Developing a Framework to measure Enterprise Architecture Debts

Abstract: Technical debt is used to describe the changing or to maintain a system due to expedient shortcuts done during its development. In the context of the software development industry, technical debt is regarded as a critical issue in terms of the negative consequences such as increased software development cost, low product quality, decreased maintainability, and slowed progress to the long-term success of developing software. Code Smells are well informed in the domain of Technical Debt. They indicate to the common bad practices that may impair the future quality of the software system. By identifying those Code Smells, it is possible to give an improved solution or make the developers aware of a possible deficiency. I explore the premise that technical debt within the enterprise should be viewed as a tool. Extensible and Appropriate tools can check the Code Smells automatically and improve the quality assessment accordingly. However, in the field of Enterprise Architecture(EA), common bad habits in EA can be called EA Smells. EA Smells itself can be a component of EA Debt. Enterprise Architecture Debt can be defined as such a metric that depicts the deviation of the currently present state of an enterprise from a hypothetical ideal state.In this thesis, we introduce SmellCull as an extensible tool for capturing, tracking and managing Enterprise Architecture debt in the EA field. SmellCull allows measuring different kinds of Enterprise Architecture debts for EA Model. SmellCull is extensible since different types of Model can be integrated as input into the tool environment and provides developers with a lightweight tool to capture EA debt and make it easier to understand them indicating corresponding parts in the implementation. The tool is used to create propagation paths for the EA debt. This allows for an up-to-date and accurate presentation of EA debt to be upheld, which enables developer conducted implementation-level micromanagement as well as higher-level debt management.Since the tool is sophisticated enough, automated detection supports the design process and ongoing change of EAS(Enterprise Architecture System). This includes the strategic development of EAS with the corresponding roadmaps, as well as design assurance and performance monitoring to assess the quality of data in EA repositories and the compliance with certain standards defined by EA Smells. Due to the limited scope of master thesis, the tool will identify a few number of EA debt. At the end, some future work suggestions in the context of identifying more salable Enterprise Architecture Debts with this tool are given.