Identifying Cloned Navigational Patterns in Web Applications

Web Applications are subject to continuous and rapid evolution. Often programmers indiscriminately duplicate Web pages without considering systematic development and maintenance methods. This practice creates code clones that make Web Applications hard to maintain and reuse. We present an approach to identify duplicated functionalities in Web Applications through cloned navigational pattern analysis. Cloned patterns can be generalized in a reengineering process, thus to simplify the structure and future maintenance of the Web Applications. The proposed method first identifies pairs of cloned pages by analyzing similarity at structure, content, and scripting code. Two pages are considered clones if their similarity is greater than a given threshold. Cloned pages are then grouped into clusters and the links connecting pages of two clusters are grouped too. An interconnection metric has been defined on the links between two clusters to express the effort required to reengineer them as well as to select the patterns of interest. To further reduce the comprehension effort, we filter out links and nodes of the clustered navigational schema that do not contribute to the identification of cloned navigational patterns. A tool supporting the proposed approach has been developed and validated in a case study

Community landscapes: an integrative approach to determine overlapping network module hierarchy, identify key nodes and predict network dynamics

Background: Network communities help the functional organization and evolution of complex networks. However, the development of a method, which is both fast and accurate, provides modular overlaps and partitions of a heterogeneous network, has proven to be rather difficult. Methodology/Principal Findings: Here we introduce the novel concept of ModuLand, an integrative method family determining overlapping network modules as hills of an influence function-based, centrality-type community landscape, and including several widely used modularization methods as special cases. As various adaptations of the method family, we developed several algorithms, which provide an efficient analysis of weighted and directed networks, and (1) determine pervasively overlapping modules with high resolution; (2) uncover a detailed hierarchical network structure allowing an efficient, zoom-in analysis of large networks; (3) allow the determination of key network nodes and (4) help to predict network dynamics. Conclusions/Significance: The concept opens a wide range of possibilities to develop new approaches and applications including network routing, classification, comparison and prediction.Comment: 25 pages with 6 figures and a Glossary + Supporting Information containing pseudo-codes of all algorithms used, 14 Figures, 5 Tables (with 18 module definitions, 129 different modularization methods, 13 module comparision methods) and 396 references. All algorithms can be downloaded from this web-site: http://www.linkgroup.hu/modules.ph

Scalable invoice-based B2B payments with microservices

Paying by invoice has several advantages for businesses over conventional payment methods such as Debit/Credit cards. An invoice not only allows a buyer to make the purchase on credit but also contains the tax information for each purchased item. Businesses need to save this information in their financial records and report it to the authorities. The core challenge in an invoice-based payment method is the ability to make an accurate credit decision for a given purchase. Such a credit decision requires information about the buying company such as their credit rating. The company information is gathered in real time from different third-party sources. In this context, Enterpay Oy provides an invoiced-based B2B payment solution and is growing its payment service to European countries. In order to support this expansion, Enterpay needs to develop new capabilities such as the ability to detect fraudulent purchases. These new features require the application architecture to be flexible in terms of technology. For example, different components of the service should be built with the most suited programming language, libraries, and frameworks. The goal of this thesis is to enable efficient scaling and high availability for Enterpay's payment service. Thus, we have migrated from a monolithic a microservice-based architecture. This transition allows us to choose the best suited technology for the business case of the given microservice. We extracted various modules from the original monolithic application, which have different scalability criteria. We built these modules as Docker containers, which run as independent microservices. We used Kubernetes as the container orchestration framework and deployed the microservice in Amazon Web Services (AWS). Finally, we conducted experiments to measure the performance of the service with the new architecture. We found that this architecture not only scales faster but also recovers from instance failures quicker than the previous solution. Additionally, we noticed that the average response time of service request is similar in both architectures. Finally, we observed that new microservices can be built using different technology stack and deployed conveniently in the same Kubernetes cluster

TRIPLE MODULAR REDUNDANCY APPROACH FOR INTERNET CONNECTIVITY

This paper discusses the issue of providing tolerance to hardware and software faults in Internet system through triplicate application servers.  A replication scheme (TMR) is presented, and a detailed dependability analysis of this scheme is performed.  The proposed model was designed mainly for fault-tolerant Internet connectivity system where faults will not impair the continuous services rendered by the Internet system, thereby exhibiting highly varying and dynamic system characteristics.  A major feature of the model under consideration is to attempt the adaptive connections of the existing Triple Modular Redundancy (TMR) scheme for the execution of redundant modules for a required level of fault tolerance.  &nbsp

Software Engineering for Real-Time NoSQL Systems-centric Big Data Analytics

Recent advances in Big Data Analytics (BDA) have stimulated widespread interest to integrate BDA capabilities into all aspects of a business. Before these advances, companies have spent time optimizing the software development process and best practices associated with application development. These processes include project management structures and how to deliver new features of an application to its customers efficiently. While these processes are significant for application development, they cannot be utilized effectively for the software development of Big Data Analytics. Instead, some practices and technologies enable automation and monitoring across the full lifecycle of productivity from design to deployment and operations of Analytics. This paper builds on those practices and technologies and introduces a highly scalable framework for Big Data Analytics development operations. This framework builds on top of the best-known processes associated with DevOps. These best practices are then shown using a NoSQL cloud-based platform that consumes and processes structured and unstructured real-time data. As a result, the framework produces scalable, timely, and accurate analytics in real-time, which can be easily adjusted or enhanced to meet the needs of a business and its customers