Undoubtedly, software development is evolving with agile and DevOps and the way software is delivered has changed a lot in recent years. Evidently, the testing process has also been evolving and has moved towards more and more test automation. Organizations are now further using Artificial Intelligence to realize AI-based Intelligent Testing.
According to the latest World Quality Report 2019-20, Artificial Intelligence (AI) is going to be among the most significant trends in Quality Assurance & Testing for the next decade, and organizations across the globe are in a need to develop a strategy around it.
TestingXperts (Tx), brings in its AI expertise with machine learning (ML) algorithms together with predictive analytics embedded in its in-house IP, Tx-SmarTest. Tx’s AI Data Scientists unleash the power of next-gen software testing and drive Intelligent software testing through AI innovation. The inclusion of AI into the software development lifecycle helps the agile and DevOps teams to predict, find and resolve the defects quickly and early.
The addition of AI in testing speeds up the testing process as the repetitive tasks are automated which results in saving time and considerable costs. AI testing with Tx-SmarTest builds tests faster, runs at scale, tests faster with agile & DevOps CI/CD processes on the go. Further, it ensures to deliver quality releases with effective outcomes for customers by embracing AI and ML as mediums for testing.
Typically, for today’s fast-paced businesses with customer experience placed at the forefront, adopting AI and automating software testing with Tx-SmarTest is a critical need. It ensures faster and quality releases along with generating quicker ROI. This AI innovation evidently ensures the least human intervention as it uses Deep learning and ML algorithms to ensure quality outcomes with intelligent testing.
Tx-SmarTest is an AI-enabled comprehensive platform that systematically caters to faster releases enabled with DevOps CI/CD processes. It helps to boost the software quality through production enabled by combining AI, ML, Deep Learning and neural network algorithms.
Tx-SmarTest – A Comprehensive AI Automation Platform Consists of:
There is a lot of effort involved with respect to balancing the testing speed and release quality which is also one of the key challenges in software engineering. Especially with the ever-growing regression scope in order to keep up testing of the new functionality of a release, there is a need to automate repetitive tasks.AI-enabled Regression Suite automation intelligently runs automated regression tests based on the changes in code each time or large data set of historical code changes and test outcomes. It primarily reduces the overall regression test cycle time by identifying and executing only the set of test cases that must be executed. This sort of testing helps to satisfy the test scope while optimally managing risk if any,related to meeting the demands of delivering quality software and ensures to deliver as per the project timelines.
Specifically, by using the Tx-SmarTest into the automated cycle, selection and execution of specific regression scripts based on large data set of historical code changes and test outcomes. It adds true value as time-to-market is ensured at the proper time as only test cases that provide test coverage for the impacted features are executed. This AI enabled intelligenceprimarily ensures test coverage is not compromised at any point of time.
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For the success of any software, the proper and effective identification of software defects plays an important role, especially with today’s tight-packed go-to-market timelines. Undoubtedly, enterprises might miss their go-to-product timelines by missing defects during the testing process. It severely affects their brands due to these unspotted defects and with deferred timelines.
With Tx-SmarTest, the AI-enabled platform’s Intelligent Defect Analysis and Prediction tool, it essentially caters to predict when and where these defects occur as it is powered by ML and NLP algorithms. It effectively improves the overall testing efficiency and reduces test cycle time remarkably. This AI defect analysis and prediction tool improves the efficiency of the entire testing process by delivering comprehensive actionable insights with intelligent predictions.
Tx-SmarTest helps to identify gaps in quality and defect targets using predictive analytics embedded in the AI-enabled tool. It helps to track bugs quickly as testing processes are automated that are usually time consuming and significantly reduces the overall software delivery time. A detailed bug report is delivered to developers and testers to take effective and faster decisions based on the bug report’s comprehensive analysis.
Test automation has been widely in use in today’s agile & DevOps processes, but it is a true reality that in these processes, changes are always on the go. It is to be noted, changes to test scripts in agile & DevOps methodologies are always made rapidly and frequently due to which automation might stop working suddenly at any given point of time.
If suppose there is a change in an object or id, then the existing automated test script also needs a change. It might explicitly need human intervention for troubleshooting whenever changes are done to object properties. But, this sort of human intervention takes time and effort and thus should be handled automatically through AI-enabled Self-Healing process. Typically, it should be handled with an AI-enabled solution to automatically heal test automation script breakages due to object or any other property changes.
In these situations, the Tx-SmarTest approach comes effective as the AI-enabled tool can identify a change in objects and the intelligent bots learn from the internal data during regular tests. The auto-healing is done based on the historical observations of the tests and the Tx-SmarTest can automatically make changes to the script to make it ready for execution. This self-healing is achieved upon the tool sensing changes in properties or attributes and also by using historical data to scrap similar objects. The AI detects any sort of abnormalities in code and automatically fixes them without any human intervention through Self-Healing enabled with AI algorithms.