A Methodology to Evaluate the Maintainability of Enterprise Application Integration Frameworks

Consulting companies that specialise in Enterprise Application Integration commonly require adapting existing frameworks to specific domains. Currently, there are many such frameworks available, most of which provide a materialisation of the well-known catalogue of patterns that was devised by Hohpe and Woolf. The decision regarding which framework must be used is critical since adaptation costs are not negligible. In this article, we report on a methodology that helps practitioners make a decision regarding which framework should be selected. To the best of our knowledge, there is not a previous methodology in the literature. Its salient features are that we have assembled a catalogue of measures regarding which there is a consensus in the literature that they are clearly aligned with the effort required to maintain a piece of software and we propose a statistically-sound method to produce a rank. We illustrate our proposal with an industrial case study that we have performed using five open-source frameworks.Ministerio de Educación y Ciencia TIN2007-64119Junta de Andalucía P07-TIC-2602Junta de Andalucía P08-TIC-4100Ministerio de Ciencia e Innovación TIN2008-04718-EMinisterio de Ciencia e Innovación TIN2010-21744Ministerio de Economía, Industria y Competitividad TIN2010-09809-EMinisterio de Ciencia e Innovación TIN2010-10811-EMinisterio de Ciencia e Innovación TIN2010-09988-EMinisterio de Economía y Competitividad TIN2013- 40848-

A Domain-Specific Language to Design Enterprise Application Integration Solutions

Enterprise Application Integration (EAI) solutions cope with two kinds of problems within software ecosystems, namely: keeping a number of application’s data in synchrony or creating new functionality on top of them. ESBs provide the technology required to implement a variety of EAI solutions at sensible costs, but they are still far from negligible. It is not surprising then that many authors are working on proposals to endow them with domain-specific tools to help software engineers reduce integration costs. In this article, we introduce a proposal called Guaraná. Its key features are as follows: it provides explicit support to devise EAI solutions using enterprise integration patterns by means of a graphical model; its DSL enables software engineers to have not only the view of a process, but also a view of the whole set of processes of which an EAI solution is composed; both processes and tasks can have multiple inputs and multiple outputs; and, finally, its runtime system provides a task-based execution model that is usually more efficient than the process-based execution models in current use. We have also implemented a graphical editor for our DSL and a set of scripts to transform our models into Java code ready to be compiled and executed. To set up a solution from this code a software engineer only needs to configure a number of adapters to communicate with the applications being integrated.Ministerio de Educación y Ciencia TIN2007-64119Junta de Andalucía P07-TIC-2602Junta de Andalucía P08-TIC-4100Ministerio de Ciencia e Innovación TIN2008-04718-EMinisterio de Ciencia e Innovación TIN2010-21744Ministerio de Ciencia y Tecnología TIN-2007-67843-C0

Cloud Configuration Modelling: a Literature Review from an Application Integration Deployment Perspective

Enterprise Application Integration has played an important role in providing methodologies, techniques and tools to develop integration solutions, aiming at reusing current applications and supporting the new demands that arise from the evolution of business processes in companies. Cloud-computing is part of a new reality in which companies have at their disposal a high capacity IT infrastructure at a low-cost, in which integration solutions can be deployed and run. The charging model adopted by cloud-computing providers is based on the amount of computing resources consumed by clients. Such demand of resources can be computed either from the implemented integration solution, or from the conceptual model that describes it. It is desirable that cloud-computing providers supply detailed conceptual models describing the variability of services and restrictions between them. However, this is not the case and providers do not supply the conceptual models of their services. The conceptual model of services is the basis to develop a process and provide supporting tools for the decision-making on the deployment of integration solutions to the cloud. In this paper, we review the literature on cloud configuration modelling, and compare current proposals based on a comparison framework that we have developed

A cloud-based integration platform for enterprise application integration: A Model-Driven Engineering approach

This article addresses major information systems integration problems, approaches, technologies, and tools within the context of Model-Driven Software Engineering. The Guaraná integration platform is introduced as an innovative platform amongst state-of-the-art technologies available for enterprises to design and implement integration solutions. In this article, we present its domain-specificmodeling language and its industrial cloud-based web development platform, which supports the design and implementation of integration solutions. A real-world case study is described and analyzed; then, we delve into its design and implementation, to finally disclose ten measures that empirically help estimating the amount of effort involved in the development of integration solutions

Advances in a DSL for Application Integration

Enterprise Application Integration (EAI) is currently one of the big challenges for Software Engineering. According to a recent report, for each dollar spent on developing an application, companies usually spend from 5 to 20 dollars to integrate it. In this paper, we propose a Domain Specific Language (DSL) for designing application integration solutions. It builds on our experience on two real-world integration projects

Towards optimisation of the number of threads in the integration platform engines using simulation models based on queueing theory

The use of applications is important to support the business processes of companies. However, most of these applications are not designed to function collaboratively. An integration solution orchestrates a group of applications, allowing data and functionality reuse. The performance of an integration solution depends on the optimum configuration of the number of threads in the runtime engine provided by the integration platforms. It is common that this configuration relies on the empirical knowledge of the software engineers, and it has a direct impact on the performance of integration solutions. The optimum number of threads may be found by means of simulation models. This article presents a methodology and a tool to assist with the generation of simulation models based on queuing theory, in order to find the optimum number of threads to execute an integration solution focusing on performance improvement. We introduce a case of study to demonstrate and experiments to evaluate our proposal

A proposal of Infrastructure-as-a-Service providers pricing model using linear regression

The increasing demand for companies to reduce the IT infrastructure (on-premise) are driving the adoption of a type of cloud computing category known as Infrastructure-as-a-Service (IaaS) to provide virtualized computing resources over the Internet. However, the choice of an instance of virtual machine whose configuration is able to meet the demands of the company is a complex task, especially concerning the price charged by providers. The lack of transparency of the mechanism of definition of the prices adopted by providers makes difficult the decision-making process, considering the influence of several factors on the final price of the instances, among them the geographical location of the data center. In view of this problem, this work presents a new proposal of price modeling of instances using multiple linear regression model, including the geographical location of the data center as one of variables of the model. To verify the accuracy of the regression model proposed, the calculated prices were compared to real prices charged by IaaS providers Amazon EC2, Google Cloud Platform e Microsoft Azure

Task Scheduling Optimization on Enterprise Application Integration Platforms Based on the Meta-heuristic Particle Swarm Optimization

Companies seek technological alternatives that provide competiti veness for their business processes. Among these alternatives, there are integration platforms that allow you to connect applications to your software ecosystems. These ecosystems are often composed of local applications and cloud computing services, such as SaaS and PaaS, and still, interact with social media. Integration platforms are specialized software that allows you to design, execute and monitor integration solutions, which connect functionality and data from different applications. Integration platforms typically provide a specific domain language, development toolkit, runtime engine, and monitoring tool. The efficiency of the engine in sche duling and performing integration tasks has a direct impact on the performance of a solution and this is one of the challenges faced by integration platforms. Our literature review has identified that integration engines adopt task scheduling algorithms based on the textit First-In-First-Out discipline, which may be inefficient. Therefore, it is appropriate to seek a task scheduling algorithm that optimizes engine performance, providing a positive impact on the performance of the integration solution in different scenarios. This article proposes an algorithm for task scheduling based on the meta-heuristic optimization technique, which assigns the tasks to the computational resources, considering the waiting time in the queue of ready tasks and the computational complexity of Each task in order to optimize the performance of the integration solution

An automatic generation of textual pattern rules for digital content filters proposal, using grammatical evolution genetic programming

AbstractThis work presents a conceptual proposal to address the problem of intensive human specialized resources that are nowadays required for the maintenance and optimized operation of digital contents filtering in general and anti-spam filtering in particular. The huge amount of spam, malware, virus, and other illegitimate digital contents distributed through network services, represents a considerable waste of physical and technical resources, experts and end users time, in continuous maintenance of anti-spam filters and deletion of spam messages, respectively. The problem of cumbersome and continuous maintenance required to keep anti-spam filtering systems updated and running in an efficient way, is addressed in this work by the means of genetic programming grammatical evolution techniques, for automatic rules generation, having SpamAssassin anti-spam system and SpamAssassin public corpus as the references for the automatic filtering customization

Stroke genetics informs drug discovery and risk prediction across ancestries

Previous genome-wide association studies (GWASs) of stroke - the second leading cause of death worldwide - were conducted predominantly in populations of European ancestry(1,2). Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis(3), and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach(4), we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry(5). Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries.</p