This poster updates a paper [3] presented at ITiCSE 2006 and re-examines a ten-year effort of our institution's use of community-based non-profit organizations (NPOs) in the information systems capstone course. Computer science or information systems majors often have adequate technical skills but lack an understanding of organizational processes, team project experience, and the ability to integrate information technology into an organizational setting. To bridge this gap, we use service-learning group projects that leverage local organizations. We document this effort to provide recommendations for successfully implementing similar courses.

Complaints often expressed about undergraduate computer science or information systems programs,,, is that students graduate with adequate technical skills but often lack an understanding of organizational processes, team project experience, and the ability to integrate information technology in an organizational setting. To address this, educators have historically created service-learning group projects, which leverage local organizations. These types of projects can be very rewarding for students and offer excellent educational opportunities for the students within the group. Educators who have taught service-learning group projects know there are significant problems with using this type of project. These problems include: motivation of students to do their best work, fair individual and overall group assessment, appropriate workload for a semester, and minimizing disruptive outside influences. In addition, the creation of new projects on a continuous semester basis would be very useful for the instructor of a project course. To solve these issues and problems, the following capstone course design was used with success at Grand Valley State University (GVSU). A socially-relevant, community-based assignment with local non-profit organizations provided the basis for the capstone information systems project course described in this paper. This project course produced working applications for actual clients that gave students a unique capstone experience.

No abstract available.

Software testing is very labor intensive and expensive and accounts for a significant portion of software system development cost. If the testing process could be automated, the cost of developing software could be significantly reduced. Test data generation in program testing is the process of identifying a set of test data that satisfies a selected testing criterion, such as statement coverage and branch coverage. In this article we present achaining approachfor automated software test data generation which builds on the current theory of execution-oriented test data generation. In the chaining approach, test data are derived based on the actual execution of the program under test. For many programs, the execution of the selected statement may require prior execution of some other statements. The existing methods of test data generation may not efficiently generate test data for these types of programs because they only use control flow information of a program during the search process. The chaining approach uses data dependence analysis to guide the search process, i.e., data dependence analysis automatically identifies statements that affect the execution of the selected statement. The chaining approach uses these statements to form a sequence of statements that is to be executed prior to the execution of the selected statement. The experiments have shown that the chaining approach may significantly improve the chances of finding test data as compared to the existing methods of automated test data generation.

Software testing, specifically, test data generation is very labor-intensive and expensive. As a result, it accounts for a significant portion of software system development cost. In this paper we present a chaining approach for automated software test data generation. The chaining approach uses data dependence analysis to guide the test data generation process. The experiments have shown that the chaining approach may significantly improve the chances of finding test data as compared to the existing methods of automated test data generation.

A novel approach to automating test data generation for distributed programs is presented. The approach is based on actual execution of the program under test, a run-time scheduler, function minimization methods, and dynamic dataflow analysis. Test data are developed for the program using actual values of the input variables. When the program is executed, the program execution flow is monitored. If during program execution an undesirable execution flow is observed then function minimization search algorithms are used to automatically locate the values of input variables for which the selected path is traversed. In addition, dynamic dataflow analysis is used to determine those input variables responsible for the undesirable program behavior, which can lead to significant speed-up of the search process.>