Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
Linear programming and network flows (2nd ed.)
Linear programming and network flows (2nd ed.)
Software testing techniques (2nd ed.)
Software testing techniques (2nd ed.)
Automated Software Test Data Generation
IEEE Transactions on Software Engineering
Automated test data generation using an iterative relaxation method
SIGSOFT '98/FSE-6 Proceedings of the 6th ACM SIGSOFT international symposium on Foundations of software engineering
UNA Based Iterative Test Data Generation and its Evaluation
ASE '99 Proceedings of the 14th IEEE international conference on Automated software engineering
Automated test data generation using iterative relaxation methods
Automated test data generation using iterative relaxation methods
Experiments with UNA for solving linear constraints in real variables
Proceedings of the 2004 ACM symposium on Applied computing
Execution generated test cases: how to make systems code crash itself
SPIN'05 Proceedings of the 12th international conference on Model Checking Software
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In a series of articles Gupta et al. develop a framework for automatic test data generation for computer programs. In general, their approach consists of a branch predicate collector, which derives a system of linear inequalities representing the branch predicates for a given path in the program. This system is solved using a solving technique of theirs called the Unified Numerical Approach (UNA) [5, 7]. In this paper we show that in contrast to traditional optimization methods the UNA is not bounded by the size of the solved system. Instead it depends on how input is composed. That is, even for very simple systems consisting of one variable we can easily get more than a thousand iterations. We will also give a formal proof that UNA does not always find a mixed integer solution when there is one. Finally, we suggest using some traditional optimization method instead, like the simplex method in combination with branch-and-bound and/or a cutting-plane algorithm as a constraint solver.