A Systematic Mapping Study of Empirical Studies on Software Cloud Testing Methods

Context: Software has become more complicated, dynamic, and asynchronous than ever, making testing more challenging. With the increasing interest in the development of cloud computing, and increasing demand for cloud-based services, it has become essential to systematically review the research in the area of software testing in the context of cloud environments. Objective: The purpose of this systematic mapping study is to provide an overview of the empirical research in the area of software cloud-based testing, in order to build a classification scheme. We investigate functional and non-functional testing methods, the application of these methods, and the purpose of testing using these methods. Method: We searched for electronically available papers in order to find relevant literature and to extract and analyze data about the methods used. Result: We identified 69 primary studies reported in 75 research papers published in academic journals, conferences, and edited books. Conclusion: We found that only a minority of the studies combine rigorous statistical analysis with quantitative results. The majority of the considered studies present early results, using a single experiment to evaluate their proposed solution

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

Testing as a Service on Cloud A Review

Software testing is an important part of software engineering life cycle. Software testing is a process used for evaluating an attributes or capability of program and makes sure that it meets the requirements. The application building techniques have changed and has adapted to newly emerging technology of cloud. Cloud computing has changed t he way of obtaining computing resources, and also has given a new direction to manage and deliver computing services, technologies, and solutions. Cloud computing not only brings new business opportunities, but also causes some majo r impacts on software testing and maintenance. Cloud computing creates an opportunity that offer s testing as a service (TaaS) for SaaS and Cloud s. This lead to a new phase shift in conventional testing thereby identifying new issues, challenges and needs in software testing, particular in testing Cloud s and Cloud - based applications. This paper gives a comprehensive view on Testing as a Service. Also a comparative view towards conventional testing and Cloud testing is also considered

Automated, Systematic and Parallel Approaches to Software Testing in Bioinformatics

Software quality assurance becomes especially critical if bioinformatics tools are to be used in a translational medical setting, such as analysis and interpretation of biological data. We must ensure that only validated algorithms are used, and that they are implemented correctly in the analysis pipeline – and not disrupted by hardware or software failure. In this thesis, I review common quality assurance practice and guidelines for bioinformatics software testing. Furthermore, I present a novel cloud-based framework to enable automated testing of genetic sequence alignment programs. This framework performs testing based on gold standard simulation data sets, and metamorphic testing. I demonstrate the effectiveness of this cloudbased framework using two widely used sequence alignment programs, BWA and Bowtie, and some fault-seeded ‘mutant’ versions of BWA and Bowtie. This preliminary study demonstrates that this type of cloud-based software testing framework is an effective and promising way to implement quality assurance in bioinformatics software that is used in genomic medicine

5W+1H pattern: A perspective of systematic mapping studies and a case study on cloud software testing

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A Reliable and Cost-Efficient Auto-Scaling System for Web Applications Using Heterogeneous Spot Instances

Cloud providers sell their idle capacity on markets through an auction-like mechanism to increase their return on investment. The instances sold in this way are called spot instances. In spite that spot instances are usually 90% cheaper than on-demand instances, they can be terminated by provider when their bidding prices are lower than market prices. Thus, they are largely used to provision fault-tolerant applications only. In this paper, we explore how to utilize spot instances to provision web applications, which are usually considered availability-critical. The idea is to take advantage of differences in price among various types of spot instances to reach both high availability and significant cost saving. We first propose a fault-tolerant model for web applications provisioned by spot instances. Based on that, we devise novel auto-scaling polices for hourly billed cloud markets. We implemented the proposed model and policies both on a simulation testbed for repeatable validation and Amazon EC2. The experiments on the simulation testbed and the real platform against the benchmarks show that the proposed approach can greatly reduce resource cost and still achieve satisfactory Quality of Service (QoS) in terms of response time and availability