CrowdAdaptor: A Crowd Sourcing Approach toward Adaptive Energy-Efficient Configurations of Virtual Machines Hosting Mobile Applications

Applications written by end-user programmers are hardly energy-optimized by these programmers. The end users of such applications thus suffer significant energy issues. In this paper, we propose CrowdAdaptor, a novel approach toward locating energy-efficient configurations to execute the applications hosted in virtual machines on handheld devices. CrowdAdaptor innovatively makes use of the development artifacts (test cases) and the very large installation base of the same application to distribute the test executions and performance data collection of the whole test suites against many different virtual machine configurations among these installation bases. It synthesizes these data, continuously discovers better energy-efficient configurations, and makes them available to all the installations of the same applications. We report a multi-subject case study on the ability of the framework to discover energy-efficient configurations in three power models. The results show that Crowd Adaptor can achieve up to 50% of energy savings based on a conservative linear power model.published_or_final_versio

Systematic and recomputable comparison of multi-cloud management platforms

With the growth and evolution of cloud applications, more and more architectures use hybrid cloud bindings to optimally use virtual resources regarding pricing policies and performance. This process has led to the creation of multi-cloud management platforms as well as abstraction libraries. At the moment, many (multi-)cloud management platforms (CMPs) are designed to cover the functional requirements. Along with growing adoption and industrial impact of such solutions, there is a need for a comparison and test environment which automatically assesses and compares existing platforms and helps in choosing the optimal one. This paper focuses on the creation of a suitable testbed concept and an actual extensible software prototype which makes multi-cloud experiments repeatable and reusable by other researchers. The work is evaluated by an exemplary comparison of 4 CMPs bound to AWS, showcasing standardised output formats and evaluation criteria

A document based traceability model for test management

Software testing has became more complicated in the emergence of distributed network, real-time environment, third party software enablers and the need to test system at multiple integration levels. These scenarios have created more concern over the quality of software testing. The quality of software has been deteriorating due to inefficient and ineffective testing activities. One of the main flaws is due to ineffective use of test management to manage software documentations. In documentations, it is difficult to detect and trace bugs in some related documents of which traceability is the major concern. Currently, various studies have been conducted on test management, however very few have focused on document traceability in particular to support the error propagation with respect to documentation. The objective of this thesis is to develop a new traceability model that integrates software engineering documents to support test management. The artefacts refer to requirements, design, source code, test description and test result. The proposed model managed to tackle software traceability in both forward and backward propagations by implementing multi-bidirectional pointer. This platform enabled the test manager to navigate and capture a set of related artefacts to support test management process. A new prototype was developed to facilitate observation of software traceability on all related artefacts across the entire documentation lifecycle. The proposed model was then applied to a case study of a finished software development project with a complete set of software documents called the On-Board Automobile (OBA). The proposed model was evaluated qualitatively and quantitatively using the feature analysis, precision and recall, and expert validation. The evaluation results proved that the proposed model and its prototype were justified and significant to support test management

Virtual distributed environments for systems with time requirements

Virtualization is widely propagating technology that is used to run multiple virtual machines on the same computational unit by means of a piece of firmware, hardware or software called a hypervisor. Despite having been used since the 60as, the current indisputable need for fast reliable communication may put this technology to question. This project analyzes the amount of impact the virtualization has on the transmission times. In the first part, the Xen hypervisor, configured with different virtual environments, simulating complex scenarios, will be evaluated to determine the size of the impact. As a bridge between the multiple virtual machines, middleware Ice, will be used. Furthermore lower in the scale, for embedded systems, the XtratuM hypervisor was designed to support real-time systems. The second part is dedicated to evaluating whether the communication maintains the real time property of these systems. Bare boned virtualization will be implemented in this second part of the project.Ingeniería en Tecnologías de Telecomunicació

Augmenting and structuring user queries to support efficient free-form code search

Source code terms such as method names and variable types are often different from conceptual words mentioned in a search query. This vocabulary mismatch problem can make code search inefficient. In this paper, we present Code voCABUlary (CoCaBu), an approach to resolving the vocabulary mismatch problem when dealing with free-form code search queries. Our approach leverages common developer questions and the associated expert answers to augment user queries with the relevant, but missing, structural code entities in order to improve the performance of matching relevant code examples within large code repositories. To instantiate this approach, we build GitSearch, a code search engine, on top of GitHub and StackOverflow Q\&A data. We evaluate GitSearch in several dimensions to demonstrate that (1) its code search results are correct with respect to user-accepted answers; (2) the results are qualitatively better than those of existing Internet-scale code search engines; (3) our engine is competitive against web search engines, such as Google, in helping users complete solve programming tasks; and (4) GitSearch provides code examples that are acceptable or interesting to the community as answers for StackOverflow questions

Cost-Sensitive Radial Basis Function Neural Network Classifier for Software Defect Prediction

Effective prediction of software modules, those that are prone to defects, will enable software developers to achieve efficient allocation of resources and to concentrate on quality assurance activities. The process of software development life cycle basically includes design, analysis, implementation, testing, and release phases. Generally, software testing is a critical task in the software development process wherein it is to save time and budget by detecting defects at the earliest and deliver a product without defects to the customers. This testing phase should be carefully operated in an effective manner to release a defect-free (bug-free) software product to the customers. In order to improve the software testing process, fault prediction methods identify the software parts that are more noted to be defect-prone. This paper proposes a prediction approach based on conventional radial basis function neural network (RBFNN) and the novel adaptive dimensional biogeography based optimization (ADBBO) model. The developed ADBBO based RBFNN model is tested with five publicly available datasets from the NASA data program repository. The computed results prove the effectiveness of the proposed ADBBO-RBFNN classifier approach with respect to the considered metrics in comparison with that of the early predictors available in the literature for the same datasets