March 4, 2021

By 2022, 70% of customer interactions will involve emerging technologies. Given the changing customer experience landscape, how can enterprises use quality engineering (QE) to drive scheduled compliance, enhance user experience and optimize cost? The answer lies in conducting agile case testing and technical scripting simultaneously.

Traditional test cases and technical scripts designing is time consuming and requires configuration of system parameters. A reusable and scalable intelligent test automation model is key to creating a command-driven, machine-led, multi-threading and multi-tasking system that can be easily integrated with the existing technology landscape across companies. Such automated quality engineering helps improve system efficiency by predicting system health and improving parameters through intelligent data modelling, recommendation for fixation of findings and self-healing capabilities.

Chatbot testing: The most sought-after intelligent test automation service

Artificial intelligence (AI) and machine learning (ML)-powered voice and chatbot support have become well established means of customer communication. According to the Business Insider, by 2023, chatbots are going to save banking, healthcare and retail sectors up to $11 billion annually. In this burgeoning market, voice assistant and chatbot testing applications can come in handy to explain business scenarios that can be automatically converted into plain text and machine-readable format to be used as functional test artifacts including strategy, use cases and APIs for automation.

The speech or chat can be sent to the cloud environment and analyzed by QE APIs to test design APIs for test execution. The voice commands can then be added through mobile phones or voice assistant devices and intelligent services can be modeled to receive such commands from a number of users and respond with faster performance. This can help manage complete automation setup with voice commands such as automated scripts, test data creation, execution across devices, browsers and other user interfaces.

For example, a remote user can use a voice channel and chatbot application to accomplish the automation task based on business requirement. Such a model will help other stakeholders create their own tests for testing. At the same time, business and end users can leverage it for creating automation test suites. This in turn helps businesses achieve:

  • Business agility with accelerated testing: Users can work in concurrent mode and start automation and record the steps at the same time.
  • Improved returns on investment (ROI): Automation at early test stage improves ROI as well as brings down the total cost of ownership (TCO).
  • Higher testing efficiency with automation: Test automation drives higher testing efficiency in execution cycles which in turn enables configuring with right set of data, launching technical scripts, and parallel executions through voice channels.

Elevating quality engineering for customer delight

Intelligent test automation services are poised to elevate quality engineering by simplifying device configuration, ensuring efficient communication and creating customized command for testing from anywhere. These are compelling reasons for stakeholders to leverage smart automation quality engineering solutions. This will encourage project teams to collaborate through effective communication, enhance early automation adoption for business need and modify tests at any point of time to improve the quality of the system. The outcome: streamlined processes and improved problem-solving approaches to build innovative solutions across industries for enhanced customer experience.

Biswajit Parija, a QE automation architect with Tata Consultancy Services, has more than 15 years of experience in quality engineering and service delivery across industries. He has led several automation testing engagements and driven test planning, environment set up, test execution, risk identification and mitigation initiatives for major global companies. Besides mentoring QE teams, Parija has also developed testing assets and testing guidelines. He has played an instrumental role in setting up a lab for cross-browser and device testing and automation at TCS.