Symbolic execution and testing
Information and Software Technology
Grading student programs using ASSYST
SIGCSE '97 Proceedings of the twenty-eighth SIGCSE technical symposium on Computer science education
Symbolic execution and program testing
Communications of the ACM
Fully automatic assessment of programming exercises
Proceedings of the 6th annual conference on Innovation and technology in computer science education
Automated Software Engineering
Validating Use-Cases with the AsmL Test Tool
QSIC '03 Proceedings of the Third International Conference on Quality Software
Static analysis of students' Java programs
ACE '04 Proceedings of the Sixth Australasian Conference on Computing Education - Volume 30
Test input generation with java PathFinder
ISSTA '04 Proceedings of the 2004 ACM SIGSOFT international symposium on Software testing and analysis
TestEra: Specification-Based Testing of Java Programs Using SAT
Automated Software Engineering
A System to Generate Test Data and Symbolically Execute Programs
IEEE Transactions on Software Engineering
Experiments with test case generation and runtime analysis
ASM'03 Proceedings of the abstract state machines 10th international conference on Advances in theory and practice
Generalized symbolic execution for model checking and testing
TACAS'03 Proceedings of the 9th international conference on Tools and algorithms for the construction and analysis of systems
Symstra: a framework for generating object-oriented unit tests using symbolic execution
TACAS'05 Proceedings of the 11th international conference on Tools and Algorithms for the Construction and Analysis of Systems
Hi-index | 0.00 |
Automatic assessment of programming exercises is typically based on testing approach. Most automatic assessment frameworks execute tests and evaluate test results automatically, but the test data generation is not automated. No matter that such test data generation techniques and tools are available. We have researched how the Java PathFinder software model checker can be adopted to the specific needs of test data generation in automatic assessment. Practical problems considered are: How to derive test data directly from students' programs (i.e. without annotation) and how to visualize and how to abstract test data automatically for students? Interesting outcomes of our research are that with minor refinements generalized symbolic execution with lazy initialization (a test data generation algorithm implemented in PathFinder) can be used to construct test data directly from students' programs without annotation, and that intermediate results of the same algorithm can be used to provide novel visualizations of the test data.