Saritha Route
IBM, Automation Innovation Center and Global Test Automation Leader
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Saritha Route is passionate about taking AI-infused solutions to enable quality delivery across enterprise platforms for clients globally. As Global Test Automation and Automation Innovation Center, leader she brings the best of intelligent automation and quality engineering to play to enable clients to achieve quality objectives while enabling their business outcomes. She collaborates with IBM Research teams to drive innovation and new research technology into mainstream quality engineering. A Distinguished IT Specialist, Saritha pushes the boundaries with innovation in test automation architecture and solution development.
Area of Expertise
Topics
Testing Chatbot beyond dialog workflow through Full AI Cycle Testing
Chat-bot are consistently being adopted by most of the clients. Clients are concerned about the quality of the chat-bots and how they are supporting the end customer needs. Quality assurance of chat-bots are highly focused on dialog workflow. A complete quality assurance solutions should look beyond dialog workflow. It should focus on fairness, consistency, effectiveness of the chat-bots and look at the larger picture of where they fit in client applications. Full AI Cycle Testing (FACT) focuses on end to end application testing that includes chat-bots or conversation services as part of it. It evaluates the efficiency of chat-bot structure using static testing, focuses on sensitivity of the solution and also evaluates the payload of the conversation service and beyond. It brings in a scientific method to ensure 100% test coverage with optimal test cases thereby covering all dialog workflows as well. It uses deep neural networks to evaluate the sensitivity of the conversation solution.
FACT Aproach for Testing AI Infused enterpise applications
Cognitive enterprises and intelligent workflows are poised to take centre stage across business platforms to drive greater business outcomes. A cornerstone of any intelligent workflow will always be AI infused applications and accelerators. The question then arises on the quality engineering and the test rigour that the AI Infused solutions undergo. From the data through the APIs and to the application front end - how is the AI infused solution and workflow being engineered and tested for quality?
Testing of AI infused business solutions tend to see depth on two ends of a typical software development life cycle- Unit testing and business acceptance testing with loosely defined continuity in between. The IBM Automation, Quality Engineering and Test Practices and IBM Research have been working to address this gap. We introduce the Full AI-cycle Test approach. Starting with the data, the FACT approach provides automation solutions to baseline and establish the quality of the training and test data, provide insights on the errors in data labelling and content to identify potential skews in the data that could influence modelling decisions upfront. Ingesting the model and validating the model performance covers the model testing aspects with automated insights. From business coverage perspective, the functional test design uses proven cognitive design algorithms that are enhanced to cover the predictions and influencing data to provide ML test coverage. The solution enables build out of comprehensive test coverage for the business tests and the AI predictions and enable testing of the data, model and application in entirety. Thus providing Full AI-cycle Testing. We share this concept and provide a demo of the component solutions for realising the appraoch.
AI driven Visual Testing a need of the hour as we move to a more digital world for business outcome
Imagine a wholly digital bank - Present only online, with no physical branches and providing customer outreach via web, mobile, contact centers, kiosks and cafes! Our client is one such unique bank. The test and quality demands from such a bank are unique as the focus on customer experience and consistency is immense. The bank required to be able to validate changes to its websites and the functional enhancements to be delivered at speed and quality. The ask was not just to validate the functionality and operations but to also ensure that there was constant visual consistency. Being a digital-first bank, the software quality and experience of the customer accessing their accounts online was of prime importance. Our team worked in an agile model to deliver quality this quality across 2400 screens in 3 months. The weekly releases demanded automation of the highest levels as changes of various kinds are frequently pushed into production via the website and the mobile applications.
Our solution was to ensure that the changes do not provide misinformation or change the user experience of the websites and across devices. Content correctness was crucial, and the team had to make sure the content was not just up-to-date and accurately but consistently presented. We have used an AI-led visual testing tool to validate the websites for content, form, formats, and experience in addition to functionality. The team evaluated options to develop solutions and opensource libraries but decided on using AppliTools AI. We share with you the way was delivered along with the lessons learnt and best practices. This is a story of not just automation using computer vision, but of bringing automation into the entire test process and integration with the DevOps cycle.
Cognitive Defect Management for Preventive Quality Assurance Strategy
AI based Defect Management solutions provides a paradigm shift from Defect Detection to Defect Prevention strategy for Quality Assurance. Advanced AI techniques have been used to develop Defect Classification tool to identify the originating cause and application. These inputs are further analyzed for defect patterns through advanced data analytics to provide Preventive Road map for enterprises.
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