Crowd science: The organization of scientific research in open collaborative projects

Code reuse is a form of knowledge reuse in software development that is fundamental to innovation in many fields. However, to date there has been no systematic investigation of code reuse in open source software projects. This study uses quantitative and qualitative data gathered from a sample of six open source software projects to explore two sets of research questions derived from the literature on software reuse in firms and open source software development. We find that code reuse is extensive across the sample and that open source software developers, much like developers in firms, apply tools that lower their search costs for knowledge and code, assess the quality of software components, and have incentives to reuse code. Open source software developers reuse code because they want to integrate functionality quickly, because they want to write preferred code, because they operate under limited resources in terms of time and skills, and because they can mitigate development costs through code reuse.

/pdf/code-reuse-in-open-source-software-657t1xhx1o.pdf

Code Reuse in Open Source Software

As organizations implement systematic software reuse programs to improve productivity and quality, they must be able to measure their progress and identify the most effective reuse strategies. This is done with reuse metrics and models. In this article we survey metrics and models of software reuse and reusability, and provide a classification structure that will help users select them. Six types of metrics and models are reviewed: cost-benefit models, maturity assessment models, amount of reuse metrics, failure modes models, reusability assessment models, and reuse library metrics.

https://homepages.dcc.ufmg.br/~figueiredo/disciplinas/papers/acm96frakes.pdf

Software reuse: metrics and models

A growing amount of scientific research is done in an open collaborative fashion, in projects that are sometimes labeled as “crowd science”, “citizen science”, or “networked science”. This paper seeks to gain a more systematic understanding of crowd science and to provide scholars with a conceptual framework and an agenda for future research. First, we briefly present three case examples that span different fields of science and illustrate the heterogeneity concerning what crowd science projects do and how they are organized. Second, we identify two fundamental elements that characterize crowd science projects - open participation and open sharing of intermediate inputs - and distinguish crowd science from other knowledge production regimes such as innovation contests or traditional “Mertonian” science. Third, we explore potential knowledge-related and motivational benefits that crowd science offers over alternative organizational modes, and potential challenges it is likely to face. Drawing on prior research on the organization of problem solving, we also consider for what kinds of tasks particular benefits or challenges are likely to be most pronounced. We conclude by outlining an agenda for future research and by discussing implications for funding agencies and policy makers.

Crowd Science: The Organization of Scientific Research in Open Collaborative Projects

A growing amount of scientific research is done in an open collaborative fashion, in projects that are sometimes labeled as “crowd science”, “citizen science”, or “networked science”. This paper se...

Systematic software reuse is a paradigm shift in software engineering from building single systems to building families of related systems. The goal of software reuse research is to discover systematic procedures for engineering new systems from existing assets. Implementing systematic reuse is risky. Not doing it is also risky. Trying systematic reuse unsuccessfully can cost precious time and resources and may make management sceptical of trying it again. But if your competitors do it successfully and you do not, you may lose market share and possibly an entire market. There is no cookbook solution-each organization must analyze its own needs, implement reuse measurements, define the key benefits it expects, identify and remove impediments, and manage risk. Reliable data on how much this costs and the benefits an organization will derive are insufficient. The article addresses issues from management, measurement, law, economics, libraries, and the design of reusable software. >

Success factors of systematic reuse

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Success factors of systematic reuse