A systematic review on cloud testing

A systematic literature review is presented that surveyed the topic of cloud testing over the period (2012-2017). Cloud testing can refer either to testing cloud-based systems (testing of the cloud), or to leveraging the cloud for testing purposes (testing in the cloud): both approaches (and their combination into testing of the cloud in the cloud) have drawn research interest. An extensive paper search was conducted by both automated query of popular digital libraries and snowballing, which resulted into the final selection of 147 primary studies. Along the survey a framework has been incrementally derived that classifies cloud testing research along six main areas and their topics. The paper includes a detailed analysis of the selected primary studies to identify trends and gaps, as well as an extensive report of the state of art as it emerges by answering the identified Research Questions. We find that cloud testing is an active research field, although not all topics have received so far enough attention, and conclude by presenting the most relevant open research challenges for each area of the classification framework.This paper describes research work mostly undertaken in the context of the European Project H2020 731535: ElasTest. This work has also been partially supported by: the Italian MIUR PRIN 2015 Project: GAUSS; the Regional Government of Madrid (CM) under project Cloud4BigData (S2013/ICE-2894) cofunded by FSE & FEDER; and the Spanish Government under project LERNIM (RTC-2016-4674-7) cofunded by the Ministry of Economy and Competitiveness, FEDER & AEI

AI-driven web API testing

Testing of web APIs is nowadays more critical than ever before, as they are the current standard for software integration. A bug in an organization’s web API could have a huge impact both in ternally (services relying on that API) and externally (third-party applications and end users). Most existing tools and testing ap proaches require writing tests or instrumenting the system under test (SUT). The main aim of this dissertation is to take web API testing to an unprecedented level of automation and thoroughness. To this end, we plan to apply artificial intelligence (AI) techniques for the autonomous detection of software failures. Specifically, the idea is to develop intelligent programs (we call them “bots”) ca pable of generating hundreds, thousands or even millions of test inputs and to evaluate whether the test outputs are correct based on: 1) patterns learned from previous executions of the SUT; and 2) knowledge gained from analyzing thousands of similar programs. Evaluation results of our initial prototype are promising, with bugs being automatically detected in some real-world APIs.Ministerio de Economía y Competitividad BELI (TIN2015-70560-R)Ministerio de Ciencia, Innovación y Universidades RTI2018-101204-B-C21 (HORATIO)Ministerio de Educación, Cultura y Deporte FPU17/0407

Adapting Quality Assurance to Adaptive Systems: The Scenario Coevolution Paradigm

From formal and practical analysis, we identify new challenges that self-adaptive systems pose to the process of quality assurance. When tackling these, the effort spent on various tasks in the process of software engineering is naturally re-distributed. We claim that all steps related to testing need to become self-adaptive to match the capabilities of the self-adaptive system-under-test. Otherwise, the adaptive system's behavior might elude traditional variants of quality assurance. We thus propose the paradigm of scenario coevolution, which describes a pool of test cases and other constraints on system behavior that evolves in parallel to the (in part autonomous) development of behavior in the system-under-test. Scenario coevolution offers a simple structure for the organization of adaptive testing that allows for both human-controlled and autonomous intervention, supporting software engineering for adaptive systems on a procedural as well as technical level.Comment: 17 pages, published at ISOLA 201

Cloud Platform for Research Crowdsourcing in Mobile Testing

Mobile application testing and testing over a cloud are two highly topical fields nowadays. Mobile testing presents specific test activities, including verification of an application against a variety of heterogeneous smartphone models and versions of operating systems (OS), build distribution and test team management, monitoring and user experience analytics of an application in production, etc. Cloud benefits are widely used to support all these activities. This study conducts in-depth analyses of existing cloud services for mobile testing and addresses their weaknesses regarding research purposes and testing needs of the critical and business-critical mobile applications.   During this study, a Cloud Testing of Mobile Systems (CTOMS) framework for effective research crowdsourcing in mobile testing was developed. The framework is presented as a lightweight and easily scalable distributed system that provides a cloud service to run tests on a variety of remote mobile devices. CTOMS provides implementation of two novel functionalities that are demanded by advanced investigations in mobile testing. First, it allows full multidirectional testing, which provides the opportunities to test an application on different devices and/or OS versions, and new device models or OS versions for their compatibility with the most popular applications in the market, or just legacy critical apps, etc. Second, CTOMS demonstrates the effective integration of the appropriate testing techniques for mobile development within such a service. In particular, it provides a user with suggestions about coverage of configurations to test on using combinatorial approaches like a base choice, pair-wise, and t-way. The current CTOMS version supports automated functional testing of Android applications and detection of defects in the user interface (UI). This has a great value because requirements for UI and user experience are high for any modern mobile application.    The fundamental analysis of possible test types and techniques using a system like CTOMS was conducted, and ways of possible enhancements and extensions of functionality for possible research are listed. The first case studies prove the work of implemented novel concepts, their usefulness, and their convenience for experiments in mobile testing. The overall work proves that a study of cloud mobile testing is feasible even with small research resources.  M.S

CLOCIS:Cloud-based conformance testing framework for IoT devices in the future internet

In recent years, the Internet of Things (IoT) has not only become ubiquitous in daily life but has also emerged as a pivotal technology across various sectors, including smart factories and smart cities. Consequently, there is a pressing need to ensure the consistent and uninterrupted delivery of IoT services. Conformance testing has thus become an integral aspect of IoT technologies. However, traditional methods of IoT conformance testing fall short of addressing the evolving requirements put forth by both industry and academia. Historically, IoT testing has necessitated a visit to a testing laboratory, implying that both the testing systems and testers must be co-located. Furthermore, there is a notable absence of a comprehensive method for testing an array of IoT standards, especially given their inherent heterogeneity. With a surge in the development of diverse IoT standards, crafting an appropriate testing environment poses challenges. To address these concerns, this article introduces a method for remote IoT conformance testing, underpinned by a novel conceptual architecture termed CLOCIS. This architecture encompasses an extensible approach tailored for a myriad of IoT standards. Moreover, we elucidate the methods and procedures integral to testing IoT devices. CLOCIS, predicated on this conceptual framework, is actualized, and to attest to its viability, we undertake IoT conformance testing and present the results. When leveraging CLOCIS, small and medium-sized enterprises (SMEs) and entities in the throes of IoT service development stand to benefit from a reduced time to market and cost-efficient testing procedures. Additionally, this innovation holds promise for IoT standardization communities, enabling them to champion their standards with renewed vigor

A self-integration testbed for decentralized socio-technical systems

The Internet of Things (IoT) comes along with new challenges for experimenting, testing, and operating decentralized socio-technical systems at large-scale. In such systems, autonomous agents interact locally with their users, and remotely with other agents to make intelligent collective choices. Via these interactions they self-regulate the consumption and production of distributed (common) resources, e.g., self-management of traffic flows and power demand in Smart Cities. While such complex systems are often deployed and operated using centralized computing infrastructures, the socio-technical nature of these decentralized systems requires new value-sensitive design paradigms; empowering trust, transparency, and alignment with citizens’ social values, such as privacy preservation, autonomy, and fairness among citizens’ choices. Currently, instruments and tools to study such systems and guide the prototyping process from simulation, to live deployment, and ultimately to a robust operation of a high Technology Readiness Level (TRL) are missing, or not practical in this distributed socio-technical context. This paper bridges this gap by introducing a novel testbed architecture for decentralized socio-technical systems running on IoT. This new architecture is designed for a seamless reusability of (i) application-independent decentralized services by an IoT application, and (ii) different IoT applications by the same decentralized service. This dual self-integration promises IoT applications that are simpler to prototype, and can interoperate with decentralized services during runtime to self-integrate more complex functionality, e.g., data analytics, distributed artificial intelligence. Additionally, such integration provides stronger validation of IoT applications, and improves resource utilization, as computational resources are shared, thus cutting down deployment and operational costs. Pressure and crash tests during continuous operations of several weeks, with more than 80K network joining and leaving of agents, 2.4M parameter changes, and 100M communicated messages, confirm the robustness and practicality of the testbed architecture. This work promises new pathways for managing the prototyping and deployment complexity of decentralized socio-technical systems running on IoT, whose complexity has so far hindered the adoption of value-sensitive self-management approaches in Smart Cities

Survey on Additive Manufacturing, Cloud 3D Printing and Services

Cloud Manufacturing (CM) is the concept of using manufacturing resources in a service oriented way over the Internet. Recent developments in Additive Manufacturing (AM) are making it possible to utilise resources ad-hoc as replacement for traditional manufacturing resources in case of spontaneous problems in the established manufacturing processes. In order to be of use in these scenarios the AM resources must adhere to a strict principle of transparency and service composition in adherence to the Cloud Computing (CC) paradigm. With this review we provide an overview over CM, AM and relevant domains as well as present the historical development of scientific research in these fields, starting from 2002. Part of this work is also a meta-review on the domain to further detail its development and structure

Planning as Optimization: Dynamically Discovering Optimal Configurations for Runtime Situations

The large number of possible configurations of modern software-based systems, combined with the large number of possible environmental situations of such systems, prohibits enumerating all adaptation options at design time and necessitates planning at run time to dynamically identify an appropriate configuration for a situation. While numerous planning techniques exist, they typically assume a detailed state-based model of the system and that the situations that warrant adaptations are known. Both of these assumptions can be violated in complex, real-world systems. As a result, adaptation planning must rely on simple models that capture what can be changed (input parameters) and observed in the system and environment (output and context parameters). We therefore propose planning as optimization: the use of optimization strategies to discover optimal system configurations at runtime for each distinct situation that is also dynamically identified at runtime. We apply our approach to CrowdNav, an open-source traffic routing system with the characteristics of a real-world system. We identify situations via clustering and conduct an empirical study that compares Bayesian optimization and two types of evolutionary optimization (NSGA-II and novelty search) in CrowdNav