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Maneuver end-to-end testing in the AI-led world

Author: Ashwini Phalle, Sr AVP, EXL

In the bustling city of Silicon Valley, a team of hardworking Software Engineers was working day and night to develop an integrated solution that would revolutionize the world of Insurance companies with the fastest response times for their customers.

Despite their expertise in Software development and Insurance domain, they were constantly being challenged by the tasks of testing of the integrated solution. There was never enough time to test the complete solution and testing coverage was very poor despite the presence of Test Automation for their regression suite. The testing methods they adopted were failing to uncover the defects which were creeping into the production environment leading to low-quality product and high cost of quality.

Test Manager, Geeta, was preparing a Test Strategy for testing this completely integrated solution which comprised of multiple heterogeneous systems which were being developed and modified by various teams. Geeta, known for her innovation and adoption of latest technologies, decided to venture into AI-Driven software Testing.

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Intelligent QA - Have we come a full circle

Author: Sandeep S Sudame, Capability Practice Leader - QA, New Vision Software

The journey...

Husshhh…it has been a long journey, indeed. And guess what, it has not finished yet! In fact, it is becoming more intense, challenging, interesting as the landscapes around us are changing lightning fast, naturally. Yes, I am talking about our journey as Testing enthusiasts, together. Truly, the Quality Assurance (QA) has come a long way,

  • from the times there weren’t any dedicated testers and Developers used to test their own code,
  • through various stages of dedicated testing and Automation tools, experts,
  • and now, where again there is a buzz if we need manual testers when RPA,
  • AI/ML has taken over most of your testing?

With rise and rise of DevSecOps, there is an unprecedented surge in use of AI/ML based tools and technology to support IT delivery functions. How can QA stay behind? Let’s have a look at how Quality Assurance is adapting to this paradigm shift.

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Winds of Change: Transcending Quality Assurance in the AI space

Author: Sasikanth Prabhakaran, Senior Project Manager, Indium Software

The need for faster delivery

Change is inevitable, as is the need for faster software product releases. This necessity arises from the exponential growth in digital adoption, advancements in software technology, and increased competition. Every decade there is a major transition happening in the software industry from the Internet boom to Mobile & Cloud Computing to Agile & DevOps Practices. With the growing demand for intelligent software delivery within shorter duration, the question arises: Is it the endgame for continuous integration and delivery?

According to Gartner, by 2026, over 80% of enterprises will be developing intelligent applications based on Generative Artificial Intelligence (GenAI). This projection suggests that there will be a significant shift in the software industry, driven by the integration of AI and ML. Though ChatGPT made a significant impact in the AI space last year, test automation tools at a very minimal level had demonstrated the glimpse of AI much earlier. The pace of AI is believed to be faster than any other technology that has been created in the past and companies have been challenged to keep up with this pace to sustain in the field.

From a broader perspective, testing focuses majorly on two areas: a) Functionality of newly developed features and b) Uninterrupted operation of existing functionalities due to new code. AI is also getting into individual testing areas like data analysis, data creation, visual testing, and automated scripting.

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AI Marvels: Navigating the Cosmos of End-to-End Testing Mastery

Author: Siddhant Wadhwani, SDET Manager, Newfold Digital

In the intricate cosmos of Software Development, where services and components form a celestial tapestry, the demand for a testing paradigm that transcends boundaries has never been more crucial. Moreover, in the grand theater of Software Testing, a new cast of characters takes center stage: OpenAI's GPT-4, Google's Gemini, X.AI's Grok, GitHub's Copilot and more. These aren't just experimental technologies; they're the virtuosos reshaping the symphony of End-to-End Testing. As the curtain rises on the year 2024, the pulse of software delivery quickens, demanding a cosmic shift in Test Automation efficacy.

Join me in this odyssey of envisioning End-to-End Testing with AI, while we explore innovative ideas, unravel the nuances of AI magic, discuss real-world examples from industry leaders, and confront the myth of AI job displacement. Enter the age of AI, where algorithms wield the baton, orchestrating a symphony of validation from Upstream UI to Downstream Databases.

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Exploring End-to-End Testing in the Age of AI: A QA Manager's Perspective

Author: Premkumar Hanumanthaiah, QE Manager, Opteamix

As a QA manager with over a decade of experience, I'm convinced that the infusion of Artificial Intelligence (AI) into the testing process isn't a luxury but a necessity. In this blog, I'll share my insights into the pivotal role AI plays in End-to-End testing, focusing on the human element that remains essential for its effective application.

The Challenge of Modern System Architecture

Software systems today are far from the straightforward applications of yesteryears. They comprise a web of microservices, APIs, databases, and interfaces, each playing a pivotal role in delivering the intended functionality. Ensuring the harmonious coexistence of these components is similar to navigating a testing environment or workflow that is overly complex or difficult to manage and that's where End-to-End testing proves invaluable.

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