Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Automated Software Test Data Generation
IEEE Transactions on Software Engineering
Symbolic execution and testing
Information and Software Technology
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
The chaining approach for software test data generation
ACM Transactions on Software Engineering and Methodology (TOSEM)
Efficiently representing populations in genetic programming
Advances in genetic programming
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Programming and Data Structures: Genetic Programming + Data Structures = Automatic Programming!
Software Testability: The New Verification
IEEE Software
An Investigation of Niche and Species Formation in Genetic Function Optimization
Proceedings of the 3rd International Conference on Genetic Algorithms
Instrumenting Programs With Flag Variables For Test Data Search By Genetic Algorithms
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Improving Evolutionary Testing By Flag Removal
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Intelligent behavior as an adaptation to the task environment
Intelligent behavior as an adaptation to the task environment
IEEE Transactions on Software Engineering
Evolutionary testing in the presence of loop-assigned flags: a testability transformation approach
ISSTA '04 Proceedings of the 2004 ACM SIGSOFT international symposium on Software testing and analysis
Search-based software test data generation: a survey: Research Articles
Software Testing, Verification & Reliability
Data Dependence Based Testability Transformation in Automated Test Generation
ISSRE '05 Proceedings of the 16th IEEE International Symposium on Software Reliability Engineering
The species per path approach to SearchBased test data generation
Proceedings of the 2006 international symposium on Software testing and analysis
Software Testing, Verification & Reliability - UKTest 2005: The Third U.K. Workshop on Software Testing Research
Improving random test sets using the diversity oriented test data generation
Proceedings of the 2nd international workshop on Random testing: co-located with the 22nd IEEE/ACM International Conference on Automated Software Engineering (ASE 2007)
An Immune Genetic Algorithm for Software Test Data Generation
HIS '07 Proceedings of the 7th International Conference on Hybrid Intelligent Systems
Improving Evolutionary Testing in the Presence of Function-Assigned Flags
TAICPART-MUTATION '07 Proceedings of the Testing: Academic and Industrial Conference Practice and Research Techniques - MUTATION
Antirandom testing: a distance-based approach
VLSI Design
Searching for Cognitively Diverse Tests: Towards Universal Test Diversity Metrics
ICSTW '08 Proceedings of the 2008 IEEE International Conference on Software Testing Verification and Validation Workshop
Adaptive Random Testing: The ART of test case diversity
Journal of Systems and Software
Predicate expression cost functions to guide evolutionary search for test data
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Parameterized unit testing with Pex
TAP'08 Proceedings of the 2nd international conference on Tests and proofs
Program-operators to improve test data generation search
WSEAS Transactions on Computers
A multiple-population genetic algorithm for branch coverage test data generation
Software Quality Control
Orthogonal exploration of the search space in evolutionary test case generation
Proceedings of the 2013 International Symposium on Software Testing and Analysis
Diversity oriented test data generation using metaheuristic search techniques
Information Sciences: an International Journal
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Finding test data to cover structural test coverage criteria such as branch coverage is largely a manual and hence expensive activity. A potential low cost alternative is to generate the required test data automatically. Search-based test data generation is one approach that has attracted recent interest. This approach is based on the definition of an evaluation or cost function that is able to discriminate between candidate test cases with respect to achieving a given test goal. The cost function is implemented by appropriate instrumentation of the program under test. The candidate test is then executed on the instrumented program. This provides an evaluation of the candidate test in terms of the "distance" between the computation achieved by the candidate test and the computation required to achieve the test goal. Providing the cost function is able to discriminate reliably between candidate tests that are close or far from covering the test goal and the goal is feasible, a search process is able to converge to a solution, i.e., a test case that satisfies the coverage goal. For some programs, however, an informative cost function is difficult to define. The operations performed by these programs are such that the cost function returns a constant value for a very wide range of inputs. A typical example of this problem arises in the instrumentation of branch predicates that depend on the value of a Boolean-valued (flag) variable although the problem is not limited to programs that contain flag variables. Although methods are known for overcoming the problems of flag variables in particular cases, the more general problem of a near constant cost function has not been tackled. This paper presents a new heuristic for directing the search when the cost function at a test goal is not able to differentiate between candidate test inputs. The heuristic directs the search toward test cases that produce rare or scarce data states. Scarce inputs for the cost function are more likely to produce new cost values. The proposed method is evaluated empirically for a number of example programs for which existing methods are inadequate.