Network testing plays an important role in the iterative process of developing new communication protocols and algorithms. However, test environments have to keep up with the evolution of technology and require continuous update and redesign. In this article, we propose COINS, a framework that can be used by wireless technology developers to enable CI practices in their testbed infrastructure. As a proof-of-concept, we provide a reference architecture and implementation of COINS for controlled testing of multi-technology 5G MTC networks. The implementation upgrades an existing wireless experimentation testbed with new software and hardware functionalities. It blends web service technology and operating system virtualization technologies with emerging Internet of Things technologies enabling CI for wireless networks. Moreover, we also extend an existing qualitative methodology for comparing similar frameworks and identify and discuss open challenges for wider use of CI practices in wireless technology development.
In the comparison, we considered an extensive list of features that can be used to compare the experimentation systems @cite_12 . The list of features, however, lacks the CI-specific properties that our work focuses on; therefore, we extended the list with four core CI concepts @cite_0 @cite_13 : Existence of a single source repository that contains everything needed for the completely automated build process. Support for completely automated build process executed on each commit. Self-tests included inside the repository, which run on each build. Fast build process so that each commit can be built and tested.
@cite_0 @cite_13 @cite_12
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In this paper we present the results of a study into the persistence and availability of web resources referenced from papers in scholarly repositories. Two repositories with different characteristics, arXiv and the UNT digital library, are studied to determine if the nature of the repository, or of its content, has a bearing on the availability of the web resources cited by that content. Memento makes it possible to automate discovery of archived resources and to consider the time between the publication of the research and the archiving of the referenced URLs. This automation allows us to process more than 160000 URLs, the largest known such study, and the repository metadata allows consideration of the results by discipline. The results are startling: 45 (66096) of the URLs referenced from arXiv still exist, but are not preserved for future generations, and 28 of resources referenced by UNT papers have been lost. Moving forwards, we provide some initial recommendations, including that repositories should publish URL lists extracted from papers that could be used as seeds for web archiving systems. Experimentation strongly suggests that, for attacking deep questions and hard problems with the assistance of an automated reasoning program, the more effective paradigms rely on the retention of deduced information. A significant obstacle ordinarily presented by such a paradigm is the deduction and retention of one or more needed conclusions whose complexity sharply delays their consideration. To mitigate the severity of the cited obstacle, I formulated and feature in this article the hot list strategy. The hot list strategy asks the researcher to choose, usually from among the input statements characterizing the problem under study, one or more statements that are conjectured to play a key role for assignment completion. The chosen statements – conjectured to merit revisiting, again and again – are placed in an input list of statements, called the hot list. When an automated reasoning program has decided to retain a new conclusion C – before any other statement is chosen to initiate conclusion drawing – the presence of a nonempty hot list (with an appropriate assignment of the input parameter known as heat) causes each inference rule in use to be applied to C together with the appropriate number of members of the hot list. Members of the hot list are used to complete applications of inference rules and not to initiate applications. The use of the hot list strategy thus enables an automated reasoning program to briefly consider a newly retained conclusion whose complexity would otherwise prevent its use for perhaps many CPU-hours. To give evidence of the value of the strategy, I focus on four contexts: (1) dramatically reducing the CPU time required to reach a desired goal, (2) finding a proof of a theorem that had previously resisted all but the more inventive automated attempts, (3) discovering a proof that is more elegant than previously known, and (4) answering a question that had steadfastly eluded researchers relying on an automated reasoning program. I also discuss a related strategy, the dynamic hot list strategy (formulated by my colleague W. McCune), that enables the program during a run to augment the contents of the hot list. In the Appendix, I give useful input files and interesting proofs. Because of frequent requests to do so, I include challenge problems to consider, commentary on my approach to experimentation and research, and suggestions to guide one in the use of McCune’s automated reasoning program OTTER. The use of automated build and continuous integration systems by software teams is a well-established practice, and has been shown to provide significant benefits. However, to qualify the value of continuous integration practices it is necessary to to compare their cost with the associated benefit. When considering automated builds or continuous integration in an agile team, this cost is the time taken by developers to run the build script and automated tests before code can be committed and or deployed. This paper discusses the effect of this time on team behaviour by comparing two projects with significantly different build times. Open source software is built by teams of volunteers. Each project has a core team of developers, who have the authority to commit changes to the repository; this team is the elite, committed foundation of the project, selected through a meritocratic process from a larger number of people who participate on the mailing list. Most projects carefully regulate admission of outsiders to full developer privileges; some projects even have formal descriptions of this process. Understanding the factors that influence the "who, how and when" of this process is critical, both for the sustainability of FLOSS projects, and for outside stakeholders who want to gain entry and succeed. In this paper we mount a quantitative case study of the process by which people join FLOSS projects, using data mined from the Apache web server, Postgres, and Python. We develop a theory of open source project joining, and evaluate this theory based on our data.