On the idea of using nature-inspired metaphors to improve software testing

The number of software defects found in software applications today costs users and companies billions of dollars annually. In general, these defects occur due to an inadequate software development process that does not give the necessary importance to testing. Another contributor to these costs is the lack of adequate automated tools that can find "bugs" that would not otherwise be verified by experts. This paper looks at the combinatorial characteristics of the problem of testing - tools essentially search among all test cases for those that are promising (find existing bugs in the application) - and the effect that abstractions inspired by nature, such as genetic algorithms and swarm intelligence, may have in the construction of more ''intelligent" testing tools. The paper argues that these abstractions may be used to construct automated tools that are more powerful, less biased, and able to incorporate expect knowledge while maintaining the ability to discover now, never-thought-of software defects.