Automatic Grading for Complex Multifile Programs

Tiantian Wang, Djoko Budi Santoso, Kechao Wang and Xiaohong Su

Complexity, 2020, vol. 2020, 1-15

Abstract:

This paper presents an automatic grading method DGRADER, which handles complex multifile programs. Both the dynamic and the static grading support multifile program analysis. So, it can be an advantage to handle complex programming problem which requires more than one program file. Dynamic analysis takes advantage of object file linker in compilation to link complex multifile program. The static grading module consists of the following steps. Firstly, the program is parsed into abstract syntax tree, which is mapped into abstract syntax tree data map. Then, the information of preprocessor is used for linking external sources called in main program by complex multifile program linker-fusion algorithm. Next, standardization process is performed for problematic code removal, unused function removal, and function sequence ordering based on function call. Finally, program matching successfully tackles structure variance problem by previous standardization process and by simple tree matching using tag classifier. The novelty of the approach is that it handles complex multifile program analysis with flexible grading with consideration of modularity and big scale of programming problem complexity. The results have shown improvement in grading precision which gives reliable grading score delivered with intuitive system.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:3279053

DOI: 10.1155/2020/3279053

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