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SmartData Collective > Analytics > QA Teams Need All-in-One Data Analytics Platforms for Testing
AnalyticsBig DataExclusive

QA Teams Need All-in-One Data Analytics Platforms for Testing

QA teams must invest in the best data analytics platforms to do their jobs effectively.

Sean Mallon
Sean Mallon
11 Min Read
big data in qa processes
Shutterstock Photo License - NicoElNino
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Data analytics is an invaluable part of the modern product development process. Companies are using big data for a variety of purposes. One of the most essential benefits is with the QA process.

Contents
All-in-One Big Data Platforms Are Key to Successful QA SystemsWhy is a testing platform a necessity for Agile-teams? Hint: It’s all about release speedTop crucial capabilities of good testing platformOne place of truthOrchestration: Set up one synchronous processLauncher: Don’t worry about the execution environmentAnalytical capabilities: Take advantage of AI/MLReporting: Get all information in a few minutesZebrunner Testing Platform as the optimal solutionQA Teams Need the Best Data Analytics Platforms

Advances in data analytics have raised the bar with QA standards. Companies need to invest in higher quality data analytics solutions to make the most of their QA methodologies. This entails creating robust, all-in-one solutions, rather than relying on a fragmented set of big data tools.

All-in-One Big Data Platforms Are Key to Successful QA Systems

QA engineers use a variety of big data tools in their work. All of these solutions are designed to make work more efficient, but in practice very often there is confusion. In this case, it is expected that each new tool will complicate things. This is not entirely true.

A high-quality testing platform easily integrates with all the data analytics and optimization solutions that QA teams use in their work and simplifies testing process, collects all reporting and analytics in one place, can significantly improve team productivity, and speeds up the release. Let’s figure out what a testing platform is and why it is beneficial to use it. This is an important part of big data testing for teams.

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Decision management and automated recommendations

Why is a testing platform a necessity for Agile-teams? Hint: It’s all about release speed

QA teams need a data analytics platform that would help them work effectively in a number of areas:

  • Run simple automated tests.
  • Writing scripts and running complex automated tests.
  • Data reporting.
  • Deep data analysis.
  • Communication with developers, as well as with management.

A significant role in this matter is played by the fact that more and more companies are implementing Agile and DevOps methodologies. They do this to speed up software development and get to market faster. Integrating testing into these software delivery models requires new QA tools that can be easily integrated into open-source test automation solutions for data engineers and QA specialists.

Thus, the presence of an all-in-one platform that integrates the entire workflow of QA engineers allows you to strengthen the potential of the team, simplify and significantly speed up many processes.

Top crucial capabilities of good testing platform

One place of truth

High quality test automation platform allows QA engineers to plan and complete all the required work. The platform easily integrates with a variety of tools and collects all data in one place.

Orchestration: Set up one synchronous process

Test Orchestration is the setting up of a well-defined sequence of automated test activities. For example, at the end of a development sprint, when the software is ready to be released, there are several steps that take place before the application is made available to users.

Each of these test actions can be individually automated, but manual intervention is still required to run all tests at the right time with the correct input. Orchestration helps link each of the individually automated test activities into one synchronous process.

With orchestration, you can run multiple rounds of testing within a limited amount of time and still achieve the desired level of quality. Test orchestration can be used by commands to perform several use cases:

  • Comprehensive testing of complex business scenarios that include multiple combinations of APIs and services.
  • Upgrade testing a release candidate against multiple upgrade paths. A certain version of the software may be upgraded from several previous versions. Testing each of the possible update paths helps to avoid a number of problems for users in the future.
  • Special security testing to identify security vulnerabilities in software or vulnerabilities with software dependencies.
  • Testing compliance with predetermined standards and rules.

The right data analytics algorithms help with all of these steps.

Launcher: Don’t worry about the execution environment

A launcher is a predefined configuration associated with a specific test repository that allows you to perform parameterized tests. The test run configuration should be available for any testing frameworks. You can configure presets for your team and run tests in one click.

Today, more and more companies have QA teams. The main responsibility of these teams is to automate the execution of test cases using the programming language. But having automated test suites is only part of what is needed. The most important part is to run these tests regularly to get instant feedback on software quality.

This is not as simple a task as it may seem at first glance.

At a minimum, this requires setting the environment for the programming language in which the tests are written. As the number of automated tests increases, the test suite becomes more time consuming, requiring some kind of scaling for the execution.

Test automation platform allows you not to worry about the execution environment. QA teams can focus on the test automation. Good platforms provide a tool that can be used to execute test suites stored in a Git repository and get comprehensive reporting at the same time.

Analytical capabilities: Take advantage of AI/ML

Test automation platform should provide a centralized place for reporting and automatically collect and analyze data from test launches, as well as report results in real time. A platform integrated into DevOps and CI/CD pipelines can do much more than just provide basic reporting. Test automation platform collects and links information from git-repositories, test launches, and a range of Agile tools to provide deep analytics of test activity such as requirements coverage, release readiness, test flakiness.

Test automation platform provides correct information for employees considering their role on the project. So, QA engineers get the information they need to work on product quality assurance, developers get the information that points to the problem spots in the code, and managers get the analytics and metrics, which allow them to evaluate the effectiveness of work on the product and its readiness for release.

High-quality test automation platform provides data processed with AI-technology. Thus, key decisions such as product release will be made on the basis of data rather than intuition. You can also train AI by yourself to improve the accuracy of its operation and facilitate the identification of failed tests in the future. 

Reporting: Get all information in a few minutes

Creating a high-quality report for the C-level can take a lot of time: you need to collect statistics from various resources, analyze it, identify trends, build charts, and calculate the necessary metrics. Test automation platform will create a high-quality report in a few minutes.

Zebrunner Testing Platform as the optimal solution

Zebrunner Testing Platform is an efficient test automation management solution. The platform significantly accelerates test execution, provides instant reporting and deep analytics, ensures full transparency and availability of test results.

Zebrunner is an advanced solution for scaling test execution, smart and transparent reporting, and QA team management. The tool helps save time and effort and makes automated testing as productive as possible.

There are critical capabilities of the Zebrunner Testing Platform:

Fast test execution and scalable testing environment. Many companies run tests in their own runtime environments with limited scalability. This is time-consuming. If a product, for example, is covered by 1000+ automated tests, their execution can take over 8 hours. As a result, the release is delayed and the project team is overloaded. Zebrunner Selenium Grid helps you run tests in parallel in the cloud or on-premise (locally). You can execute up to 1000 threads in 15 minutes. The team gains time to fix the bugs, and managers can be confident in the release.

Transparent reporting. Preparation of a high-quality report on the results of the tests can take lots of hours. Zebrunner Testing Platform does this automatically. Users can customize numerous widgets and dashboards to fit their metrics and create reports within minutes.

Analytics. Zebrunner helps QA engineers check for failed tests, find their causes, and provides many test artifacts (test logs, screenshots, videos). It is a perfect data analytics tool.

After running automated tests, a lot of time can be spent analyzing the causes of failures. Zebrunner Testing Platform solves this problem by using AI/ML automatic classification of test failure reasons. You can add links to bugs directly to Jira and GitHub so that the developer understands where and what needs to be fixed. Recurring problems can be identified using the test history.

Test process monitoring. Zebrunner Testing Platform gives management access to the QA process 24/7. Managers see release timelines, test coverage, ROI, KPI, so they can easily identify gaps in team productivity and optimize workload.

QA Teams Need the Best Data Analytics Platforms

Data analytics is crucial for QA processes. These companies need to invest in the right data tools to do their jobs successfully.

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BySean Mallon
Sean is a freelance writer and big data expert with a passion for exploring the depths of information that can be extracted from massive datasets. With years of experience in the field, he has developed a deep understanding of how data can be harnessed to drive insights and make informed decisions.

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