Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Applying design of experiments to software testing: experience report
ICSE '97 Proceedings of the 19th international conference on Software engineering
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Detecting change in categorical data: mining contrast sets
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Symbolic execution and program testing
Communications of the ACM
CIL: Intermediate Language and Tools for Analysis and Transformation of C Programs
CC '02 Proceedings of the 11th International Conference on Compiler Construction
Mining Association Rules with Weighted Items
IDEAS '98 Proceedings of the 1998 International Symposium on Database Engineering & Applications
Data Mining for Very Busy People
Computer
DART: directed automated random testing
Proceedings of the 2005 ACM SIGPLAN conference on Programming language design and implementation
CUTE: a concolic unit testing engine for C
Proceedings of the 10th European software engineering conference held jointly with 13th ACM SIGSOFT international symposium on Foundations of software engineering
EXE: automatically generating inputs of death
Proceedings of the 13th ACM conference on Computer and communications security
Carving differential unit test cases from system test cases
Proceedings of the 14th ACM SIGSOFT international symposium on Foundations of software engineering
Dytan: a generic dynamic taint analysis framework
Proceedings of the 2007 international symposium on Software testing and analysis
Proceedings of the twenty-second IEEE/ACM international conference on Automated software engineering
Automatically finding the control variables for complex system behavior
Automated Software Engineering
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
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We describe a testing technique that uses information computed by symbolic execution of a program unit to guide the generation of inputs to the system containing the unit, in such a way that the unit's, and hence the system's, coverage is increased. The symbolic execution computes unit constraints at run-time, along program paths obtained by system simulations. We use machine learning techniques ---treatment learning and function fitting--- to approximate the system input constraints that will lead to the satisfaction of the unit constraints. Execution of system input predictions either uncovers new code regions in the unit under analysis or provides information that can be used to improve the approximation. We have implemented the technique and we have demonstrated its effectiveness on several examples, including one from the aerospace domain.