This paper presents a graph-based grading system for Java introductory programming courses, eGrader. This system grades submission both dynamically and statically to ensure a complete and through grading job. While dy...This paper presents a graph-based grading system for Java introductory programming courses, eGrader. This system grades submission both dynamically and statically to ensure a complete and through grading job. While dynamic analysis is based on JUnit framework, the static analysis is based on the graph representation of the program and its quality which is measured by software metrics. The graph representation is based on the Control Dependence Graphs (CDG) and Method Call Dependencies (MCD). eGrader outperforms existing systems in two ways: the ability of grading submission with semantic-errors, effectively, and generating reports for students, as a feedback on their performance, and instructors on the overall performance of the class. eGrader is well received by instructors not only for saving time and effort but also for its high success rate that is measured by four performance indicators which are sensitivity (97.37%), specificity (98.1%), precision (98.04%) and accuracy (97.07%).展开更多
文摘This paper presents a graph-based grading system for Java introductory programming courses, eGrader. This system grades submission both dynamically and statically to ensure a complete and through grading job. While dynamic analysis is based on JUnit framework, the static analysis is based on the graph representation of the program and its quality which is measured by software metrics. The graph representation is based on the Control Dependence Graphs (CDG) and Method Call Dependencies (MCD). eGrader outperforms existing systems in two ways: the ability of grading submission with semantic-errors, effectively, and generating reports for students, as a feedback on their performance, and instructors on the overall performance of the class. eGrader is well received by instructors not only for saving time and effort but also for its high success rate that is measured by four performance indicators which are sensitivity (97.37%), specificity (98.1%), precision (98.04%) and accuracy (97.07%).
