Unsupervised machine learning is the training of an artificial intelligence system using information that is neither classified nor labeled, with a view to modeling the underlying structure or distribution in a dataset. Since unsupervised machine learning systems are widely used in many real-world applications, assessing the appropriateness of these systems and validating their implementations with respect to individual users' requirements and specific application scenarios @math contexts are indisputably two important tasks. Such assessment and validation tasks, however, are fairly challenging due to the absence of a priori knowledge of the data. In view of this challenge, we develop a @math amorphic @math esting approach to assessing and validating unsupervised machine @math arning systems, abbreviated as METTLE. Our approach provides a new way to unveil the (possibly latent) characteristics of various machine learning systems, by explicitly considering the specific expectations and requirements of these systems from individual users' perspectives. To support METTLE, we have further formulated 11 generic metamorphic relations (MRs), covering users' generally expected characteristics that should be possessed by machine learning systems. To demonstrate the viability and effectiveness of METTLE we have performed an experiment involving six commonly used clustering systems. Our experiment has shown that, guided by user-defined MR-based adequacy criteria, end users are able to assess, validate, and select appropriate clustering systems in accordance with their own specific needs. Our investigation has also yielded insightful understanding and interpretation of the behavior of the machine learning systems from an end-user software engineering's perspective, rather than a designer's or implementor's perspective, who normally adopts a theoretical approach.
 Metamorphic testing is an approach to both test case generation and test result verification. A central element is a set of metamorphic relations, which are necessary properties of the target function or algorithm in relation to multiple inputs and their expected outputs. Since its first publication, we have witnessed a rapidly increasing body of work examining metamorphic testing from various perspectives, including metamorphic relation identification, test case generation, integration with other software engineering techniques, and the validation and evaluation of software systems. In this article, we review the current research of metamorphic testing and discuss the challenges yet to be addressed. We also present visions for further improvement of metamorphic testing and highlight opportunities for new research. A test oracle determines whether a test execution reveals a fault, often by comparing the observed program output to the expected output. This is not always practical, for example when a program's input-output relation is complex and difficult to capture formally. Metamorphic testing provides an alternative, where correctness is not determined by checking an individual concrete output, but by applying a transformation to a test input and observing how the program output “morphs” into a different one as a result. Since the introduction of such metamorphic relations in 1998, many contributions on metamorphic testing have been made, and the technique has seen successful applications in a variety of domains, ranging from web services to computer graphics. This article provides a comprehensive survey on metamorphic testing: It summarises the research results and application areas, and analyses common practice in empirical studies of metamorphic testing as well as the main open challenges.