Decision Support for Reducing Unnecessary IT Complexity of Application Architectures

As many companies exist for decades, their software systems and IT architectures grow massively with the companies requirements. To avoid failures due to changes to a productive system, new demands often lead to new solutions while neglecting to restructure existing software systems despite a similar purpose of use. As a consequence, the IT complexity of such software systems and IT architectures increases sharply accompanied by higher costs, reduced adaptability and increased effort for evolving and maintaining the entire IT landscape. Although there are applications that fulfill a company's requirements, there are also software solutions and variants of them that seems to be redundant. This causes unnecessary IT complexity, which is not essential for a company's goals and requirements. To identify and reduce this unnecessary part of IT complexity, we introduce an approach to support experts in decision making regarding these redundant artifacts. We provide a method to identify variability of application architectures (AAs) and an iterative decision process to determine and remove artifacts that are not required, which enable experts to reduce unnecessary IT complexity of given AAs. We show the feasibility of our approach by applying it to industrial data in a preliminary case study.