Until now, various techniques for predicting fault-prone modules have been proposed and evaluated in terms of their prediction performance; however, their actual contribution to business objectives such as quality improvement and cost reduction has rarely been assessed. This paper proposes using a simulation model of software testing to assess the cost effectiveness of test effort allocation strategies based on fault prediction results. The simulation model estimates the number of discoverable faults with respect to the given test resources, the resource allocation strategy, a set of modules to be tested, and the fault prediction results. In a case study applying fault prediction of a small system to acceptance testing in the telecommunication industry, results from our simulation model showed that the best strategy was to let the test effort be proportional to "the number of expected faults in a module × log(module size)." By using this strategy with our best fault prediction model, the test effort could be reduced by 25 percent while still detecting as many faults as were normally discovered in testing, although the company required about 6 percent of the test effort for metrics collection, data cleansing, and modeling. The simulation results also indicate that the lower bound of acceptable prediction accuracy is around 0.78 in terms of an effort-aware measure, Norm(Popt). The results indicate that reduction of the test effort can be achieved by fault prediction only if the appropriate test strategy is employed with high enough fault prediction accuracy. Based on these preliminary results, we expect further research to assess their general validity with larger systems.

In this session, we will take a look at what kinds of research we think are needed in the area of education and training for software engineering. As groups, we will brainstorm about what we should be studying and how. We will also take a short look at the many ways to communicate before, during, and after research. This will not be a formal roadmapping of research directions, but more an informal discussion of possibilities. The goal of this panel is to promote and exchange ideas about research in our field, encourage possibilities for collaboration, and get people started on research and reporting for next year!

This study describes a new method of development visualization along with empirical evidence of its usefulness. Typically, development activities such as program design, programming, and unit testing are not disclosed to the procurement organization (project owner). However, during integration and testing, various issues require collaboration between the procurement organization and developers. When this occurs, it is important to make the development process visible. Recent reports indicate the usefulness for project management of various in-process project measurements which allow visualization of the formerly invisible software project progress [1–6]. Based on this background, the authors investigated a case study where in-process measurement during the integration and test phase helped to make development issues visible. In this study, data obtained from the integration and testing phase were compared to a development process model. This model was based on the author's experience, and provided a vivid picture of the development activity. By applying in-process measurements in collaboration during the integration test phase, the development activity was clearly visualized, and the procurement organization understood problems.

An Overview of the new workshop on Accountability and Traceability in Global Software Engineering (ATGSE2007) will be introduced here.

This paper focuses on in-process measurements during requirements definition where measurements of processes and products are relatively difficult. However, development processes in Japan based on the enterprise architecture method provide standardized formats for such upstream processes and products, allowing in-process measurements. Based on previous work and on this examination of in-process measurements of requirements definition with the enterprise architecture method and previous results of empirical studies of in-process measurements and empirically validates of later development processes, this paper proposes a new measurement model, the "full in-process process and product (I-PAP) measurement model," which includes the complete software development process from requirements to maintenance. Standardization of the requirements definition phase using the enterprise architecture method in Japan allows in-process measurement across the complete development lifecycle. Combining this with collaborative filtering and a project benchmark database will support project evaluation, estimation, and prediction.

Extracting requirements for large-scale software development has become increasingly complicated because many users from different organizations should collaborate with each other. Although use cases and UML diagrams for analyzing requirements are powerful tools, users often have difficulty understanding them. Therefore, we tried to extract detailed requirements via an empirical approach when we developed a plagiarism detection tool for students' reports. Key points of this approach are recording empirical data in an experiment and using an incomplete prototype based on component requirements. By comparing empirical data with output of the incomplete prototype, the detailed requirements are incrementally determined without additional efforts by the users. As a result of this approach, accuracy of the detection tool's output has reached 71%. In addition, we propose a framework for applying an empirical approach to requirements to adapt this approach to various developments.

The authors describe a new research framework for applying empirical software engineering methods in industrial practice and accomplishments in using it. The selected target for applying the framework is a governmentally funded software development project involving multiple vendors. This project involved in-process project data measurement in real time, data sharing with industry and academia, data analysis, and feedback to the project members. Today the project is in the system integration process. This paper shows the value of this research framework and describes issues of empirical data sharing between industry and academia which have emerged while using it. This experiment raised two major issues. One is the necessity of a new research framework for project measurement called the "Macro Measurement Tool". The other is effects of the software industry structure on this framework.