The quality of service oriented architecture (SOA) solutions is becoming more and more important along with the increasing adoption of SOA. Continuous integration testing (CIT) is an effective technology to discover bugs as early as possible. However, the diversity of programming models used in an SOA solution and the distribution nature of an SOA solution pose new challenges for CIT. Existing testing frameworks more focus on the integration testing of applications developed by a single programming model. In this paper, a unified test framework is proposed to overcome these limitations and enable the CIT of SOA solutions across the whole development lifecycle. This framework is designed following the model driven architecture (MDA). The information of an executable test case is separated into two layers: the behavior layer and the configuration layer. The behavior layer represents the test logic of a test case and is platform independent. The configuration layer contains the platform specific information and is configurable for different programming models. An extensible and pluggable test execution engine is specially designed to execute the integration test cases. A global test case identifier instrumentation approach is used to merge the distributed test case execution traces captured by ITCAM - an IBM integrated management tool. A verification approach supporting Boolean expression and back-end service interaction verification is proposed to verify the test execution result. Initial experiments have shown the effectiveness of this unified test framework.

This poster paper addresses the fitness calculation problem fornested if-else constructs. A new term "optimism level" is incorporated into the fitness function to assess the branch distance of nested branches for a given test data.

Automated test data generation is always a hot topic in software engineering, and evolutionary testing (ET) is an emerging and promising technology for this purpose. However, in structural testing, the presence of flag variables lead evolutionary testing degenerate to random testing. All previous work only focused on the flag problem in structural programs, and no attention has been paid to unstructured programs with flag conditions, although numerous industrial real-world programs are of this kind. In this paper, as a further step of the author's research, a fitness calculation rule for flag conditions in unstructured programs is proposed. The experiments on exemplifications recurrent in industrial real-world programs, such as Linux and NS2, show that our new fitness calculation rule could effectively guide evolutionary search to successfully find the required test data at low cost, while all previous approaches failed.

Evolutionary testing (ET), automatically generating test data with good quality, is an effective technique based on evolutionary algorithm. However, the presence of flag variables will make it degenerate to random testing in structural testing. Much of previous work has addressed this problem, but all can be characterized as program-specific. In this paper, flag cost function is introduced as the main component of fitness function, whose value changes with the variation of flag problem. Based on this, a unified fitness calculation rule for flag conditions is proposed. The experiments on programs with flag problems, once considered as inextricable in previous work, and the Traffic Alert and Collision Avoidance System (TCAS) code showed the effectiveness of our unified approach.