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Software testing using model programs
Author(s) -
Manolache L. I.,
Kourie D. G.
Publication year - 2001
Publication title -
software: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.437
H-Index - 70
eISSN - 1097-024X
pISSN - 0038-0644
DOI - 10.1002/spe.409
Subject(s) - computer science , oracle , non regression testing , keyword driven testing , outcome (game theory) , context (archaeology) , regression testing , test strategy , white box testing , functional testing , manual testing , software , model based testing , acceptance testing , software reliability testing , software testing , test (biology) , reliability engineering , test case , software engineering , software quality , software development , programming language , machine learning , software construction , engineering , mathematics , regression analysis , paleontology , mathematical economics , testability , biology
A strategy described as ‘testing using M model programs’ (abbreviated to ‘ M ‐mp testing’) is investigated as a practical alternative to software testing based on manual outcome prediction. A model program implements suitably selected parts of the functional specification of the software to be tested. The M ‐mp testing strategy requires that M ( M ≥ 1) model programs as well as the program under test, P , should be independently developed. P and the M model programs are then subjected to the same test data. Difference analysis is conducted on the outputs and appropriate corrective action is taken. P and the M model programs jointly constitute an approximate test oracle. Both M ‐mp testing and manual outcome prediction are subject to the possibility of correlated failure. In general, the suitability of M ‐mp testing in a given context will depend on whether building and maintaining model programs is likely to be more cost effective than manually pre‐calculating P 's expected outcomes for given test data. In many contexts, M ‐mp testing could also facilitate the attainment of higher test adequacy levels than would be possible with manual outcome prediction. A rigorous experiment in an industrial context is described in which M ‐mp testing (with M = 1) was used to test algorithmically complex scheduling software. In this case, M ‐mp testing turned out to be significantly more cost effective than testing based on manual outcome prediction. Copyright © 2001 John Wiley & Sons, Ltd.