Robustness Metrics: Consolidating the multiple approaches to quantify Robustness

The robustness of a design has a major influence on how much the product's performance will vary and is of great concern to design, quality, and production engineers. While variability is always central to the definition of robustness, the concept does contain ambiguity, and although subtle, this ambiguity can have significant influence on the strategies used to combat variability, the way it is quantified and ultimately, the quality of the final design. In this contribution, the literature for robustness metrics was systematically reviewed. From the 108 relevant publications found, 38 metrics were determined to be conceptually different from one another. The metrics were classified by their meaning and interpretation based on the types of the information necessary to calculate the metrics. Four different classes were identified: (1) sensitivity robustness metrics; (2) size of feasible design space robustness metrics; (3) functional expectancy and dispersion robustness metrics; and (4) probability of compliance robustness metrics. The goal was to give a comprehensive overview of robustness metrics and guidance to scholars and practitioners to understand the different types of robustness metrics and to remove the ambiguities of the term robustness. By applying an exemplar metric from each class to a case study, the differences between the classes were further highlighted. These classes form the basis for the definition of four specific subdefinitions of robustness, namely the “robust concept,” “robust design,” “robust function,” and “robust product.”</jats:p

A Weighted Grid for Measuring Program Robustness

Robustness is a key issue for all the programs, especially safety critical ones. In the literature, Program Robustness is defined as “the degree to which a system or component can function correctly in the presence of invalid input or stressful environment” (IEEE 1990). Robustness measurement is the value that reflects the Robustness Degree of the program. In this thesis, a new Robustness measurement technique; the Robustness Grid, is introduced. The Robustness Grid measures the Robustness Degree for programs, C programs in this instance, using a relative scale. It allows programmers to find the program’s vulnerable points, repair them, and avoid similar mistakes in the future. The Robustness Grid is a table that contains Language rules, which is classified into categories with respect to the program’s function names, and calculates the robustness degree. The Motor Industry Software Reliability Association (MISRA) C language rules with the Clause Program Slicing technique will be the basis for the robustness measurement mechanism. In the Robustness Grid, for every MISRA rule, a score will be given to a function every time it satisfies or violates a rule. Furthermore, Clause program slicing will be used to weight every MISRA rule to illustrate its importance in the program. The Robustness Grid shows how much each part of the program is robust and effective, and assists developers to measure and evaluate the robustness degree for each part of a program. Overall, the Robustness Grid is a new technique that measures the robustness of C programs using MISRA C rules and Clause program slicing. The Robustness Grid shows the program robustness degree and the importance of each part of the program. An evaluation of the Robustness Grid is performed to show that it offers new measurements that were not provided before