Results from code clone detectors may contain plentiful useless code clones, but judging whether each code clone is useful varies from user to user based on a user’s purpose for the clone. In this research, we propose a classification model that applies machine learning to the judgments of each individual user regarding the code clones. To evaluate the proposed model, 32 participants completed an online survey to test its usability and accuracy. The result showed several important observations on the characteristics of the true positives of code clones for the users. Our classification model showed more than 70 % accuracy on average and more than 90 % accuracy for some particular users and projects.

This article presents a framework that adapts the conventional home electric appliances with the infrared remote controls (legacy appliances) to the emerging home network system (HNS). The proposed method extensively uses the concept of service-oriented architecture to improve programmatic interoperability among multi-vendor appliances. We first prepare APIs that assist a PC to send infrared signals to the appliances. We then aggregate the APIs within self-contained service components, so that each of the components achieves a logical feature independent of device/vendor-specific operations. The service components are finally exhibited to the HNS as Web services. As a result, the legacy appliances can be used as distributed components with open interfaces. To demonstrate the effectiveness, we implement an actual HNS and integrated services with multi-vendor legacy appliances.

Sensor-driven services often cause chain reactions, since one service may generate an environmental impact that automatically triggers another service. We first propose a framework that can formalize and detect such service chains based on ECA (event, condition, action) rules. Although the service chain can be a major source of feature interactions, not all service chains lead to harmful interactions. Therefore, we then propose a method that identifies feature interactions within the service chains. Specifically, we characterize the degree of deviation of every service chain by evaluating the gap between expected and actual service states. An experimental evaluation demonstrates that the proposed method successfully detects 11 service chains and 6 feature interactions within 7 practical sensor-driven services.

This chapter presents a practical framework that adapts the conventional home electric appliances with the infrared remote controls (legacy appliances) to the