Self-adaptive systems are able to adjust their behaviour in response to environmental condition changes and are widely deployed as Internetwares. Considered as a promising way to handle the ever-growing complexity of software systems, they have seen an increasing level of interest and are covering a variety of applications, e.g., autonomous car systems and adaptive network systems. Many approaches for the construction of self-adaptive systems have been developed, and probabilistic models, such as Markov decision processes (MDPs), are one of the favoured. However, the majority of them do not deal with the problems of the underlying MDP being obsolete under new environments or unsatisfactory to the given properties. This results in the generated policies from such MDP failing to guide the self-adaptive system to run correctly and meet goals. In this article, we propose a systematic approach to updating an obsolete MDP by exploring new states and transitions and removing obsolete ones, and repairing an unsatisfactory MDP by adjusting its structure in a more meaningful way rather than arbitrarily changing the transition probabilities to values not in line with reality. Experimental results show that the MDPs updated and repaired by our approach are more competent in guiding the self-adaptive systems' correct running compared with the original ones.

Application programming interface (API) libraries are extensively used by developers. To correctly program with APIs and avoid bugs, developers shall pay attention to API directives, which illustrate the constraints of APIs. Unfortunately, API directives usually have diverse morphologies, making it time-consuming and error-prone for developers to discover all the relevant API directives. In this paper, we propose an approach leveraging text classification to discover API directives from API specifications. Specifically, given a set of training sentences in API specifications, our approach first characterizes each sentence by three groups of features. Then, to deal with the unequal distribution between API directives and non-directives, our approach employs an under-sampling strategy to split the imbalanced training set into several subsets and trains several classifiers. Given a new sentence in an API specification, our approach synthesizes the trained classifiers to predict whether it is an API directive. We have evaluated our approach over a publicly available annotated API directive corpus. The experimental results reveal that our approach achieves an F-measure value of up to 82.08%. In addition, our approach statistically outperforms the state-of-the-art approach by up to 29.67% in terms of F-measure.

The cyber-physical space is a spatial environment that integrates the cyber and physical worlds to provide an intelligent environment for users to conduct their day-to-day activities. Mobile users and mobile objects are ubiquitous in this space, thereby exerting tremendous pressure on its security model. This model must ensure that both cyber and physical objects are always handled securely in this dynamic environment. In this paper, we propose a systematic solution to be able to specify security policies of the cyber-physical space and ensure that security requirements hold in these policies. We first formulate a topology configuration model to capture the topology characteristics of the cyber and physical worlds. Then, based on this model, a Topology-Aware Cyber-Physical Access Control model (TA-CPAC) is proposed, which can ensure the security of the cyber and physical worlds at the same time by adjusting permission assignment dynamically. Then, the topology configuration and TA-CPAC models are formalized by bigraphs and Bigraph Reactive System (BRS), respectively, allowing us to use model checking to rationalize the consequences of the evolution of topological configurations on the satisfaction of security requirements. Finally, a case study on a building automation access control system is conducted to evaluate the effectiveness of the proposed approach.

As an emerging “human problem solving strategy”, crowdsourcing has attracted much attention where requesters want to employ reliable workers to complete specific tasks. Task assignment is an important branch of crowdsourcing. Most existing studies in crowdsourcing have not considered self-interested individuals’ strategy. To guarantee truthfulness, auction has been regarded as a promising method to charge the requesters for the tasks completed and reward the workers for performing the tasks. In this study, an online task assignment scenario is considered where each worker has a set of experienced skills, whereas a specific task is budget-constrained and requires one or more skills. In this scenario, the crowdsourcing task assignment was modeled as a reverse auction where the requesters are buyers and the workers are sellers. Three incentive mechanisms, namely, Truthful Mechanism for Crawdsourcing-Vickrey-Clarke-Grove (TMC-VCG), TMC-Simple Task (ST) for a simple task case, and TMC-Complex Task (CT) for a complex task case are proposed. Here, a simple task case means that the requester asks for a single skill, and a complex task case means that the requester asks for multiple skills. The related properties of each of the three mechanisms are determined theoretically. Moreover, the truthfulness is verified, and other performances are evaluated by extensive simulations.