Boolean Differentiation for Formalizing Myers' Cause-Effect Graph Testing Technique

Abstract

Abstract-Cause-Effect Graph Testing is a popular technique used for almost four decades. Based on Boolean algebra, this technique assists deriving test cases from a given specification informally written in a natural language. The present paper suggests Boolean differentiation for formalizing this technique. The new approach is applied to an example, borrowed from G. Myers, for demonstrating and analyzing its features. Evaluations show that the new approach outperforms Myers' approach in terms of the detected faults per test cases.

Prioritizing MCDC test cases by spectral analysis of Boolean functions

“…In this empirical study, arbitrarily selected TCAS expressions numbered 1,4,9,19 and 20 are used 11 [34]. For each expression, all possible DC test suites are generated and average FEP values are calculated.…”

“…Priorities are adjusted as inversely proportional to the influences. Hence the valuation function should be designed as given in Equation (19).…”

“…In general, it is an effective method to test Boolean functions where applicable, like branch testing, requirement testing, or specification testing . For example, cause‐effect graphing is a popular requirement‐based testing introduced by Myers several decades ago and a recent study proposes the application of MCDC to generate test cases from these graphs . For further information on MCDC analysis, there exist some fundamental studies , including a systematic literature overview .…”

Forward-Propagation Approach for Generating Feasible and Minimum Test Case Suites From Cause-Effect Graph Specifications

“…Cause-effect graphs are a popular black-box testing technique because logical relations represent Boolean mathematical principles that are easy to understand and intuitive to use [4], as well as because this technique can be applied to safety-critical systems which have rigorous testing requirements (such as missile navigation systems described in [5]). For these reasons, the cause-effect graphing technique has been continually enhanced and generalized to a larger set of problems since from the 1970s, when it was first introduced in [6], to this day.…”

“…Recent studies such as [10] and [11] focus on the application of the cause-effect graphing technique on realtime safety-critical systems. This further amplifies the necessity of improving the quality of test case suites by using a novel algorithm which results in higher effect coverage (a cause-effect graphing metric introduced in [12]) and higher fault detection (a black-box metric commonly used for measuring the effectiveness of tests used in more recent works such as [4] and [9]) for black-box testing these types of systems.…”