A Methodology for Operationalizing Enterprise Architecture and Evaluating Enterprise IT Flexibility

We propose a network-based methodology for analyzing a firm’s enterprise architecture. Our methodology uses “Design Structure Matrices” (DSMs) to capture the coupling between components in the architecture, including both business and technology-related elements. It addresses the limitations of prior work, in that it i) is based upon the actual architecture “in-use” as opposed to planned or “idealized” versions; ii) identifies discrete layers in a firm’s architecture associated with different technologies (e.g., applications, servers and databases); iii) reveals the main “flow of control” within an architecture (i.e., the set of inter-connected components); and iv) generates measures of architecture that can be used to predict performance. We demonstrate the application of our methodology using a novel dataset developed with the division of a large pharmaceutical firm. The dataset consists of all components in the enterprise architecture, the observed dependencies between them, and estimated costs of change for software applications within this architecture. We show that measures of the architecture derived from a DSM predict the cost of change for software applications. In particular, applications that are tightly coupled to other components in the architecture cost more to change. The analysis also shows that the measure of coupling that best predicts the cost of change is one that captures all direct and indirect connections between components (i.e., it captures the potential for changes to propagate via all possible paths between components). Our work represents an important step in making the concept of enterprise architecture more operational, thereby improving a firm’s ability to understand and improve its architecture over time

Visualizing and Measuring Enterprise Application Architecture: An Exploratory Telecom Case

We test a method for visualizing and measuring enterprise application architectures. The method was designed and previously used to reveal the hidden internal architectural structure of software applications. The focus of this paper is to test if it can also uncover new facts about the applications and their relationships in an enterprise architecture, i.e., if the method can reveal the hidden external structure between software applications. Our test uses data from a large international telecom company. In total, we analyzed 103 applications and 243 dependencies. Results show that the enterprise application structure can be classified as a core-periphery architecture with a propagation cost of 25%, core size of 34%, and architecture flow through of 64%. These findings suggest that the method could be effective in uncovering the hidden structure of an enterprise application architecture

Visualizing and Measuring Enterprise Architecture: An Exploratory BioPharma Case

We test a method that was designed and used previously to reveal the hidden internal architectural structure of software systems. The focus of this paper is to test if it can also uncover new facts about the components and their relationships in an enterprise architecture, i.e., if the method can reveal the hidden external structure between architectural components. Our test uses data from a biopharmaceutical company. In total, we analyzed 407 components and 1,157 dependencies. Results show that the enterprise structure can be classified as a core-periphery architecture with a propagation cost of 23%, core size of 32%, and architecture flow through of 67%. We also found that business components can be classified as control elements, infrastructure components as shared, and software applications as belonging to the core. These findings suggest that the method could be effective in uncovering the hidden structure of an enterprise architecture

Multivariate Unsupervised Machine Learning for Anomaly Detection in Enterprise Applications

Existing application performance management (APM) solutions lack robust anomaly detection capabilities and root cause analysis techniques, that do not require manual efforts and domain knowledge. In this paper, we develop a density-based unsupervised machine learning model to detect anomalies within an enterprise application, based upon data from multiple APM systems. The research was conducted in collaboration with a European automotive company, using two months of live application data. We show that our model detects abnormal system behavior more reliably than a commonly used outlier detection technique and provides information for detecting root causes

Designing an Agile Software Portfolio Architecture: The Impact of Coupling on Performance

The modern industrial corporation encompasses a myriad of different software applications, each of which must work in concert to deliver functionality to end-users. However, the increasingly complex and dynamic nature of competition in today’s product-markets dictates that this software portfolio be continually evolved and adapted, in order to meet new business challenges. This ability – to rapidly update, improve, remove, replace, and reimagine the software applications that underpin a firm’s competitive position – is at the heart of what has been called IT agility. Unfortunately, little work has examined the antecedents of IT agility, with respect to the choices a firm makes when designing its “Software Portfolio Architecture.” We address this gap in the literature by exploring the relationship between software portfolio architecture and IT agility at the level of the individual applications in the architecture. In particular, we draw from modular systems theory to develop a series of hypotheses about how different types of coupling impact the ability to update, remove or replace the software applications in a firm’s portfolio. We test our hypotheses using longitudinal data from a large financial services firm, comprising over 1,000 applications and over 3,000 dependencies between them. Our methods allow us to disentangle the effects of different types and levels of coupling. Our analysis reveals that applications with higher levels of coupling cost more to update, are harder to remove, and are harder to replace, than those with lower coupling. The measures of coupling that best explain differences in IT agility include all indirect dependencies between software applications (i.e., they include coupling and dependency relationships that are not easily visible to the system architect). Our results reveal the critical importance of software portfolio design decisions, in developing a portfolio of applications that can evolve and adapt over time

Exploring the Relationship between Architecture Coupling and Software Vulnerabilities: A Google Chrome Case

Employing software metrics, such as size and complexity, for predicting defects has been given a lot of attention over the years and proven very useful. However, the few studies looking at software architecture and vulnerabilities are limited in scope and findings. We explore the relationship between software vulnerabilities and component metrics (like code churn and cyclomatic complexity), as well as architecture coupling metrics (direct, indirect, and cyclic coupling). Our case is based on the Google Chromium project, an open source project that has not been studied for this topic yet. Our findings show a strong relationship between vulnerabilities and both component level metrics and architecture coupling metrics. Unfortunately, the effects of different types of coupling are somewhat hard to distinguish

A communal catalogue reveals Earth's multiscale microbial diversity

Our growing awareness of the microbial world's importance and diversity contrasts starkly with our limited understanding of its fundamental structure. Despite recent advances in DNA sequencing, a lack of standardized protocols and common analytical frameworks impedes comparisons among studies, hindering the development of global inferences about microbial life on Earth. Here we present a meta-analysis of microbial community samples collected by hundreds of researchers for the Earth Microbiome Project. Coordinated protocols and new analytical methods, particularly the use of exact sequences instead of clustered operational taxonomic units, enable bacterial and archaeal ribosomal RNA gene sequences to be followed across multiple studies and allow us to explore patterns of diversity at an unprecedented scale. The result is both a reference database giving global context to DNA sequence data and a framework for incorporating data from future studies, fostering increasingly complete characterization of Earth's microbial diversity.Peer reviewe