Software systems are present all around us and playing their vital roles in our daily life.The correct function-ing of these systems is of prime concern.In addition to clas-sical testing techniques,formal techniques like model check-ing are used to reinforce the quality and reliability of software systems.However,obtaining of behavior model,which is es-sential for model-based techniques,of unknown software sys-tems is a challenging task.To mitigate this problem,an emerg-ing black-box analysis technique,called Model Learning,can be applied.It complements existing model-based testing and verification approaches by providing behavior models of black-box systems fully automatically.This paper surveys the model learning technique,which recently has attracted much attention from researchers,especially from the domains of testing and verification.First,we review the background and foundations of model learning,which form the basis of subsequent sections.Second,we present some well-known model learning tools and provide their merits and shortcomings in the form of a com-parison table.Third,we describe the successful applications of model learning in multidisciplinary fields,current challenges along with possible future works,and concluding remarks.