Open Source Legal Debt

Open source legal debt is unwanted open-source code in your products and projects.

Code may be unwanted, if it does not fit your (a company’s) business model. An example is code that has been copied from StackOverflow into your code base. That’s because code from StackOverflow has a copyleft license, which means that as you distribute your software, you can only use the license StackOverflow uses, not your own. According to this license, those who receive your software are free to pass it on, for free, and you can’t do anything about it.

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Non-Software-Industry User-Led Open-Source Consortia

tl;dr We observe sustained growth in what we call non-software-industry user-led open-source consortia. These are open-source consortia (non-profit organizations) created by companies from outside the software industry with the goal of developing the applications these companies need to run their business. Their behaviors are different from other open-source consortia and we can see this expressed in their governance rules.

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Two Issue Break of IEEE Computer Column Open Source Expanded

Thanks to this editor’s inability to wield their whip effectively, the IEEE Computer magazine’s column Open Source Expanded is taking a two issue break: There wasn’t an August 2022 article and there won’t be an October 2022 article in IEEE Computer magazine. We will resume our regular bi-monthly schedule in December 2022. Until then you may enjoy rereading some of the old articles. Thank you for your patience and continued support.

Management Accounting Concepts for Inner Source Software Engineering [ICSOB 2022]

Abstract: Inner source software development is the use of open source developmentā€™s best practices inside a company. In inner source, developers collaborate on reusable software components across company-internal organizational silo boundaries for mutual benefit. As such, inner source goes against the grain of traditional management techniques. In this article, we present two conceptual models of management accounting for inner source. We derived these prototypes by performing a literature review and triangulating the results with interviews of industry practitioners. We demonstrate how the conceptual models can be used for monitoring and controlling inner source projects and to determine their future viability.

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A Research Model for the Economic Assessment of Inner Source Software Development [HICSS 2023]

Abstract: Inner source is the use of open-source practices within companies. It enables more efficient software development, shortens time-to-market, and lowers costs through increased company-internal collaboration. While existing studies examine social and organizational impact factors on inner source adoption, only a few have looked at measuring and economically assessing inner source. This article presents an overview of current research regarding inner source, its measurement, economic assessment, and impact on businesses and their processes. Based on a systematic literature review we build a research model for economic inner source assessment. This research model shows thematic dependencies between the economic impact of inner source and its measurement. Additionally, it proposes research questions and hypotheses on measuring, economically assessing, and subsequently adopting inner source.

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Open Source Software Governance: A Case Study Evaluation of Supply Chain Management Best Practices [HICSS 2023]

Abstract: Corporate open source governance aims to manage the increasing use of free/libre and open source software (FLOSS) in companies. To avoid the risks of the ungoverned use, companies need to establish processes addressing license compliance, component approval, and supply chain management (SCM). We proposed a set of industry-inspired best practices for supply chain management organized into a handbook. To evaluate the handbook, we ran a one-year case study at a large enterprise software company, where we performed semi-structured interviews, workshops, and direct observations. We assessed the initial situation of open source governance, the implementation of the proposed SCM best practices, and the resulting impact. We report the results of this study by demonstrating and discussing the artifacts created while the case study company implemented the SCM-focused governance process. The evaluation case study enabled the real-life application and the improvement of the proposed best practices.

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A Solution for Automated Grading of QDA Homework [HICSS 2023]

Abstract: Teaching research methods is important in any curriculum that prepares students for an academic career. While theoretical frameworks for qualitative theory building can be adequately conveyed through lecturing, the practices of qualitative data analysis (QDA) cannot. However, using experiential learning techniques for teaching QDA methods to large numbers of students presents a challenge to the instructor due to the effort required for the grading of homework. Any homework involving the coding of qualitative data will result in a myriad of different interpretations of the same data with varying quality. Grading such assignments requires significant effort. We approached this problem by using methods of inter-rater agreement and a model solution as a proxy for the quality of the submission. The automated agreement data serves as the foundation for a semi-automated grading process. Within this paper, we demonstrate that this proxy has a high correlation with the manual grading of submissions.

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Challenges to Open Collaborative Data Engineering [HICSS 2023]

Abstract: Open data is data that can be used, modified, and passed on, for free, similar to open-source software. Unlike open-source, however, there is little collaboration in open data engineering. We perform a systematic literature review of collaboration systems in open data, specifically for data engineering by users, taking place after data has been made available as open data. The results show that open data users perform a wide range of activities to acquire, understand, process and maintain data for their projects without established best practices or standardized tools for open collaboration. We identify and discuss technical, community, and process challenges to collaboration in data engineering for open data.

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