Different types of web resources, such as Web Services, HTTP-based APIs and websites, can be located on the web to provide various services, such as information access and online banking. Such services are the basic building blocks to compose more complex functionality that cannot be achieved by a single service. Many service-oriented applications can be composed to fulfill similar functional requirements. Among various applications, a set of services can be frequently used together to deliver a unique functionality. Such set of services are called a service composition pattern. More specifically, a service composition pattern consists of a set of services and the control flow among the services. The reuse of the service composition patterns can facilitate the composition of new applications, improve existing applications and optimize maintenance process of services. To facilitate the identification and reuse of service composition patterns, we propose an approach that mines the service composition patterns from execution logs produced by service-oriented applications during runtime. Since the execution logs can be produced by heterogeneous web resources, we propose a unified description schema to describe various web resources in order to identify functionally similar services of different types. This helps reveal complete service composition patterns. Then we identify frequently associated services using Apriori algorithm and heuristics. Finally, we recover the control flow among the services using the event graph and process mining techniques. The effectiveness of the approach is evaluated through two case studies. The result shows that the unified description schema facilitates the identification of similar services of different types and our approach can effectively identify service composition patterns. / Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2010-09-29 18:08:07.55

Context: Testing is an important activity in software development and is typically estimated to account for nearly half of the efforts in the software development cycle. This puts a great demand on improving the artifacts involved in this task such as the test cases and test suites (a collection of test cases).  Objective: When executing test programs, it is typical to record runtime information associated with the test cases in the form of test execution logs or traces. The aim of this work is to explore how this information can be utilized to improve the software testing process. To this end, two main aspects are investigated which are (1) in the context of test case generation and (2) in the context of different optimizations regarding existing test suites. Furthermore, the role of the logs regarding fault localization in connection with improving the existing test suites is investigated. Method: A systematic literature review is conducted to investigate, identify and analyze the existing literature on test case generation and test suite optimization that utilizes the test execution logs. Results: After a rigorous search in six digital databases, 26 primary studies were identified. 5 of the selected papers propose approaches in the context of test data generation, 8 papers suggest test case prioritization (TCP) techniques, 4 papers discuss approaches in test case selection (TCS), and 5 papers propose approaches in test suite minimization (TSM). Furthermore, we identified, 3 papers that discuss fault localization, and one paper that discussed the decomposition of large test cases into smaller single purpose test cases using the logs from previous test executions. Conclusion: The test execution logs are a useful source of information for different testing activities. Regarding test case generation, the main theme observed is the use of genetic algorithms in attempting to generate appropriate test cases when the alternative might have been to use random test data generation methods. When it comes to improving existing test suites several approaches within TCP, TCS and TSM such as similarity-based, modification-based, cluster-based, and search-based were put forward by the authors of the selected primary studies. Furthermore, several fault localization techniques using the logs were suggested.