IT Best Practices

Developing a Software Certification for Quality Assurance

Fuzzy Logic for Quality Assurance

In this paper by Dr. Mohamed Shaheen Elgamel and Prof. Doctor Ezzat A. Korani &Kariman R. El Helow, the process of determining and developing a “fuzzy logic” model to combine attributes of fuzzy logic and existing processes is explored.

One of the major problems facing software development, according to the paper, is “a lack of direct measures for activities that are carried out in organizations.” These measurements can help not only in reducing budget costs, but also help in increasing overall quality and expected results. Quality is an essential element in a company’s products, and quality has become more important in today’s business environment. Visibility, volume, and customer interaction has resulted in new pressures to make quality the highest priority—and developing a concrete way to certify quality is now a very real concern of many businesses.  To this end, the authors of the paper explain how a fuzzy logic approach was used in order to develop a procedure to develop this certification:

The fuzzy logic approach is a procedure that consists of analyzing and defining problems, creating sets and logical relationships, converting information to what are called fuzzy sets, and interpreting all parts of a model. It is a mathematical tool for dealing with uncertainty and also to provide a technique to deal with imprecision and information granularity. It can use a number of criteria to determine whether a fuzzy-logic approach would lend itself to solve a specific problem by specifying prerequisites. These prerequisites include the level of ambiguity of the data (determined mathematically) and the required accuracy of the output.

Measurements and Certification

The paper then goes on to share the selected attributes that were measured, the process of measurement, and the overall outcome of the effort. The model merged measurement attributes for software certification while using the fuzzy logic model, allowing for more accurate results despite ambiguous data.

To read about the entire effort, read the full report here:

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