ABSTRACT
Machine learning and artificial intelligence have a long history that began in the 1950s. Applications that use AI and ML technologies have grown in prominence over the past few years. An effective AI/ML application must include software testing, just as with conventional development. Nevertheless, the process employed in AI/ML development differs in many ways from conventional development. These variances give rise to a variety of software testing difficulties. Identification is the primary objective of this study, which discusses some of the most significant difficulties that software testers have while working with AI/ML applications. This study has important repercussions for upcoming research. Each issue raised in this paper is perfect for future research and has tremendous potential to illuminate the path to software that is more productive.
