Planning Efficient Software Tests


Phadke, Madhav S.   (1997, ASQ)   Phadke Associates, Inc., Colts Neck, NJ

Annual Quality Congress, Orlando, FL    Vol. 51    No. 0
QICID: 10649    May 1997    pp. 991-998
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Article Abstract

Robust Testing is a method of selecting cases for a software testing program. Based on Taguchi's Robust Design method for hardware systems, Robust Testing can reduce testing costs while allowing fewer faults to reach the customer. Testing may account for 30% to 70% of software development resources, and so it is important that test cases be selected intelligently. Typical test planing methods include: one factor at a time testing, which does not detect faults in some multiple factor situations; exhaustive testing, which usually covers too many cases; the deductive/analytic method, which requires high planning skills; the random/intuitive method, which is unpredictable; and orthogonal array based Robust Testing. Of all these methods, Robust Testing is the most effective, as measured in number of faults detected per test. The orthogonal array consists of rows of test cases and columns of test parameters. The tests it identifies lead to: detection and isolation of all single mode faults; detection of all double mode faults; and detection of many multimode faults. This paper provides a geometric picture showing how test cases identified by Robust Testing are spread uniformly in the test domain. In a case study of electronic mail software, an original test plan covered 1,500 tests. However, a Robust Testing plan needed only 422 tests, which identified 41 faults. After fixing these faults and being in the field for two years, the product has generated no other faults.


Case study,Design of experiments (DOE),Testing,Taguchi method,Software quality

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