Software testing is an integral and important part of the software engineering discipline, and its automation has been actively pursued in both academia and industry to reduce its high costs. In the past decades, a considerable research effort has been devoted to automatic test case generation, automatic test selection, and automatic test oracles. The practice of software test automation has also moved forward significantly, and in recent years, a large number of software test tools have been developed and released to the market. However, progress in automatic software testing (AST) research is still required. The increasing complexity, pervasiveness and inter-connection of software systems, the ever-shrinking development cycles and time-to-market, and the scarcity of tools that can support all testing tasks within one environment, have posed new challenges and stricter constraints. Thus, despite significant achievements both in theory and practice, AST remains a challenging research area, and there is an urgent requirement to improve test automation to scale up productivity and quality in software development. Many times, however, industry needs differ from the research agenda, as companies need to prioritize reducing cost and time-to-market. Moreover, practitioners may have a hard time choosing a particular testing method or technology, since the real challenges that influence the decision are usually hidden. This special issue includes revised and extended versions of the best papers presented at the 2nd ACM/IEEE International Conference on Automation of Software Test (AST 2021), held in conjunction with the 43rd International Conference on Software Engineering (ICSE 2021), as well as new original submissions on the theme of “Automatic Software Testing from the Trenches.” This issue initially received a total of 13 submissions. Both the extended papers from AST 2021 as well as the new original submissions underwent a rigorous review process, and ultimately, 8 submissions were accepted for inclusion in this special issue.