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On the Efficiency of Automated Testing by Marcel Böhme; Soumya Paul is a scholarly article available to read on EtoBox.

What is On the Efficiency of Automated Testing about?

The aim of automated program testing is to gain confidence about a program's correctness by sampling its input space. The sampling process can be either systematic or random. For every systematic testing technique the sampling is informed by the analysis of some program artefacts, like the specification, the source code (e.g., to achieve coverage), or even faulty versions of the program (e.g., mutation testing). This analysis incurs some cost. In contrast, random testing is unsystematic and does not sustain any analysis cost. In this paper, we investigate the theoretical efficiency of systematic versus random testing. First, we mathematically model the most effective systematic testing technique S0 in which every sampled test input strictly increases the "degree of confidence" and is subject to the analysis cost c. Note that the efficiency of S0 depends on c. Specifically, if we increase c, we also increase the time it takes S0 to establish the same degree of confidence. So, there exists a maximum analysis cost beyond which R is generally more efficient than S0. Given that we require the confidence that the program works correctly for x% of its input, we prove an upper bound on c o

Author

Marcel Böhme; Soumya Paul

Publisher

ACM

Published

2014

Language

EN