Enhancing Software Quality: A Multicriteria Decision-Making Approach for Identifying Defective Modules

As software technology continues to advance, ensuring quality and reliability of software products remains a significant challenge. One of the critical aspects of this challenge is the early detection of defects in software modules. In this paper, we research and analyze the use of multicriteria analysis in the detection of defects in software. In this method, a combination of two multi-criteria approaches and steps was used to obtain a quantitative analysis regarding the question addressed. In addition, we demonstrate the possibility of using a simpler method to assist in the detection of defects. To this end, a dataset made available by the National Aeronautics and Space Administration (NASA) was used, in addition, the Analytic Hierarchy Process (AHP) and Elimination and Choice Translating Reality (ELECTRE II) methods were used to classify the dataset source codes from the most complex to the least complex one. Results of the multicriteria analysis show that more complex codes are 70% more likely to have some type of defect, while the less complex ones are 50% less likely to have an error. This approach underscores the significance of employing multicriteria decision-making methods in addressing intricate problems, a theme resonant with current trends in computational intelligence research and its applications.