Optimizing testing efficiency with error-prone path identification and genetic algorithms

We present a method for optimizing software testing efficiency by identifying the most error prone path clusters in a program. We do this by developing variable length genetic algorithms that optimize and select the software path clusters which are weighted with sources of error indexes. Although various methods have been applied to detecting and reducing errors in a whole system, there is little research into partitioning a system into smaller error prone domains for testing. Exhaustive software testing is rarely possible because it becomes intractable for even medium sized software. Typically only parts of a program can be tested, but these parts are not necessarily the most error prone. Therefore, we are developing a more selective approach to testing by focusing on those parts that are most likely to contain faults, so that the most error prone paths can be tested first. By identifying the most error prone paths, the testing efficiency can be increased.