In the software development industry, technical debt is regarded as a critical issue in term 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. However, despite the vast research contributions in technical debt management for software engineering, the idea of technical debt fails to provide a holistic consideration to include both IT and business aspects. Further, implementing an enterprise architecture (EA) project might not always be a success due to uncertainty and unavailability of resources. Therefore, we relate the consequences of EA implementation failure with a new metaphor --Enterprise Architecture Debt (EA Debt). We anticipate that the accumulation of EA Debt will negatively influence EA quality, also expose the business into risk.
Addicks and Appelrath @cite_5 searched for key figures and their metrics in order to unitize the quality assessment of an application landscape. This approach can be applied to the EA as a whole, because business processes for example influence the application's quality. They stated that all key figures must at least fit one of the following three conditions: (a) it must be used for indications of applications and be based on the application’s attributes, (b) it must be an indicator of an application and its value is determined by attributes and relations from other EA artifacts (the applications’ enterprise context), and (c) it must indicate a landscape’s quality and therefore use all attributes of applications and their enterprise context'' @cite_5 .
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This contribution presents a method to evaluate business applications. The method allows for using artifacts of enterprise architectures. Artifacts like business processes or hardware can exert influence on the application's quality and thus have to be regarded. A central aspect is to modularize the method's basic components, which are key figures and their metrics. The modularization allows for flexible usage and customization to fit the heterogeneity of different organizations. To make the method more intuitive, a key figure is encapsulated by a fuzzy logic based component denoted as criterion. A criterion addresses a certain aspect to evaluate an application and allows for using linguistic terms to represent a key figure. This article presents a model for projects that have to adhere to Enterprise Architecture (EA) in order for their results to be aligned with the broader organization. The model features project artifacts (i.e. deliverables such as Software Architecture Documents), their mutual relationships, their relationship with EA, and the processes in which they are created and tested on conformance. We start with applying Activity Theory to show the crucial mediating role that artifacts have in projects and to identify and justify the new EA-related artifacts we introduce. We subsequently incorporate these findings and existing best practices in a standard systems development approach in order to create a practical model that projects can apply for EA conformance. This model features both new, dedicated EA artifacts, and well-known existing artifacts of which we describe the way they should conform to EA. Finally, two action research studies are used to empirically support the model. Top-k dominating queries combine the natural idea of selecting the k best items with a comprehensive “goodness” criterion based on dominance. A point p1 dominates p2 if p1 is as good as p2 in all attributes and is strictly better in at least one. Existing works address the problem in settings where data objects are multidimensional points. However, there are domains where we only have access to the distance between two objects. In cases like these, attributes reflect distances from a set of input objects and are dynamically generated as the input objects change. Consequently, prior works from the literature cannot be applied, despite the fact that the dominance relation is still meaningful and valid. For this reason, in this work, we present the first study for processing top-k dominating queries over distance-based dynamic attribute vectors, defined over a metric space. We propose four progressive algorithms that utilize the properties of the underlying metric space to efficiently solve the problem and present an extensive, comparative evaluation on both synthetic and real-world datasets. Automated tools for understanding application behavior and its changes during the application lifecycle are essential for many performance analysis and debugging tasks. Application performance issues have an immediate impact on customer experience and satisfaction. A sudden slowdown of enterprise-wide application can effect a large population of customers, lead to delayed projects, and ultimately can result in company financial loss. Significantly shortened time between new software releases further exacerbates the problem of thoroughly evaluating the performance of an updated application. Our thesis is that online performance modeling should be a part of routine application monitoring. Early, informative warnings on significant changes in application performance should help service providers to timely identify and prevent performance problems and their negative impact on the service. We propose a novel framework for automated anomaly detection and application change analysis. It is based on integration of two complementary techniques: (i) a regression-based transaction model that reflects a resource consumption model of the application, and (ii) an application performance signature that provides a compact model of runtime behavior of the application. The proposed integrated framework provides a simple and powerful solution for anomaly detection and analysis of essential performance changes in application behavior. An additional benefit of the proposed approach is its simplicity: It is not intrusive and is based on monitoring data that is typically available in enterprise production environments. The introduced solution further enables the automation of capacity planning and resource provisioning tasks of multitier applications in rapidly evolving IT environments.