The data mining approach to automated software testing

In today's industry, the design of software tests is mostly based on the testers' expertise, while test automation tools are limited to execution of pre-planned tests only. Evaluation of test outputs is also associated with a considerable effort by human testers who often have imperfect knowledge of the requirements specification. Not surprisingly, this manual approach to software testing results in heavy losses to the world's economy. The costs of the so-called "catastrophic" software failures (such as Mars Polar Lander shutdown in 1999) are even hard to measure. In this paper, we demonstrate the potential use of data mining algorithms for automated induction of functional requirements from execution data. The induced data mining models of tested software can be utilized for recovering missing and incomplete specifications, designing a minimal set of regression tests, and evaluating the correctness of software outputs when testing new, potentially flawed rele...

• Type: Conference
• Year: 2003
• Where: KDD
• Authors: Mark Last, Menahem Friedman, Abraham Kandel
• Keywords: Data Mining, Data Mining Algorithm, Data Mining Algorithms, KDD 2003