231,026 research outputs found

    An Empirical Study of User Support Tools in Open Source Software

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    End users’ positive response is essential for the success of any software. This is true for both commercial and Open Source Software (OSS). OSS is popular not only because of its availability, which is usually free but due to the user support it provides, generally through public platforms. The study model of this research establishes a relationship between OSS user support and available support tools. To conduct this research, we used a dataset of 100 OSS projects in different categories and examined five user support tools provided by different OSS projects. The results show that project trackers, user mailing lists, and updated versions have a significant role in gaining user support. However, we were unable to find a significant association between user support and documentation, as well as between user support and the troubleshooting guidelines provided by OSS projects

    A theory-grounded framework of Open Source Software adoption in SMEs

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    This is a post-peer-review, pre-copyedit version of an article published in European Journal of Information Systems. The definitive publisher-authenticated version Macredie, RD and Mijinyawa, K (2011), "A theory-grounded framework of Open Source Software adoption in SMEs", European Journal of Informations Systems, 20(2), 237-250 is available online at: http://www.palgrave-journals.com/ejis/journal/v20/n2/abs/ejis201060a.html.The increasing popularity and use of Open Source Software (OSS) has led to significant interest from research communities and enterprise practitioners, notably in the small business sector where this type of software offers particular benefits given the financial and human capital constraints faced. However, there has been little focus on developing valid frameworks that enable critical evaluation and common understanding of factors influencing OSS adoption. This paper seeks to address this shortcoming by presenting a theory-grounded framework for exploring these factors and explaining their influence on OSS adoption, with the context of study being small- to medium-sized Information Technology (IT) businesses in the U.K. The framework has implications for this type of business – and, we will suggest, more widely – as a frame of reference for understanding, and as tool for evaluating benefits and challenges in, OSS adoption. It also offers researchers a structured way of investigating adoption issues and a base from which to develop models of OSS adoption. The study reported in this paper used the Decomposed Theory of Planned Behaviour (DTPB) as a basis for the research propositions, with the aim of: (i) developing a framework of empirical factors that influence OSS adoption; and (ii) appraising it through case study evaluation with 10 U.K. Small- to medium-sized enterprises in the IT sector. The demonstration of the capabilities of the framework suggests that it is able to provide a reliable explanation of the complex and subjective factors that influence attitudes, subjective norms and control over the use of OSS. The paper further argues that the DTPB proved useful in this research area and that it can provide a variety of situation-specific insights related to factors that influence the adoption of OSS

    Lab Retriever: a software tool for calculating likelihood ratios incorporating a probability of drop-out for forensic DNA profiles.

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    BackgroundTechnological advances have enabled the analysis of very small amounts of DNA in forensic cases. However, the DNA profiles from such evidence are frequently incomplete and can contain contributions from multiple individuals. The complexity of such samples confounds the assessment of the statistical weight of such evidence. One approach to account for this uncertainty is to use a likelihood ratio framework to compare the probability of the evidence profile under different scenarios. While researchers favor the likelihood ratio framework, few open-source software solutions with a graphical user interface implementing these calculations are available for practicing forensic scientists.ResultsTo address this need, we developed Lab Retriever, an open-source, freely available program that forensic scientists can use to calculate likelihood ratios for complex DNA profiles. Lab Retriever adds a graphical user interface, written primarily in JavaScript, on top of a C++ implementation of the previously published R code of Balding. We redesigned parts of the original Balding algorithm to improve computational speed. In addition to incorporating a probability of allelic drop-out and other critical parameters, Lab Retriever computes likelihood ratios for hypotheses that can include up to four unknown contributors to a mixed sample. These computations are completed nearly instantaneously on a modern PC or Mac computer.ConclusionsLab Retriever provides a practical software solution to forensic scientists who wish to assess the statistical weight of evidence for complex DNA profiles. Executable versions of the program are freely available for Mac OSX and Windows operating systems
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