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Automation engineers working on automating various tasks in any organization's development and testing environment face challenges. This article addresses the challenges faced by testers working in selenium automation testing company providing them solutions as well.

Simple and easy maintenance of testing scripts

21st century AI (Artificial Intelligence) tools and software have addressed maintaining the test scripts. This trend continues with new and emerging AI being launched in the market. Newer bots improve the quality of maintaining the testing scripts as compared to their predecessors.

Recruiting quality test automation engineers has been the #1 challenge companies face. Hence, they look towards software and AI for any solutions. Developers have to work in sync with the test automation engineers whether they would be able to automate the tests. Companies, even startups, face the challenge of not only creating but also maintaining test automation programs.

IT teams have always faced the failure of test scripts. Why? Maybe because some developer changed the application beneath the test's element IDs. That is okay, but the person failed to update the automated test scripts.! Here comes the AI-enriched tools that automatically update the underlying test's element ID and those parameters during runtime. So, now test automation engineers can relax rather than manually update those parameters in the scripts.

Self-correction and auto-learning

Testing automation tools often collect enormous data. This data can include HTML text, multimedia files, input-output logs, errors encountered while ongoing text execution.

It is fed as input into the machine learning algorithms of the test automation tool. The tool learns and updates its behavior just like a human.

AI-enriched tools always function as cognitively aware software and train their algorithm just like a human would. Training is an ongoing process, and the tool uses training data to establish a baseline. Baselines are set to distinguish the behavior that deviates from the baseline or normal functioning of a test.

Gradually, such deviations from trained behavior function as alerts for potential errors and incidents. Training data can also be used to rectify the tool at runtime if any test encounters runtime issues.

Not just with self-correction and self-healing but AI-enriched tools often help engineers analyze and report on the test results. Again specific machine learning algorithms come to the rescue.

Automating test analysis and reporting

To save the time required to triage test failures, engineers introduced machine learning capabilities. ML algorithms perform the tasks of hunting the errors through log files and correlate the same. Taking the struggle out of testing is the #1 benefit of AI-enabled tools.

For instance, testers cannot search through millions of server logs, but a machine program is easing this task for testers. A machine can be trusted to show testers the insights, deviations, remediation suggestions, and likewise.

The recent past has shown us AI and ML tools improve quality test reports through data analytics services. Traditionally, stakeholders had to rely solely on the testers' or engineers' opinions, feelings, and experience. ML-enabled tools are capable enough to analyze not only the code coverage of tests but also test coverage. Also, when a developer commits a code, the same tools show the exact number of tests to be run on that code.

Automation tools to become more popular as compared to the Selenium-based ones

Software testing companies state that browser-based automation tool vendors are slowly dying off. Testers now use the open-source Selenium tool. Selenium has become the de-facto browser-based automation tool for testers. Hence, vendors of commercial tools also provide add-on support for Selenium.

Many testers don't prefer using Selenium but other AI-enabled tools as per their team's requirements, working styles, and preferences. Nowadays, AI-based automation testing alternatives from various vendors are available with a variable pricing structure. Newer tools are introduced that never have leveraged Selenium WebDriver. Jest, TestCafe, Cypress.io, etc., are a few examples of AI-enabled automation testing tools.

Record test macros and reduce test design time

Selenium IDE offers AI-based recording of tests and playback functionalities. Testers can first design the tests and then can use machine learning to improve reliability during runtime. This offers improved analysis and reporting capabilities. Also, the testers have to design the test once, modify as per requirements, and run it simultaneously. This saves a lot of struggle from the testers' end as they save their precious time.

Continuous and constant testing

Continuous testing means having the ability to instantly assess the risk of a new code release before it affects customers. Testers want to identify baseline deviations as soon as they are introduced in the project. As it is commonly understood, the sooner engineers find or prevent bugs, the easier and cheaper they are to fix.

"Test early, test often, and test everywhere in an automated fashion." is the motto of continuous testing. Continuous testing begins right from the initiation phases of SDLC, i.e., the unit module design. Every iterative test is a milestone completed to reach the complete coverage in the SDLC pipeline. Testers often execute automated tests probing the software quality during each stage of the SDLC (Software Development Lifecycle).

Collation and consolidation of vendor tools

Newly established vendors in the test automation market are creating an extensive list of clients to acquire end-to-end testing technologies. So the test automation market is booming. Organizations are procuring and acquiring startups and technologies in the areas of performance testing or test management. More mergers and acquisitions are upcoming in 2022.

Organizations continue to require automation testing technologies so they can scale their application delivery lifecycles. Also, continuous testing is primarily done to accelerate the application delivery timeline as well. Hopefully, the acquisitions may improve product suites, making 2022 an excellent time to be a tester.

The market is booming, and businesses are developing quality software rapidly. Hence, test automation vendors are too investing in testing tools to capture their market share.