In the digital world, most businesses need to obtain top-notch quality for software and applications but in minimal time. To turn this expectation into a reality, it becomes essential for software testing companies to consider the test automation for any test cycle as it allows to go live in the shortest possible time frame while conserving budgets. As a result, testers have more time to generate innovative ideas and retain their productivity during work. Moreover, test automation allows them to make the final product high-performing and better.
Idea of Applying Data Analytics to Automation : Where does it come from?
Data Analytics aims to help people find and analyze the raw data to understand the latest business trends and the answers to those questions that enterprises always search on the internet to make their business successful.
Similarly, it has been noticed that in some of the top software testing companies, testers gather a wide range of metrics about defect count, classifications of test case execution, and test velocity. However, after collecting a lot of data, their information doesn’t help them always resolve the concerns about product quality or when it comes to analyzing how much money one can save with testing. Thus, the idea of applying data analytics to automation comes from testers when they’ve decided to deliver better business value by integrating test automation with regression analysis and leveraging visual analytical tools, which are specially curated to examine data and acquire deeper insights about emerging trends.
Related Read : Emerging Trends in Test Automation
Introduction to Data Analytics
Data Analytics is a discipline that focuses on extracting information from large amounts of data. It involves tools, techniques, and processes of data analysis and management, including collect, arrange and store the data of organizations. For this, one should have expertise in data models and reporting packages. People should have the ability to analyze large datasets and write comprehensive reports. In addition, one should have strong verbal and written communication skills, problem-solving nature, and an analytical mind to make recommendations about the methods and techniques in which companies obtain and analyze data to enhance the quality and the efficiency of data systems. By understanding the processes of data analytics, one can have a clear picture of where you are and where you should go.
How Will the Way of Automated Testing Change?
We are working in the era of Big Data and Data Analytics, where we can expect the transformation means the way we worked before on automated testing will change for sure. Consequently, to improve the final product, one needs to create test cycles based on the insights from the previous tests. Moreover, one should create data-driven tests to avoid mistakes during testing and easily identify the behaviour of past tests and patterns.
Today, the objective is not only to create automated test cases for building software or an application, but also one needs to work on updates simultaneously. Accordingly, whenever it comes to create or introduce updates in the application, one should have a collection of data about the performance of the existing version. Nowadays, worldwide software companies focus on regular updates, and they utilize data from test cases, so that users can get a great experience while using any product or application. For instance, Google launched eight updated versions of chrome in 2018 to help its users get the latest security features with make it possible for them to enjoy the different and unique browser.
If you want to comprehend what is happening in the real world, you should have customer-driven data for each feature that is being used.
Today, the demand of DevOps is working on continuous testing to release cycles as often as daily or weekly. The continuous delivery pipeline requires a well-fed development cycle for each process with constant feedback, often defines as the feedback loop. Moreover, QA professional in software testing companies mostly prefer to adopt and use Devops testing methodology to get the desired results with continuous testing. This pipeline needs the continuous execution of automated tests, with tests triggered on every code commit. In addition, several functional and non-functional test automation tools are used by Agile & DevOps teams to conduct unit testing as a part of the test delivery process.
Furthermore, many companies use Agile & DevOps methodologies as a norm for software development around the world. However, testing teams are using these methodologies to collaborate better, design, code, test, and deploy more reliable and high-quality software faster.
We assume that test automation helps us provide continuous feedback. Still, it is not completely true in the current times because automated tests are considered to maximize the velocity of the output in the end. We can drive better value for businesses if we apply data analytics to the test automation as Data Analytics allows you to collect data during and after performing the initial tests. This data you can use to analyze or get a better insight into the software’s performance.
It is also feasible for you to eliminate redundant data from the test cycles when you analyze the data with thorough recording. Overall, we can say that you can get the continuous regression testing done efficiently if you apply data analytics to the test automation.
Improve Test Automation with Data Analytics
It's critical to keep track of all results of test execution outcomes. With this process, you can gain valuable insights into the overall scope and health of your product. Most of the time, you plan to create test reports from the execution results, but you forget to take this plan into action. Thus, you should take access to the centralized database that allows you to use all the data about the actual scripts, iteration or test cycle, execution results with logs, product release, and helps you get a number of benefits.
Furthermore, automated tests are useful for producing machine data like device vitals, event logs, and server parameters, which you can use as a measurement to create new test cases. Data Analytics makes it easy to use an information many times for making changes in the codes. It also helps you get specific API tools to identify data nuggets, which you may use for working on activities for free.
The inclusive analysis of the test automation history provides you the following benefits:
Data analytics allows you to identify and exclude flaky tests. The test cycle, which is created by test teams, has various flaky tests that can pass or fail for the same features. Earlier identification of such tests is necessary to save time during execution, and it helps you decrease maintenance costs. To fix such issues, you need to perform analysis on test series and identify pass or fail tests.
During updates, you perform the tests for all software functions to confirm whether the system gets affected or not. With data analytics, you don’t need to implement all tests, which are not necessary because it gives you a clear picture of the areas that can affect through updates. Based on the analysis, you can run tests. In addition, it enables you to exclude those subsystems that are not affected by the new test cycles to help you save your time and money.
To know the impact of code generates through test cases, you can use data analytics. When two coders perform the testing at the same time, the chances get increased that they will leave more bugs or make mistakes in the system. Yet, you can resolve this issue with Data Analytics because it helps you collect data from the source change history sheet to identify where the changes are made by the tester or a professional. You can also understand how the change will impact the overall product. Therefore, Data analytics becoming the necessary need of QA companies to improve the skill set of their software testers to make the testing process.