After studying this chapter you will be able to: Understand the rationale for systematic (nonrandom) selection of test cases; understand why functional test selection is a primary, base-line technique; distinguish functional testing from other systematic testing techniques. | Functional testing (c) 2007 Mauro Pezzè & Michal Young Ch 10, slide 1 Learning objectives • Understand the rationale for systematic (nonrandom) selection of test cases – Understand the basic concept of partition testing and its underlying assumptions • Understand why functional test selection is a primary, base-line technique – Why we expect a specification-based partition to help select valuable test cases • Distinguish functional testing from other systematic testing techniques (c) 2007 Mauro Pezzè & Michal Young Ch 10, slide 2 Functional testing • Functional testing: Deriving test cases from program specifications • Functional refers to the source of information used in test case design, not to what is tested • Also known as: – specification-based testing (from specifications) – black-box testing (no view of the code) • Functional specification = description of intended program behavior – either formal or informal (c) 2007 Mauro Pezzè & Michal Young Ch 10, slide 3 Systematic vs Random Testing • Random (uniform): – Pick possible inputs uniformly – Avoids designer bias • A real problem: The test designer can make the same logical mistakes and bad assumptions as the program designer (especially if they are the same person) – But treats all inputs as equally valuable • Systematic (non-uniform): – Try to select inputs that are especially valuable – Usually by choosing representatives of classes that are apt to fail often or not at all • Functional testing is systematic testing (c) 2007 Mauro Pezzè & Michal Young Ch 10, slide 4 Why Not Random? • Non-uniform distribution of faults • Example: Java class “roots” applies quadratic equation Incomplete implementation logic: Program does not properly handle the case in which b2 - 4ac =0 and a=0 Failing values are sparse in the input space — needles in a very big haystack. Random sampling is unlikely to choose a= and b= (c) 2007 Mauro Pezzè & Michal Young Ch 10, slide .
