International Journal of Software Engineering and Knowledge Engineering

Testing is necessary to ensure the quality of web services that are loosely coupled, dynamic bound and integrated through standard protocols. Exhaustive testing of web services is usually impossible due to unavailable source code, diversified user requirements and large number of possible service combinations delivered by the open platform. This paper proposes a risk-based approach for selecting and prioritizing test cases for testing service-based systems. We specially address the problem in the context of semantic web services. Semantic web services introduce semantics to service integration and interoperation using ontology models and specifications. Semantic errors are considered more difficult to detect than syntactic errors. Due to the complexity of conceptual uniformity, it is hard to ensure the completeness, consistency and unified quality of ontology model. A failure of the semantic service-based software may result from many factors such as misused data, unsuccessful service binding, and unexpected usage scenarios. This work analyzes the two factors of risk estimation: failure probability and importance, from three aspects: ontology data, service and composite service. With this approach, test cases are associated to semantic features, and are scheduled based on the risks of their target features. Risk assessment is used to control the process of Web Services progressive group testing, including test case ranking, test case selection and service ruling out. This paper discusses the control architecture and adaptive measurement mechanism for adaptive group testing. As a statistical testing technique, the proposed approach aims to detect, as early as possible, the problems with highest impact on the users.

Recent research has shown the value of social metrics for defect prediction. Yet many repositories lack the information required for a social analysis. So, what other means exist to infer how developers interact around their code? One option is static code metrics that have already demonstrated their usefulness in analyzing change in evolving software systems. But do they also help in defect prediction? To address this question we selected a set of static code metrics to determine what classes are most "active" (i.e., the classes where the developers spend much time interacting with each other's design and implementation decisions) in 33 open-source Java systems that lack details about individual developers. In particular, we assessed the merit of these activity-centric measures in the context of "inspection optimization" — a technique that allows for reading the fewest lines of code in order to find the most defects. For the task of inspection optimization these activity measures perform as well as (usually, within 4%) a theoretical upper bound on the performance of any set of measures. As a result, we argue that activity-centric static code metrics are an excellent predictor for defects.

The different views and modeling techniques of both the business analysts and software developers are a common problem in business process modeling. Various modeling approaches result in communication problems, as well as redundancies and inconsistencies in system documentation. Thus, when modeling process-oriented enterprise applications seamless support of both expert groups is necessary. However, current business process management and workflow technologies are not fully integrated with user interaction, nor do they offer an appropriate data model. Based on the requirements of two industrial projects, we developed an integrated framework that combines the best practices from process-oriented and form-based approaches to overcome these shortcomings. The described framework supports the submit/response-style interaction paradigm and is independent of modeling languages and tools. In this work, we will present a detailed description of the proposed framework, its application to an industrial project and a discussion of related work.

Nodes comprising ubiquitous sensor networks have limited processing speed, storage capacity and communication bandwidth. Developing a USN application considering such problems is a difficult task. In this paper, a framework for automated construction of node software using low-level attributes is presented in order to support the developers who are not familiar with developing USN applications for their effective development of USN applications. The presented framework consists of attributes necessary for designing an application, code templates necessary for generating softwares for sensor nodes in the application, and tools necessary for designing and generating the application. Through the presented framework, the developers can easily design a USN application by using low-level attributes and automatically generate node softwares for the USN application. Therefore, development productivity will be increased.

In this article we report on novel insights in model-based software debugging of hardware description languages (HDLs). Today's simulation driven working process emphasizes the need for exploiting test suites not only for detecting but also for localizing the root cause of misbehavior. We discuss the modeling approaches for the various artifacts of the Verilog hardware description language (blocking and non-blocking statements, expressions, execution ordering) and present a novel model incorporating test suites. The evaluation of our approach on the well-known ISCAS89 benchmarks concerning single and double-fault diagnoses clearly indicates that incorporating test suites into the fault localization technique (and development process) considerably improves the accuracy of the obtained diagnosis candidates.