Transformation-based diagnosis of student programs for programming tutoring systems

A robust technology that automates the diagnosis of students' programs is essential for programming tutoring systems. Such technology should be able to determine whether programs coded by a student are correct. If a student's program is incorrect, the system should be able to pinpoint erro...

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Veröffentlicht in:IEEE transactions on software engineering Jg. 29; H. 4; S. 360 - 384
Hauptverfasser: Xu, Songwen, Chee, Yam San
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.04.2003
IEEE Computer Society
Schlagworte:
Automatic control
Automatic programming
Automation
Computer errors
Computer programming
Computer Society
Data analysis
Diagnosis
Diagnostics
Error analysis
Error correction
Methods
PASCAL
Program processors
Programming
Programming languages
Robustness
Semantics
Software quality
Standardization
Students
Studies
Syntax
Tasks
Testing
Tutoring
ISSN:0098-5589, 1939-3520
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Zusammenfassung:A robust technology that automates the diagnosis of students' programs is essential for programming tutoring systems. Such technology should be able to determine whether programs coded by a student are correct. If a student's program is incorrect, the system should be able to pinpoint errors in the program as well as explain and correct the errors. Due to the difficulty of this problem, no existing system performs this task entirely satisfactorily, and this problem still hampers the development of programming tutoring systems. This paper describes a transformation-based approach to automate the diagnosis of students' programs for programming tutoring systems. Improved control-flow analysis and data-flow analysis are used in program analysis. Automatic diagnosis of student programs is achieved by comparing the student program with a specimen program at the semantic level after both are standardized. The approach was implemented and tested on 525 real student programs for nine different programming tasks. Test results show that the method satisfies the requirements stated above. Compared to other existing approaches to automatic diagnosis of student programs, the approach developed here is more rigorous and safer in identifying student programming errors. It is also simpler to make use of in practice. Only specimen programs are needed for the diagnosis of student programs. The techniques of program standardization and program comparison developed here may also be useful for research in the fields of program understanding and software maintenance.
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ISSN:0098-5589
1939-3520
DOI:10.1109/TSE.2003.1191799