AN INSIGHT INTO MICROSERVICES TESTING STRATEGIES Arvind Sundar, Technical Test Lead
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Software Architecture: Past, Present, Future
Software Architecture: Past, Present, Future Wilhelm Hasselbring 1 Introduction For large, complex software systems, the design of the overall system structure (the software architecture) is an essential challenge. The architecture of a software system defines that system in terms of components and connections among those components [55, 58]. It is not the design of that system which is more detailed. The architecture shows the correspondence between the requirements and the constructed system, thereby providing some rationale for the design decisions. This level of design has been addressed in a number of ways including informal diagrams and descriptive terms, module interconnection languages, and frameworks for systems that serve the needs of specific application domains. An architecture embodies decisions about quality properties. It represents the earliest opportunity for evaluating those decisions. Furthermore, reusability of components and services depends on how strongly coupled they are with other components in the system architecture. Performance, for instance, depends largely upon the complexity of the required coordination, in particular when the components are distributed via some network. The architecture is usually the first artifact to be examined when a programmer (particularly a maintenance programmer) unfamiliar with the system begins to work on it. Software architecture is often the first design artifact that represents decisions on how requirements of all types are to be achieved. As the manifestation of early design decisions, it represents design decisions that are hardest to change and hence most deserving of careful consideration. W. Hasselbring () Kiel University, Kiel, Germany e-mail: [email protected] © The Author(s) 2018 169 V. -
Model-Based Api Testing for Smt Solvers
MODEL-BASED API TESTING FOR SMT SOLVERS Aina Niemetz ?y, Mathias Preiner ?y, Armin Biere ? ?Johannes Kepler University, Linz, Austria yStanford University, USA SMT Workshop 2017, July 22 – 23 Heidelberg, Germany SMT Solvers highly complex usually serve as back-end to some application key requirements: correctness robustness performance −! full verification difficult and still an open question −! solver development relies on traditional testing techniques 1/22 Testing of SMT Solvers State-of-the-art: unit tests regression test suite grammar-based black-box input fuzzing with FuzzSMT [SMT’09] generational input fuzzer for SMT-LIB v1 patched for SMT-LIB v2 compliance generates random but valid SMT-LIB input especially effective in combination with delta debugging not possible to test solver features not supported by the input language This work: model-based API fuzz testing −! generate random valid API call sequences 2/22 Model-Based API fuzz testing −! generate random valid API call sequences Previously: model-based API testing framework for SAT [TAP’13] implemented for the SAT solver Lingeling allows to test random solver configurations (option fuzzing) allows to replay erroneous solver behavior −! results promising for other solver back-ends Here: model-based API testing framework for SMT lifts SAT approach to SMT implemented for the SMT solver Boolector tailored to Boolector for QF_(AUF)BV with non-recursive first-order lambda terms −! effective and promising for other SMT solvers −! more general approach left to future -
An Experimental Study on Microservices Based Edge Computing Platforms Qian Qu, Ronghua Xu, Seyed Yahya Nikouei, Yu Chen Dept
1 An Experimental Study on Microservices based Edge Computing Platforms Qian Qu, Ronghua Xu, Seyed Yahya Nikouei, Yu Chen Dept. of Electrical & Computer Engineering, Binghamton University, SUNY, Binghamton, NY 13902, USA fqqu2, rxu22, snikoue1, [email protected] Abstract—The rapid technological advances in the Internet of development and easy deployment among multiple service Things (IoT) allows the blueprint of Smart Cities to become providers. feasible by integrating heterogeneous cloud/fog/edge computing Because of many attractive features, such as good scalabil- paradigms to collaboratively provide variant smart services in our cities and communities. Thanks to attractive features like fine ity, fine granularity, loose coupling, continuous development, granularity and loose coupling, the microservices architecture and low maintenance cost, the microservices architecture has has been proposed to provide scalable and extensible services in emerged and gained a lot of interests both in industry and large scale distributed IoT systems. Recent studies have evaluated academic community [17], [21]. Compared to traditional SOAs and analyzed the performance interference between microser- in which the system is a monolithic unit, the microservices vices based on scenarios on the cloud computing environment. However, they are not holistic for IoT applications given the architecture divides an monolithic application into multiple restriction of the edge device like computation consumption and atomic microservices that run independently on distributed network capacity. This paper investigates multiple microservice computing platforms. Each microservice performs one specific deployment policies on edge computing platform. The microser- sub-task or service, which requires less computation resource vices are developed as docker containers, and comprehensive ex- and reduces the communication overhead. -
Devsecops DEVELOPMENT & DEVOPS INFRASTRUCTURE
DevSecOps DEVELOPMENT & DEVOPS INFRASTRUCTURE CREATE SECURE APPLICATIONS PARASOFT’S APPROACH - BUILD SECURITY IN WITHOUT DISRUPTING THE Parasoft provides tools that help teams begin their security efforts as DEVELOPMENT PROCESS soon as the code is written, starting with static application security test- ing (SAST) via static code analysis, continuing through testing as part of Parasoft makes DevSecOps possible with API and the CI/CD system via dynamic application security testing (DAST) such functional testing, service virtualization, and the as functional testing, penetration testing, API testing, and supporting in- most complete support for important security stan- frastructure like service virtualization that enables security testing be- dards like CWE, OWASP, and CERT in the industry. fore the complete application is fully available. IMPLEMENT A SECURE CODING LIFECYCLE Relying on security specialists alone prevents the entire DevSecOps team from securing software and systems. Parasoft tooling enables the BENEFIT FROM THE team with security knowledge and training to reduce dependence on PARASOFT APPROACH security specialists alone. With a centralized SAST policy based on in- dustry standards, teams can leverage Parasoft’s comprehensive docs, examples, and embedded training while the code is being developed. ✓ Leverage your existing test efforts for Then, leverage existing functional/API tests to enhance the creation of security security tests – meaning less upfront cost, as well as less maintenance along the way. ✓ Combine quality and security to fully understand your software HARDEN THE CODE (“BUILD SECURITY IN”) Getting ahead of application security means moving beyond just test- ✓ Harden the code – don’t just look for ing into building secure software in the first place. -
Comparing Service- Based Architectures
Comparing Service- based Architectures @neal4d nealford.com 1 agenda Micro Service-oriented Service-based 2 Service-oriented Architecture 3 origins: hubs System B System A System C 4 origins: hubs System B System A System C System D (ftp only) 5 origins: hubs System E (http only) System B System A System C System D (ftp only) 6 origins: hubs System E (http only) System B System A System C System D (ftp only) 7 origins: hubs System E (http only) System B Integration System A Hub System C System D (ftp only) 8 origins: hubs System E (http only) System B Integration System A Hub System C System D (ftp only) 9 origins: hubs System E (http only) System B Integration System A Hub System C System D (ftp only) 10 origins: hubs looks great, but what about single point of failure and performance bottleneck considerations? 11 orchestration hub intelligent hub service oriented architecture / enterprise service bus pattern 12 service-oriented architecture abstraction service taxonomy shared resources middleware interoperability 13 service-oriented architecture business services BS BS BS BS BS BS message bus process choreographer service orchestrator enterprise services ES ES ES ES ES ES application services AS infrastructure services IS 14 service-oriented architecture business services BS BS BS BS BS BS messageabstract bus enterprise-level coarse-grained process choreographer owned and defined by business users data represented as WSDL, BPEL, XML, etc. service orchestrator no implementation - only name, input, and output enterpriseAre we services -
Case Study on Building Data- Centric Microservices
Case Study on Building Data- Centric Microservices Part I - Getting Started May 26, 2020 | Version 1.0 Copyright © 2020, Oracle and/or its affiliates Public DISCLAIMER This document in any form, software or printed matter, contains proprietary information that is the exclusive property of Oracle. Your access to and use of this confidential material is subject to the terms and conditions of your Oracle software license and service agreement, which has been executed and with which you agree to comply. This document and information contained herein may not be disclosed, copied, reproduced or distributed to anyone outside Oracle without prior written consent of Oracle. This document is not part of your license agreement nor can it be incorporated into any contractual agreement with Oracle or its subsidiaries or affiliates. This document is for informational purposes only and is intended solely to assist you in planning for the implementation and upgrade of the product features described. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described in this document remains at the sole discretion of Oracle. Due to the nature of the product architecture, it may not be possible to safely include all features described in this document without risking significant destabilization of the code. TABLE OF CONTENTS DISCLAIMER INTRODUCTION ARCHITECTURE OVERVIEW Before You Begin Our Canonical Application -
Model-Based API Testing for SMT Solvers∗
Model-Based API Testing for SMT Solvers∗ Aina Niemetz, Mathias Preiner, and Armin Biere Institute for Formal Models and Verification Johannes Kepler University, Linz, Austria Abstract Verification back ends such as SMT solvers are typically highly complex pieces of soft- ware with performance, correctness and robustness as key requirements. Full verification of SMT solvers, however, is difficult due to their complex nature and still an open question. Grammar-based black-box input fuzzing proved to be effective to uncover bugs in SMT solvers but is entirely input-based and restricted to a certain input language. State-of-the- art SMT solvers, however, usually provide a rich API, which often introduces additional functionality not supported by the input language. Previous work showed that applying model-based API fuzzing to SAT solvers is more effective than input fuzzing. In this pa- per, we introduce a model-based API testing framework for our SMT solver Boolector. Our experimental results show that model-based API fuzzing in combination with delta debugging techniques is effective for testing SMT solvers. 1 Introduction State-of-the-art Satisfiability Modulo Theories (SMT) solvers are typically highly complex pieces of software, and since they usually serve as back-end to some application, the level of trust in this application strongly depends on the level of trust in the underlying solver. Full verification of SMT solvers, however, is difficult due to their complex nature and still an open question. Hence, solver developers usually rely on traditional testing techniques such as unit and regression tests. At the SMT workshop in 2009, in [10] Brummayer et al. -
Continuous Testing for Devops Evolving Beyond Simple Automation
Technical Whitepaper 1 Continuous Testing for DevOps Evolving Beyond Simple Automation INTRODUCTION DevOps represents a cultural shift that stresses collaboration be- on acceleration. Moreover, adopting a bona fide Continuous Testing tween the business, developers, and IT professionals. Software test process (more than just automated tests running regularly) helps automation can enhance these connections and help organizations promote all of the core pillars of DevOps: Culture, Automation, Lean, achieve desired SDLC acceleration, but it’s important to recognize Metrics, and Sharing. that automation is just one piece of the DevOps puzzle. In this paper, we’ll explore why and how Continuous Testing’s real- Since testing is often one of the greatest constraints in the SDLC, time objective assessment of an application’s business risks is a optimizing quality processes to allow testing to begin earlier, as well critical component of DevOps. as shrink the amount of testing required, can have a marked impact DEVOPS PRINCIPLES There are several key pieces to understanding DevOps revolutions and they are often brought about by a compelling event at an organization, such as a shift to agile. As organizations start to move into an agile development methodology, they start to uncover other processes that can be accelerated, such as delivery by DevOps and testing by Continuous Testing. The acceleration that is set in motion via agile makes it necessary to accelerate the release schedule. In order to ensure a successful release, an organization must adopt continuous testing to make sure the conveyer belt does not break down. The modernization maturity model has these three distinct phases: AGILE Agile software development is a different way of thinking about approaching the challenge of development time. -
Evaluating Service-Oriented and Microservice Architecture Patterns to Deploy Ehealth Applications in Cloud Computing Environment
applied sciences Article Evaluating Service-Oriented and Microservice Architecture Patterns to Deploy eHealth Applications in Cloud Computing Environment Huriviades Calderón-Gómez 1,2 , Luis Mendoza-Pittí 1,2 , Miguel Vargas-Lombardo 2,* , José Manuel Gómez-Pulido 1,3 , Diego Rodríguez-Puyol 3,4,5, Gloria Sención 6 and María-Luz Polo-Luque 3,7 1 Department of Computer Science, University of Alcalá, 28805 Alcalá de Henares, Spain; [email protected] (H.C.-G.); [email protected] (L.M.-P.); [email protected] (J.M.G.-P.) 2 e-Health and Supercomputing Research Group (GISES), Technological University of Panama, 0819-07289 Panama City, Panama 3 Department of Medicine and Medical Specialties, Ramón y Cajal Institute for Health Research (IRYCIS), 28034 Madrid, Spain; [email protected] (D.R.-P.); [email protected] (M.-L.P.-L.) 4 Foundation for Biomedical Research, Hospital Universitario Príncipe de Asturias, 28805 Alcalá de Henares, Spain 5 Department of Medicine and Medical Specialties, University of Alcalá, 28805 Alcalá de Henares, Spain 6 School of Medicine Autonomous, University of Santo Domingo, Santo Domingo 10105, Dominican Republic; [email protected] 7 Department of Nursing and Physiotherapy, University of Alcalá, 28801 Alcalá de Henares, Spain * Correspondence: [email protected] Citation: Calderón-Gómez, H.; Mendoza-Pittí, L.; Vargas-Lombardo, Abstract: This article proposes a new framework for a Cloud-based eHealth platform concept focused M.; Gómez-Pulido, J.M.; on Cloud computing environments, since current and emerging approaches using digital clinical Rodríguez-Puyol, D.; Sención, G.; history increasingly demonstrate their potential in maintaining the quality of the benefits in medical Polo-Luque, M.-L. -
Accelerate Software Innovation Through Continuous Quality
Accelerate Software Innovation Through Continuous Quality 1 Software quality is recognized as the #1 issue IT executives are trying to mitigate. Enterprise organizations strive to accelerate the delivery of a compelling user experience to their customers in order to drive revenue. Software quality is recognized as the #1 issue IT executives are trying to mitigate. QA teams know they have issues and are actively looking for solutions to save time, increase quality, improve security, and more. The most notable difficulties are in identifying the right areas to test, the availability of flexible and reliable test environments and test data, and the realization of benefits from automation. You may be facing many challenges with delivering software to meet the high expectations for quality, cost, and schedule driven by the business. An effective software testing strategy can address these issues. If you’re looking to improve your software quality while achieving your business goals, Parasoft can help. With over 30 years of making testing easier for our customers, we have the innovation you need and the experience you trust. Our extensive continuous quality suite spans every testing need and enables you to reach new heights. 3 QUALITY-FIRST APPROACH You can’t test quality into an application at the end of the software development life cycle (SDLC). You need to ensure that your software development process and practices put a priority on quality- driven development and integrate a comprehensive testing strategy to verify that the application’s functionality meets the requirements. Shift testing left to the start of your development process to bring quality to the forefront. -
Accelerate Your Digital Transformation with Continuous API Testing
Accelerate Your Digital Transformation with Continuous API Testing Continuously Improve DevOps, Agile Development, and CI/CD Pipelines with Continuous API Testing Powered by Contents Executive Summary 3 Quality-at-Speed 5 The Journey to Continuous API Testing 7 CI/CD Toolchain 9 Product Overview: API Fortress 10 Key Benefits 10 Competitive Edge: Continuous API Quality 12 How API Fortress Works 14 Core Features Basic Use Case 15 Advanced Use Case - “The Developer Flow” 16 API Risk Assessment 18 Contact Us Accelerate Your Digital Transformation with Continuous API Testing 2 Executive Summary Companies are being pushed to transform and innovate faster than ever. The need to stay ahead of new startups and technologies is leading to a mass shift in how work is done. This is best evidenced by the 36 60 percent spike from 2015 to 2017 in the adoption of Agile Development at enterprises according to Forrester. This has signaled a “critical mass realization” of the need for immediate digital 40 transformation. CIO senior writer, Clint Boulton, states, “Digital transformation [can be defined] as a euphemism for [a CIO’s] modernization efforts: 2017 moving from legacy architectures, on-premises systems and waterfall development to API-driven 20 microservices, the cloud and agile1.” At the heart of digital transformation is speed—the 2015 acceleration of release cycles for software features and APIs. With faster releases, developers can plan and build digital products in a nimble fashion that Widespread Adoption of responds more promptly to constantly changing Agile Development business cases. After all, customer preferences and loyalties are less stable than ever, and innovative competitors can disrupt instantly. -
Automate Your Jenkins CI/CD Pipeline with Automated REST and SOAP
API Fortress for Jenkins CI/CD Pipelines Automate your Jenkins CI/CD Pipeline with Automated REST and SOAP API Testing Deploy API Fortress for Jenkins CI/CD pipeline on- premises or hybrid cloud using Kubernetes or Docker. Or use the API Fortress API testing SaaS platform. Web Mobile App API Fill the Gap in Your Build Jenkins CI/CD Pipeline In just minutes, incorporate continuous API testing Test powered by API Fortress into your CI/CD pipeline. Deploy API Fortress on-premises or use our cloud platform. Test automation with immediate Deploy feedback directly into your Jenkins instance. Automate your Jenkins CI/CD Pipeline with Automated REST and SOAP API Testing With a CI/CD pipeline, the work of distributed teams come together in an automated flow to build, test, and deploy new code. That means rewriting the rules of how releases are built and tested. One of the first things that the Jenkins wiki (Jenkins Best Practices) tells newcomers to CI/CD is that “unit testing is often not enough to provide confidence [of desired quality].” The wiki then talks about the necessity to automate API testing throughout the lifecycle to ensure that all distributed teams are continually working with good services and data. Let’s take a closer look at those two stipulations of a CI/CD pipeline: 1. UI Testing Isn’t Enough: In our eBook, Nine Bugs That UI 2. Run API Testing Continuously: CI/CD pipelines produce Testing Could Not Diagnose, we tell nine horror stories from iterative releases so that services and mobile apps can real customers of API Fortress involving API vulnerabilities evolve quickly without increasing the number of bugs or and bugs that UI testing did not detect early in the lifecycle, vulnerabilities released.