Low-Code Platform Selection Using Interval Picture Fuzzy TOPSIS

Abstract

A low-code platform is a software development environment that enables users to build software applications rapidly with a minimal amount of coding. These platforms provide visual tools, drag-and-drop components, and pre-built logic, allowing users to easily design, develop, and deploy applications. Picture Fuzzy (PF) sets represent experts' evaluations by using the degrees of membership, non-membership, and hesitancy regarding an element's inclusion in a set. PF sets have been extended to Interval-Valued Picture Fuzzy Sets (IVPFSs) to offer greater flexibility in assigning these degrees. With this structure, interval-valued picture fuzzy multi-criteria decision-making methods yield more accurate and realistic results compared to other approaches, as they can better capture the opinions of experts. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is a widely used multi-criteria decision-making (MCDM) method for ranking and selecting the best alternative from a set of options based on multiple, often conflicting, criteria. In this study, we propose an evaluation and selection decision model for low-code platforms using the IVPFS-TOPSIS methodology.