document: 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 releases of the system . To study the feasibility of the proposed approach , we have applied a novel data mining algorithm called Info-Fuzzy Network ( IFN ) to execution data of a general-purpose code for solving partial differential equations . After being trained on a relatively small number of randomly generated input-output examples , the model constructed by the IFN algorithm has shown a clear capability to discriminate between correct and faulty versions of the program .
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