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Software Test Automation

Preface

This paper was published as part of a presentation for the QAI International Software Testing Conference (2000) and the SQE Test Automation Conference (2001).

Abstract

"Most of today’s software applications can work with large amounts of data. Whether you’re testing a database, a word processor, an Internet browser or a 3D-action game, you need to do some of your testing with very large, rich data sets. Maintaining oracles for those data sets is time consuming and expensive. But without a good oracle, a traditional data set is worthless for testing, because your tests’ accuracy can’t be verified."

"In this paper, we describe 'Self Verifying Data,' a method that stores both the test data and the oracle in a single data set. Combining the data and oracle solves most of the expensive maintenance problems and makes verification simpler. This method easily scales to data sets of any size and it can be used with manual and automated testing. You can adapt Self Verifying Data principles to the data you use to test almost any kind of software application."

About the Author

"As a life-long devotee to the study of the esoteric, Noel Nyman has made unique contributions to software test automation. When he joined Microsoft, Nyman discontinued his exhaustive study of the undocumented op-codes in 65xx series microprocessors and devoted his unusual talents to applying unstructured randomized stochastic testing to retail GUI applications (dumb monkey testing). He specializes in complex test using Rational Visual Test, and may be in large part for that product’s changing market penetration. An occasional presenter at software testing conferences and contributor to Software Testing and Quality Engineering magazine, Nyman continues his career at Microsoft as a Test Lead in Applications Compatibility."

Self Verifying Data - Testing without an Oracle (.rtf format) (817 kb)