It gets harder and harder to guarantee the quality of software systems due to their increasing complexity and fast development. Because it helps spot errors and gaps during the first phases of software development, software testing is one of the most crucial stages of software engineering. Software testing used to be done manually, which is a time-consuming, imprecise procedure that comes with errors and gaps and costs money, time, and effort. Currently, testing professionals routinely automate testing to obtain trustworthy results while saving time, cost, and labor. We’ve also moved the starting point of the software cycle to the developer, and made write tests before even writing code, or what’s known as TDD (Test Driven Development). The use of new artificial intelligence techniques will enable the generation of smart test cases to improve test quality and provide better coverage and accurate results. In this study, we used the Web of Science database to acquire bibliometric data about intelligent software testing papers which were conducted between 2012 and 2022, and we used Biblioshiny from the R bibliomerix package, alongside with VOSViewer in order to analyze the data and extract insights and answer research questions about the authors, articles, journals, organizations, and countries publishing in the field of intelligent software testing. The focus of this study is on scientific progress and collaborative trends in scholarly research, providing a blueprint for showcasing worldwide developments in the realm of intelligent software testing. By gaining a comprehensive understanding of the present state of research on the application of artificial intelligence in software testing, this study can offer valuable insights to software engineers, architects, and researchers in the field.

The need for effective and timely testing processes has become critical in the constantly changing field of software development. Large Language Models (LLMs) have demonstrated promise in automating test case creation, defect detection, and other software testing tasks through the use of the capabilities of machine/deep learning and natural language processing. This work explores the field of intelligent software testing, with a focus on the use of LLMs in this context. The purpose of this comparative study is to assess the corpus of research in the field in terms of used LLMs, how to interact with them, the use of fine-tuning, and prompt engineering, and explore the different technologies and testing types automated using LLMs. The findings of this study not only contribute to the growing body of knowledge on intelligent software testing but also guide fellow researchers and industry engineers in selecting the most suitable LLM for their specific testing needs.

Since the Model Driven Architecture (MDA) approach appearance, model’s transformation has been considered as one of the main computing keys in the software industry, which grows exponentially to ensure competitiveness. For that, the Object Management Group (OMG) offers for the MDA approach three abstraction levels, which are Computation Independent Model (CIM), Platform Independent Model (PIM) and Platform Specific Model (PSM). Most researchers put much focus on the PIM to PSM transformation because these levels have multiple common points, but they disregard the CIM to PIM transformation. So, our defiance in this paper is to propose a disciplined method that generates the UML2 Sequence diagrams automatically from the E3value model. Therefore, we produce the UML’s behavior and interaction model in the PIM level, from the value-based requirements model in the CIM level. The transformations are done automatically using the Atlas transformation language (ATL). The purpose is to reduce the effort, time, and the development cost of projects as all transformations is automated, increasing the chance of being more competitive in the software industry.