Fuzzy Logic Testing Approach for Measuring Software Completeness

Due to advancements in science and technology, software is constantly evolving. To adapt to newly demanded requirements in a piece of software, software components are modified or developed. Measuring software completeness has been a challenging task for software companies. The uncertain and imprecise intrinsic relationships within software components have been unaddressed by researchers during the validation process. In this study, we introduced a new fuzzy logic testing approach for measuring the completeness of software. We measured the fuzzy membership value for each software component by a fuzzy logic testing approach called the fuzzy test. For each software component, the system response was tested by identifying which software components in the system required changes. Based on the measured fuzzy membership values for each software component, software completeness was calculated. The introduced approach scales the software completeness between zero and one. A software component with a complete membership value indicates that the software component does not require any modification. A non-membership value specifies that the existing software component is no longer required in the system or that a new software component is required to replace it. The partial membership value specifies that the software component requires few new functionalities according to the new software requirements. Software with a partial membership value requires partial restructuring and design recovery of its components. Symmetric design of software components reduces the complexity in the restructuring of software during modification. In the study, we showed that by using the introduced approach, high-quality software that is faultless, reliable, easily maintained, efficient, and cost-effective can be developed.

Perceptual Tuning Influences Rule Generalization: Testing Humans With Monkey-Tailored Stimuli

Comparative research investigating how nonhuman animals generalize patterns of auditory stimuli often uses sequences of human speech syllables and reports limited generalization abilities in animals. Here, we reverse this logic, testing humans with stimulus sequences tailored to squirrel monkeys. When test stimuli are familiar (human voices), humans succeed in two types of generalization. However, when the same structural rule is instantiated over unfamiliar but perceivable sounds within squirrel monkeys’ optimal hearing frequency range, human participants master only one type of generalization. These findings have methodological implications for the design of comparative experiments, which should be fair towards all tested species’ proclivities and limitations.

Diagnosing of a complex technical object in four-valued logic

This paper presents the essence of an investigation of a complex technical object with the use of four-valued logic. To this end, an intelligent diagnostic system (DIAG 2) is described. A special feature of this system was its capability of inferring k at {k = 4, 3, 2}, in which case the logic {k = 4} is applied. An important part of this work was to present the theoretical foundations describing the essence of inference in the four-valued logic contemplated. It was also pointed out that the basis for classification of states in the multiple-valued logic of the diagnostic system (DIAG 2) was the permissible interval of changes in the values of diagnostic signal features. Four-valued logic testing was applied to a system of wind turbine equipment. Keywords: technical diagnostics, diagnostic inference, multiple-valued logic, artificial intelligence