Applications in the fields of scientific computing, simulation, optimization, machine learning, etc. are sometimes said to be “non-testable programs ” because there is no reliable test oracle to indicate what the correct output should be for arbitrary input. In some cases, it may be impossible to know the program’s correct output a priori; in other cases, the creation of an oracle may simply be too hard. These applications typically fall into a category of software that Weyuker describes as “Programs which were written in order to determine the answer in the first place. There would be no need to write such programs, if the correct answer were known. ” The absence of a test oracle clearly presents a challenge when it comes to detecting subtle errors, faults, defects or anomalies in software in these domains. As these types of programs become more and more prevalent in various aspects of everyday life, the dependability of software in these domains takes on increasing importance. Machine learning and scientific computing software may be used for critical tasks such as helping doctors perform a medical diagnosis or enabling weather forecasters to more accurately predict the paths of hurricanes; hospitals may use simulation software to

Challenges arise in assuring the quality of applications that do not have test oracles, i.e., for which it is impossible to know what the correct output should be for arbitrary input. Metamorphic testing has been shown to be a simple yet effective technique in addressing the quality assurance of these “non-testable programs”. In metamorphic testing, if test input x produces output f (x), specified “metamorphic properties ” are used to create a transformation function t, which can be applied to the input to produce t(x); this transformation then allows the output f (t(x)) to be predicted based on the already-known value of f (x). If the output is not as expected, then a defect must exist. Previously we investigated the effectiveness of testing based on metamorphic properties of the entire application. Here, we improve upon that work by presenting a new technique called Metamorphic Runtime Checking, a testing approach that automatically conducts metamorphic testing of individual functions during the program’s execution. We also describe an implementation framework called Columbus, and discuss the results of empirical studies that demonstrate that checking the metamorphic properties of individual functions increases the effectiveness of the approach in detecting defects, with minimal performance impact.

Abstract. Recent advances of computing and networking technology have shifted computing convention from stationary to mobile. A forecast conducted by Gartner indicates that global mobile handset sales will increase exponentially in 2008 by 10 % from 1.3 billion units in 2007 [13]. Furthermore, the popularity of wireless networking topology including both Wi-Fi access point and cellular mobile telecommunications enables users with constant access to online connection and further intensifies the demand of mobile devices. This provides the fundamental elements for creating ubiquitous environments of computing, networking, and interfacing that is both aware of and reactive to the presence of people. Such an environment is defined as Ambient Intelligence (AmI). Existing approaches that attempt to understand AmI environment mainly focus on how to seamlessly integrate hardware, i.e. mobile device and sensors, into human society and intelligently provide personalized knowledge and services. This has, however, left many essential issues unanswered, especially in regards to the integrity and performance of such dynamically distributed environments. In this chapter, we formulate a generic framework in which an AmI environment is generalized to consist only of users with devices, hosts where services are provided, and directory servers that act as information desks to users