Model-based Testing in Cloud Brokerage Scenarios

In future Cloud ecosystems, brokers will mediate between service providers and consumers, playing an increased role in quality assurance, checking services for functional compliance to agreed standards, among other aspects. To date, most Software-as-a-Service (SaaS) testing has been performed manually, requiring duplicated effort at the development, certification and deployment stages of the service lifecycle. This paper presents a strategy for achieving automated testing for certification and re-certification of SaaS applications, based on the adoption of simple state-based and functional specifications. High-level test suites are generated from specifications, by algorithms that provide the necessary and sufficient coverage. The high-level tests must be grounded for each implementation technology, whether SOAP, REST or rich-client. Two examples of grounding are presented, one into SOAP for a traditional web service and the other into Selenium for a SAP HANA rich-client application. The results demonstrate good test coverage. Further work is required to fully automate the grounding

Model-Based Testing for Composite Web Services in Cloud Brokerage Scenarios

Cloud brokerage is an enabling technology allowing various services to be merged together for providing optimum quality of service for the end-users. Within this collection of composed services, testing is a challenging task which brokers have to take on to ensure quality of service. Most Software-as-a-Service (SaaS) testing has focused on high-level test generation from the functional specification of individual services, with little research into how to achieve sufficient test coverage of composite services. This paper explores the use of model-based testing to achieve testing of composite services, when two individual web services are tested and combined. Two example web services – a login service and a simple shopping service – are combined to give a more realistic shopping cart service. This paper focuses on the test coverage required for testing the component services individually and their composition. The paper highlights the problems of service composition testing, requiring a reworking of the combined specification and regeneration of the tests, rather than a simple composition of the test suites; and concludes by arguing that more work needs to be done in this area

Structure and Feedback in Cloud Service API Fuzzing

Over the last decade, we have witnessed an explosion in cloud services for hosting software applications (Software-as-a-Service), for building distributed services (Platform- as-a-Service), and for providing general computing infrastructure (Infrastructure-as-a- Service). Today, most cloud services are programmatically accessed through Application Programming Interfaces (APIs) that follow the REpresentational State Trans- fer (REST) software architectural style and cloud service developers use interface-description languages to describe and document their services. My thesis is that we can leverage the structured usage of cloud services through REST APIs and feedback obtained during interaction with such services in order to build systems that test cloud services in an automatic, efficient, and learning-based way through their APIs. In this dissertation, I introduce stateful REST API fuzzing and describe its implementation in RESTler: the first stateful REST API fuzzing system. Stateful means that RESTler attempts to explore latent service states that are reachable only with sequences of multiple interdependent API requests. I then describe how stateful REST API fuzzing can be extended with active property checkers that test for violations of desirable REST API security properties. Finally, I introduce Pythia, a new fuzzing system that augments stateful REST API fuzzing with coverage-guided feedback and learning-based mutations

Cloud-Based Implementation of an Automatic Coverage Estimation Methodology for Self-Organising Network

UIDB/EEA/50008/2020One of the main concerns of telecommunications operators is related to network coverage. A weak coverage can lead to a performance decrease, not only in the user experience, when using the operators' services, such as multimedia streaming, but also in the overall Quality of Service. This paper presents a novel cloud-based framework of a semi-empirical propagation model that estimates the coverage in a precise way. The novelty of this model is that it is automatically calibrated by using drive test measurements, terrain morphology, buildings in the area, configurations of the network itself and key performance indicators, automatically extracted from the operator's network. Requirements and use cases are presented as motivations for this methodology. The results achieve an accuracy of about 5 dB, allowing operators to obtain accurate neighbour lists, optimise network planning and automate certain actions on the network by enabling the Self-Organising Network concept. The cloud implementation enables a fast and easy integration with other network management and monitoring tools, such as the Metric platform, optimising operators' resource usage recurring to elastic resources on-demand when needed. This implementation was integrated into the Metric platform, which is currently available to be used by several operators.publishersversionpublishe

Using Machine Learning for Handover Optimization in Vehicular Fog Computing

Smart mobility management would be an important prerequisite for future fog computing systems. In this research, we propose a learning-based handover optimization for the Internet of Vehicles that would assist the smooth transition of device connections and offloaded tasks between fog nodes. To accomplish this, we make use of machine learning algorithms to learn from vehicle interactions with fog nodes. Our approach uses a three-layer feed-forward neural network to predict the correct fog node at a given location and time with 99.2 % accuracy on a test set. We also implement a dual stacked recurrent neural network (RNN) with long short-term memory (LSTM) cells capable of learning the latency, or cost, associated with these service requests. We create a simulation in JAMScript using a dataset of real-world vehicle movements to create a dataset to train these networks. We further propose the use of this predictive system in a smarter request routing mechanism to minimize the service interruption during handovers between fog nodes and to anticipate areas of low coverage through a series of experiments and test the models' performance on a test set

SmartUnit: Empirical Evaluations for Automated Unit Testing of Embedded Software in Industry

In this paper, we aim at the automated unit coverage-based testing for embedded software. To achieve the goal, by analyzing the industrial requirements and our previous work on automated unit testing tool CAUT, we rebuild a new tool, SmartUnit, to solve the engineering requirements that take place in our partner companies. SmartUnit is a dynamic symbolic execution implementation, which supports statement, branch, boundary value and MC/DC coverage. SmartUnit has been used to test more than one million lines of code in real projects. For confidentiality motives, we select three in-house real projects for the empirical evaluations. We also carry out our evaluations on two open source database projects, SQLite and PostgreSQL, to test the scalability of our tool since the scale of the embedded software project is mostly not large, 5K-50K lines of code on average. From our experimental results, in general, more than 90% of functions in commercial embedded software achieve 100% statement, branch, MC/DC coverage, more than 80% of functions in SQLite achieve 100% MC/DC coverage, and more than 60% of functions in PostgreSQL achieve 100% MC/DC coverage. Moreover, SmartUnit is able to find the runtime exceptions at the unit testing level. We also have reported exceptions like array index out of bounds and divided-by-zero in SQLite. Furthermore, we analyze the reasons of low coverage in automated unit testing in our setting and give a survey on the situation of manual unit testing with respect to automated unit testing in industry.Comment: In Proceedings of 40th International Conference on Software Engineering: Software Engineering in Practice Track, Gothenburg, Sweden, May 27-June 3, 2018 (ICSE-SEIP '18), 10 page