State of AI applied to Quality Engineering 2021-22

S4.2: Automate & Scale

by Sogeti

Business ●●●●●
Technical ○○○○○

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Introduction

Testing consists of verification, validation and exploration activities that provide information about the quality and the related risks, to establish the level over confidence that a test object will be able to deliver the pursued business value.

The objective is to gather information that will assist stakeholders in developing confidence in an IT product's ability to add value to the organization and its users. This information is gathered through activities in the field of verification, validation, and exploration.

https://www.tmap.net/article/history-definition-testing

In this section, we look at scaling these activities in addition to computer vision we previously explored.

  • Chapter 1 investigates both model-free and model-based approaches for autonomous testing of modern Saas and digital applications.
  • Chapter 2 examines a few concrete examples where we have used AI to solve mobile testing challenges.
  • Chapter 3 reviews how machine learning models can help us evaluate mobile user interface design and how they can be used for other domain-specific testing.
  • Chapter 4 shares the advances in AI applied to usability testing.
  • In Chapter 5, we show how AI-driven self-learning techniques can reduce human error and bias in quality engineering. This is based on GPT3, an autoregressive language model that uses deep learning to produce human-like text.
  • In Chapter 6, self-healing RPA is used to scale automation in QE.

In this section

Chapter 1: A framework for autonomous testing of modern Saas and digital applications

Chapter 2: Expedite mobile testing cycles with AI-generated automated functional tests

Chapter 3: Use AI to asses how beautiful your app is

Chapter 4: AI applied to Usability Testing

Chapter 5: Experimenting with GPT3 to scale quality engineering

Chapter 6: Self-healing RPA for Quality Engineering