Automated Software Test Data Generation
IEEE Transactions on Software Engineering
The chaining approach for software test data generation
ACM Transactions on Software Engineering and Methodology (TOSEM)
Automated program flaw finding using simulated annealing
Proceedings of the 1998 ACM SIGSOFT international symposium on Software testing and analysis
The concept of dynamic analysis
ESEC/FSE-7 Proceedings of the 7th European software engineering conference held jointly with the 7th ACM SIGSOFT international symposium on Foundations of software engineering
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Generating Software Test Data by Evolution
IEEE Transactions on Software Engineering
A System to Generate Test Data and Symbolically Execute Programs
IEEE Transactions on Software Engineering
An Evaluation of Random Testing
IEEE Transactions on Software Engineering
Embedding Intelligence into EDA Tools
Proceedings of the 2006 conference on Integrated Intelligent Systems for Engineering Design
Optimisation of software testing using Genetic Algorithm
International Journal of Artificial Intelligence and Soft Computing
Combining concept lattice with call graph for impact analysis
Advances in Engineering Software
Heuristic search-based approach for automated test data generation: a survey
International Journal of Bio-Inspired Computation
| Hi-index | 0.00 |
Automatic Test Generators (ATGs) are an important support tool for large-scale software development. Contemporary ATGs include JTest that does white box testing down to the method level only and black box testing if a specification exists, and AETG that tests pairwise interactions among input variables. The first automatic test generation approaches were static, based on symbolic execution.Korel suggested a dynamic approach to automatic test data generation using function minimization and directed search. A dynamic approach can handle array, pointer, function and other dynamic constructs more accurately than a static approach but it may also be more expensive since the program under test is executed repeatedly. Subsequent ATGs explored the use of genetic algorithms and simulated annealing. These ATGs address the problem of producing test data for low level code coverage like statement, branch and condition/decision and depend on branch function style instrumentation and/or the program graph.