AI-POWERED SOFTWARE QUALITY ASSURANCE: TRANSFORMING TESTING WITH AI AND MACHINE LEARNING

Book excerpt: New challenges have arisen for the construction of contemporary AI-based systems as a result of recent developments in artificial intelligence (AI), in particular machine learning (ML) and deep learning (DL), and their incorporation into software-based systems utilized in all sectors of the economy. These systems place a heavy reliance on data, are constantly evolving and bettering themselves, and display a degree of intrinsic nondeterminism. As a consequence, their behavior displays a degree of uncertainty that is universally acknowledged. As a result of these characteristics, the field of software engineering has to devise adaptable and innovative approaches to quality assurance (QA) that are capable of both constructive and in-depth analysis. This is essential in order to guarantee the product's high quality throughout the whole development process as well as while it is being put to use in actual settings. On the other hand, as Borg has pointed out, the concept of "quality" in AI-based systems does not yet have a definitive definition at this time. As was noted before, the terminology that is utilized in the field of artificial intelligence and software engineering is distinct from one another. When developing AI-based systems, the knowledge and experiences of a wide variety of organizations are combined and utilized in the construction process. While this does lead to new and creative ways, exciting breakthroughs, and a major advancement in what can be done with current AI-based systems, it also encourages the babel of language, concepts, perceptions, and underlying assumptions and principles. While this does lead to new and creative methods, exciting breakthroughs, and a substantial advancement in what can be done with current AI-based systems, this does lead to new and creative approaches. While this does result in novel and creative methods, exciting discoveries, and a significant leap forward in terms of what can be accomplished with contemporary AI-based systems, it does so in spite of the fact that. For instance, in the field of machine learning (ML), the term "regression" may be used to refer to regression models or regression analysis, but in the field of software engineering (SE), the term "regression" is used to refer to regression testing. However, in the context of machine learning (ML), the term "testing" refers to the evaluation of performance characteristics (such as accuracy) of a trained model using a holdout validation dataset. In the context of software engineering (SE), "testing" is described as the activity of executing the system in order to uncover errors. As a consequence of this, there is an increasing amount of confusion, as well as the potential of solutions that are in contradiction with one another, about how to approach quality assurance for AI-based systems and how to deal with the challenges that are associated with it. This is because of the fact that there are a growing number of solutions that are based on AI. Although the authors of this study begin their investigation from the perspective of software engineering, their ultimate goal is to include and talk about a wide variety of different points of view, all of which will eventually come together to provide a multi-dimensional picture of quality assurance for AI-based systems. While the authors of this study begin their investigation from the perspective of software engineering, their ultimate goal is to include and talk about a wide variety of different points of view. In the first part of this study project, our primary focus is on defining the terminologies related with artificial intelligence quality assurance. In the following section, Section 3, we will discuss the challenges that are involved with QA for AI. In the last part of the inquiry, we will summarize what we found and form our conclusions.