Unsupervised Mapping of Quantitative Measures to Qualitative Characteristics in Hierarchical Software Quality Assurance

Ensuring software quality is essential to the software development and deployment process. Evaluating software for known vulnerabilities and weaknesses is a method of assessing the quality of software. However, the existence of a large number of vulnerabilities and weaknesses can hinder decision-making and mitigation. To address this, hierarchical software quality models aggregate quantitative weakness measures into qualitative characteristics to simplify decision making. However, challenges exist when mapping quantitative measures to qualitative characteristics, especially when the relationship between measures and characteristics is ill-defined/unknown or when prior knowledge is absent, thus posing threats to the construct validity of the mapping. This paper presents a pseudo-label-based regression framework to generate qualitative values when given a set of quantitative measurements. To exemplify this research, we created a use case where we applied the framework to binary program analysis.