the Flash, speed often sacrifices quality, particularly in the hands of a human. However, when automated cloud-based testing processes are introduced, the fastest tester alive does more than push a button to execute tests on cloud devices. This tester can view the overall process and determine the health of their applications and processes while staying aligned with business strategy.

Making the switch from manual to automated testing in the cloud is like the lightning strike that gives the Flash his powers. It helps accelerate testing while adding accuracy and reliability. The result is higher-quality web and mobile applications that will perform flawlessly and delight your customers.

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