Automated Black-Box Boundary Value Detection
Felix Dobslaw, Robert Feldt, Francisco de Oliveira Neto

TL;DR
This paper introduces AutoBVA, an automated black-box method for detecting boundary values in software testing, using a program derivative metric to systematically identify and rank boundary candidates.
Contribution
It proposes a novel automated approach for boundary value detection that quantifies boundaryness and couples it with search algorithms for consistent results.
Findings
Effective boundary candidates identified with simple derivatives
Method works across diverse example programs
Broad sampling enhances boundary detection
Abstract
The input domain of software systems can typically be divided into sub-domains for which the outputs are similar. To ensure high quality it is critical to test the software on the boundaries between these sub-domains. Consequently, boundary value analysis and testing has been part of the toolbox of software testers for long and is typically taught early to students. However, despite its many argued benefits, boundary value analysis for a given specification or piece of software is typically described in abstract terms which allow for variation in how testers apply it. Here we propose an automated, black-box boundary value detection method to support software testers in systematic boundary value analysis with consistent results. The method builds on a metric to quantify the level of boundariness of test inputs: the program derivative. By coupling it with search algorithms we find and…
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Taxonomy
TopicsSoftware Testing and Debugging Techniques · Software Engineering Research · Software Reliability and Analysis Research
