On the Use of Semantic-Based AIG to Automatically Generate Programming Exercises

In introductory programming courses, proficiency is typically achieved through substantial practice in the form of relatively small assignments and quizzes. Unfortunately, creating programming assignments and quizzes is both, time-consuming and error-prone. We use Automatic Item Generation (AIG) in order to address the problem of creating numerous programming exercises that can be used for assignments or quizzes in introductory programming courses. AIG is based on the use of test-item templates with embedded variables and formulas which are resolved by a computer program with actual values to generate test-items. Thus, hundreds or even thousands of test-items can be generated with a single test-item template. We present a semantic-based AIG that uses linked open data (LOD) and automatically generates contextual programming exercises. The approach was incorporated into an existing self-assessment and practice tool for students learning computer programming. The tool has been used in different introductory programming courses to generate a set of practice exercises different for each student, but with the same difficulty and quality

Model Superimposition in Software Product Lines

Abstract. In software product line engineering, feature composition generates software tailored to specific requirements from a common set of artifacts. Superimposition is a technique to merge code pieces belonging to different features. The advent of model-driven development raises the question of how to support the variability of software product lines in modeling techniques. We propose to use superimposition as a model composition technique in order to support variability. We analyze the feasibility of superimposition for model composition, offer corresponding tool support, and discuss our experiences with three case studies (including an industrial case study).