You have probably heard the terms eigenvalue and eigenvector before but probably aren’t sure exactly what these terms mean. They are staples of discrete mathematics courses required for computer science degrees at many universities. For years I’ve wondered if eigenvalues and eigenvectors have any application to software testing. I don’t have an answer. Let me explain what eigenvalues and eigenvectors are, and why I suspect they might be important to software testing. Suppose you are dealing with points x = (x,y) in the twodimensional plane. Then a 2×2 square matrix A implicitly defines a transformation of any point. Bear with me here. If there exists a vector x and a value λ (Greek letter lambda) that have the relationship Ax = λx then λ is called an eigenvalue and x is called an eigenvector. For example, if A = [ (6,3), (2,1) ], and λ = 0, and x = [1,2] the λ and x form an eigenvalueeigenvector pair. OK, so what? One of the surprising things about eigenvalue analysis is the amazing number of realworld problems they apply to in very unexpected ways. For example, using eigenvalue analysis it was shown that the optimal shape of a building support column is curvy, somewhat like the shape of a decorative stair post. Anyway, software testing is all about state changes which can be represented mathematically as vectors and matrices. There may be some interesting and useful ways that eigenvalues and eigenvectors can be applied to software testing.
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