Software Community Research Articles

Hacking Technology, Hacking Communities: Codes of Conduct and Community Standards in Open Source

In recent years, the free/libre and open source software (FLOSS) and hacking communities have engaged in lively debates about diversity and inclusion in their ranks. These conversations gained momentum when research showed that women’s rate of participation in FLOSS was shockingly low, even compared to academic computer science and industry. Participants questioned whether the commitment to “openness” in coding and technical activities extended to the communities themselves, and proposed changes to how their communities were governed. One change many communities instituted was to introduce codes of conduct, intended to formalize community standards for how people should behave when they collaborate. This case presents a 2015 example of an introduction of a code of conduct and the discussion surrounding it as a way to surface some of the tacit patterns in FLOSS communities. Communities continue to refine their values and how they wish to guide participation in coding and hacking.

Dual Channel Among Task and Contribution on OSS Communities: An Empirical Study

Open Source Software (OSS) community has attracted a large number of distributed developers to work together, e.g. reporting and discussing issues as well as submitting and reviewing code. OSS developers create links among development units (e.g. issues and pull requests in GitHub), share their opinions and promote the resolution of development units. Although previous work has examined the role of links in recommending high-priority tasks and reducing resource waste, the understanding of the actual usage of links in practice is still limited. To address the research gap, we conduct an empirical study based on the 5W1H model and data mining from five popular OSS projects on GitHub. We find that links originating from a PR are more common than the other three types of links, and links are more frequently created in Documentation. We also find that average duration between development units’ create time in a link is half a year. We observed that link behaviors are very complex and the duration of link increases with the complexity of link structure. We also observe that the reasons of link are very different, especially in P–P and I–I. Finally, future works are discussed in conclusion.

Evolution of Coordination Structures in OSS Software: An Exponential Random Graph Model

In Open Source Software (OSS) communities, coordination happens based on artifact-mediated interactions among geographically dispersed individuals. Prior literature on OSS and software evolution ar...

Intent-driven cloud resource design framework to meet cloud performance requirements and its application to a cloud-sensor system

In cloud service delivery, the cloud user is concerned about “what” function and performance the cloud service could provide, while the cloud provider is concerned about “how” to design proper underlying cloud resources to meet the cloud user’s requirements. We take the cloud user’s requirement as intent and aim to translate the intent autonomously into cloud resource decisions. In recent years, intent-driven management has been a widely spread management concept which aims to close the gap between the operator’s high-level requirements and the underlying infrastructure configuration complexity. Intent-driven management has drawn attention from telecommunication industries, standards organizations, the open source software community and academic research. There are various application of intent-driven management which are being studied and implemented, including intent-driven Software Defined Network (SDN), intent-driven wireless network configuration, etc. However, application of intent-driven management into the cloud domain, especially the translation of cloud performance-related intent into the amount of cloud resource, has not been addressed by existing studies. In this work, we have proposed an Intent-based Cloud Service Management (ICSM) framework, and focused on realizing the RDF (Resource Design Function) to translate cloud performance-related intent into concrete cloud computation resource amount settings that are able to meet the intended performance requirement. Furthermore, we have also proposed an intent breach prevention mechanism, P-mode, which is essential for commercial cloud service delivery. We have validated the proposals in a sensor-cloud system, designed to meet the user’s intent to process a large quantity of images collected by the sensors in a restricted time interval. The validation results show that the framework achieved 88.93 ~ 90.63% precision for performance inference, and exceeds the conventional resource approach in the aspects of human cost, time cost and design results. Furthermore, the intent breach prevention mechanism P-mode significantly reduced the breach risk, at the same time keeping a high level of precision.

Open Source Hardware, Exploring how Industry Regulation Affects Knowledge Commons Governance: An Exploratory Case Study

Tools for clinical examination have not fundamentally evolved since the invention of the stethoscope by Rene Laennec in the nineteenth century. However, three decades ago, the medical community started to consider repurposing ultrasound scanners to improve physical examinations. A broad community of healthcare professionals trained in the new clinical examination paradigm could not be created due to the very high price of portable ultrasound scanners available on the market. In this paper, we study an Open-Source Hardware (OSH) community that aims to improve diagnosis in hospitals and medically underserved areas worldwide. They are designing an echo-stethoscope – a portable ultrasound scanner – that would be affordable in low and middle-income countries. The variety of expertise pooled to achieve this objective puts this knowledge common (KC) at the crossroads of open-source software (OSS), OSH, and medical communities. Unlike typical KC outcomes, an ultrasound probe is a physical object. Development and innovation in the physical world bring social dilemmas that the community has to overcome, restrictions in terms of openness, and in this case, unintended privatization. Our study uses the governing knowledge common framework (GKCF), a modified institutional analysis and development framework, to untangle the interactions between resources, participants, and governance structures. Our research describes why and how the creation of a physical object subject to industry regulation influences the evolution and governance of the KC. We provide evidence that temporary privatization of the KC can be used as a way to protect and sustain a common during the industrialization phase. We also demonstrate how a portfolio of projects is an effective and resilient way to help the common survive this privatization step.

Code of Conduct Conversations in Open Source Software Projects on Github

The rapid growth of open source software necessitates a deeper understanding of moderation and governance methods currently used within these projects. The code of conduct, a set of rules articulating standard behavior and responsibilities for participation within a community, is becoming an increasingly common policy document in open source software projects for setting project norms of behavior and discouraging negative or harassing comments and conversation. This study describes the conversations around adopting and crafting a code of conduct as well as those utilizing code of conduct for community governance. We conduct a qualitative analysis of a random sample of GitHub issues that involve the code of conduct. We find that codes of conduct are used both proactively and reactively to govern community behavior in project issues. Oftentimes, the initial addition of a code of conduct does not involve much community participation and input. However, a controversial moderation act is capable of inciting mass community feedback and backlash. Project maintainers balance the tension between disciplining potentially offensive forms of speech and encouraging broad and inclusive participation. These results have implications for the design of inclusive and effective governance practices for open source software communities.

The Role of Competitive Robotics in Providing Context to Classroom Learning and Technical Skill Development in School Age Students—A Survey of Current Avenues, Assessment, and Path Forward with Systematic Implementation

Finding context, examples, and ample hands-on experimentation is fundamental for understanding complex ideas in subjects such as science and math. Recent popularity of competitive robotics has become a catalyst in the development of DIY and hobby kits. Manufacturers have made available easy to work, re-configurable, and functional, structural elements as well as control electronics. Additionally, extensive participation from the open-source software community is providing cutting edge and effective software. Despite all the right ingredients, competitive robotics continues to be unregulated and non-standardized. Additionally, in absence of regulations and standards, the organizers, suppliers, educators, and participants are left to their own means and resources that necessarily may not align with systematic learning. The cost of approved competitive kits as well as field kits becomes inhibitive for students from poorer communities. This paper surveys a wide range of competitive robotics avenues available to school-age students. A survey with various stake holders including participants, mentors, referees, and organizers is done and findings are included. A path for standardizing competitive robotics within the framework of the World Robotics League is found to be an effective tool to train the students. A description of the World Robotics League framework and initial findings are reported.

A Machine-Learning Approach to Generalisation of GIS Data

The automation of map generalisation in this study involves an expert system approach that consists of four main components including knowledge acquisition, an inference engine, knowledge representation and a user interface. The acquired knowledge was then utilised to build a knowledge-based solution: a ‘Generalisation Expert System’ (GES) developed in Java, Python and C programming environments for the delivery of generalised geographical features. Its capabilities are demonstrated in a case study through generalising several line and polyline databases over the study area in Canberra, Australia. The cartographic and GIS software communities will benefit from this study through access to a set of tools, guidelines and protocols that incorporate a standardised cartographic generalisation methodology. The results of the trials utilising GES were analysed: a series of generalisation routines were performed to assess the quality of simplification results for different spatial layers. Cartometric measures such as the total length and number of line or polyline segments were used as indices of generalisation to quantify generalisation performance for the target small scale. For example, there are 101,228 segments in 1:250,000 scale and 9,491 segments in 1:500,000 scale contours over the study area. This requires a reduction in the complexity and the density of elevation data. Changes in the representation of contour features at 1:250,000 and 1:500,000 scales as a result of generalisation were quantified. Outputs from map derivation have been analysed applying the Radical Law, this determines the retained number of objects for a given scale change and the number of objects of the original source map. Testing demonstrated that the implemented algorithms in GES are able to extract characteristic vertices on the original entity lines and polylines (e.g. for roads) while excluding non-characteristic lines and polylines to reduce irrelevant computation. This study has demonstrated reasonable improvements in Vertex Reduction, Classification and Merge, Enhanced Douglas-Peucker and Douglas-Peucker-Peschier algorithms. The test results show that GES generalises line features accurately while still maintaining their geometric relations. Existing generalisation software requires advanced technical skills from users; GES however, has a basic and user friendly Graphical User Interface (GUI) which is an advantage to users with basic technical skills and understanding of spatial data management. Changes to geographic parameters should be updated in multi-scale maps and spatial databases in near real-time. GES can be developed as a potential tool for generalising large-scale maps into smaller scales, and creating maps of different themes across a variety of scales. Test results have also demonstrated that the methodology developed improves the efficiency of line and polyline generalisation. This study aims to contribute to generalisation system design and the production of a clear framework for users. Experiments presented in this book can be applied to real world problems such as the generalisation of road networks and area features using GES. Future research should be directed towards developing web mapping platforms with generalisation functionality at varying scales.

3D printing in surgical simulation: emphasized importance in the COVID-19 pandemic era

3D printing in surgical simulation: emphasized importance in the COVID-19 pandemic era

Ending Medicine’s Chronic Dysfunction: Tools and Standards for Medical Decision Making

This book describes an overlooked solution to a long-standing problem in health care. The problem is an informational supply chain that is unnecessarily dependent on the minds of doctors for assembling patient data and medical knowledge in clinical decision making. That supply chain function is more than the human mind can deliver. Yet, dependence on the mind is built into the traditional role of doctors, who are educated and licensed to rely heavily on personal knowledge and judgment. The culture of medicine has long been in denial of this problem, even now that health information technology is increasingly used, and even as artificial intelligence (AI) tools are emerging. AI will play an important role, but it is not a solution. The solution instead begins with traditional software techniques designed to integrate novel functionality for clinical decision support and electronic health record (EHR) tools. That functionality implements high standards of care for managing health information. This book describes that functionality in some detail. This description is intended in part to be a starting point for developers in the open source software community, who have an opportunity to begin developing an integrated, cloud-based version of the tools described, working with interested clinicians, patients, and others. The tools grew out of work beginning more than six decades ago, when this book’s lead author (deceased) originated problem lists and structured notes in medical records. The electronic tools he later developed led him to reconceive education and licensure for doctors and other health professionals, which are also part of the solution this book describes.

An approach to generate the bug report summaries using two-level feature extraction

Bug report is one of the major software artifact which is generated during the software development process. Changing requirements in the software development process leads to the continuous evolution of bugs which give challenges to the project management task. Bug Reports are the most consulted artifact by the software community. A Bug Report not only contains the information about the bug but also includes information like the resolution process, the enhancements by other persons, and the suggestions from the users if there are any. During the software evolution and maintenance phase, a developer spends a lot of effort and time searching for the appropriate bug report for resolving the bug quickly. Automatic Bug Report Summarization is one approach to solve the issue of time and effort. Bug report summarization helps developers not only find the appropriate bug report quickly but also assists in managing many tasks related to Bug Report Maintenance. In this paper, we have developed a two-level approach to generate the Bug Report summaries where the title, the description and the comments of a resolved bug report are considered for the summary. We find the entities in the title to create a template-based sentence for describing what the bug report is about. We use the PageRank algorithm along with the cosine similarity measure to find the summary of the Description field of bug report. The two level feature-based approach is used to find the relevant comments and the sentences from the comments. We have used the BRC dataset which has been used by most of the research community in this field. Finally the summaries from title, description and comments are merged together to create a final summary. The approach uses the features which have been successfully used by other researchers for the text summarization and especially to the domain of the meeting conversation like data. Empirical results shows that our approach works equally well with the other supervised and unsupervised approaches in terms of ROUGE Scores.

Exploring Community Smells in Open-Source: An Automated Approach

Software engineering is now more than ever a community effort. Its success often weighs on balancing distance, culture, global engineering practices and more. In this scenario many unforeseen socio-technical events may result into additional project cost or “social” debt, e.g., sudden, collective employee turnover. With industrial research we discovered community smells , that is, sub-optimal patterns across the organisational and social structure in a software development community that are precursors of such nasty socio-technical events. To understand the impact of community smells at large, in this paper we first introduce CodeFace4Smells , an automated approach able to identify four community smell types that reflect socio-technical issues that have been shown to be detrimental both the software engineering and organisational research fields. Then, we perform a large-scale empirical study involving over 100 years worth of releases and communication structures data of 60 open-source communities: we evaluate (i) their diffuseness, i.e., how much are they distributed in open-source, (ii) how developers perceive them, to understand whether practitioners recognize their presence and their negative effects in practice, and (iii) how community smells relate to existing socio-technical factors, with the aim of assessing the inter-relations between them. The key findings of our study highlight that community smells are highly diffused in open-source and are perceived by developers as relevant problems for the evolution of software communities. Moreover, a number of state-of-the-art socio-technical indicators (e.g., socio-technical congruence) can be used to monitor how healthy a community is and possibly avoid the emergence of social debt.

Predicting issue types on GitHub

Software maintenance and evolution involves critical activities for the success of software projects. To support such activities and keep code up-to-date and error-free, software communities make use of issue trackers, i.e., tools for signaling, handling, and addressing the issues occurring in software systems. However, in popular projects, tens or hundreds of issue reports are daily submitted. In this context, identifying the type of each submitted report (e.g., bug report, feature request, etc.) would facilitate the management and the prioritization of the issues to address. To support issue handling activities, in this paper, we propose Ticket Tagger , a GitHub app analyzing the issue title and description through machine learning techniques to automatically recognize the types of reports submitted on GitHub and assign labels to each issue accordingly. We empirically evaluated the tool's prediction performance on about 30,000 GitHub issues. Our results show that the Ticket Tagger can identify the correct labels to assign to GitHub issues with reasonably high effectiveness. Considering these results and the fact that the tool is designed to be easily integrated in the GitHub issue management process, Ticket Tagger consists in a useful solution for developers.

More Is Not Necessarily Better: An Absorptive Capacity Perspective on Network Effects in Open Source Software Development Communities

Previous research has predominantly taken a social network perspective suggesting that building more network connections or becoming deeply embedded in a network provides a better position to access network knowledge in open source software communities.This perspective implicitly assumes that accessed network knowledge automatically gets absorbed and transferred to projects, so that building more and deeper network connections is beneficial: Drawing from an absorptive capacity perspective, this research challenges such conventional wisdom, arguing instead that the benefits depend on a project’s absorptive capacity. Network connections provide access to external knowledge in the community; the absorption and transfer of this new knowledge require appropriate internal knowledge and developer roles. With longitudinal data collected from 4,518 open source software development projects hosted at SourceForge, the authors show that knowledge breadth (depth) helps with the absorption of external knowledge achieved from network depth (breadth), but it inhibits the absorption of external knowledge obtained from network breadth (depth). Further, developer roles (e.g., bridge members, role diversity) can mitigate the negative consequences of suboptimal combinations and facilitate effective transfers of absorbed external knowledge across and within projects. These findings provide important theoretical and managerial implications for managing network connections, knowledge, and developer roles in open source software communities.

How context and attention shape behaviors in online communities: a modified garbage can model

AbstractOnline communities have emerged as important organizational forms, but there are many gaps in our understanding. In particular, researchers have mainly focused on individual-level drivers of behaviors in communities, while downplaying (formal, informal) context at various levels. We theorize that different dimensions of context (i.e. omnibus and discrete context) influence decision-making in online communities through mechanisms involving community members’ attention. Specifically, context influences which problems members perceive and which solutions they retrieve and apply, thereby shaping the process of matching solutions and problems. We derive four hypotheses about contribution behaviors in online communities and how such behaviors are influenced by context. The empirical setting for our study is the open-source software community. We find support for our hypotheses in a unique dataset that captures the behavior of 24,057 community members who used the SourceForge.net online platform from 2000 to 2002.

M-BiRank: co-ranking developers and projects using multiple developer-project interactions in open source software community

Social collaborative coding is a popular trend in software development, and such platforms as GitHub provide rich social and technical functionalities for developers to collaborate on open source projects through multiple interactions. Developers often follow popular developers and projects for learning, technical selection, and collaboration. Thus, identifying popular developers and projects is very meaningful. In this paper, we propose a multiplex bipartite network ranking model, M-BiRank, to co-rank developers and projects using multiple developer-project interactions. Firstly, multiple developer-project interactions such as commit, issue, and watch are extracted and a multiplex developer-project bipartite network is constructed. Secondly, a random layer is selected from this multiplex bipartite network and initial ranking scores are calculated for developers and projects using BiRank. Finally, initial ranking scores diffuse to other layers and mutual reinforcement is taken into consideration to iteratively calculate ranking scores of developers and projects in different layers. Experiments on real-world GitHub dataset show that M-BiRank outperforms degree centrality, traditional single layer ranking methods, and multiplex ranking method.

Deferral and Resumption of Lung Cancer Screening After COVID-19 Surge: Patient Perspectives From Two Institutions.

Deferral and Resumption of Lung Cancer Screening After COVID-19 Surge: Patient Perspectives From Two Institutions.

Progress in the R ecosystem for representing and handling spatial data

Twenty years have passed since Bivand and Gebhardt (J Geogr Syst 2(3):307–317, 2000. https://doi.org/10.1007/PL00011460) indicated that there was a good match between the then nascent open-source R programming language and environment and the needs of researchers analysing spatial data. Recalling the development of classes for spatial data presented in book form in Bivand et al. (Applied spatial data analysis with R. Springer, New York, 2008, Applied spatial data analysis with R, 2nd edn. Springer, New York, 2013), it is important to present the progress now occurring in representation of spatial data, and possible consequences for spatial data handling and the statistical analysis of spatial data. Beyond this, it is imperative to discuss the relationships between R-spatial software and the larger open-source geospatial software community on whose work R packages crucially depend.

Social network analysis of open source software: A review and categorisation

Context: As companies have become large users of Open Source Software , it is important that they feel comfortable in their Open Source strategies. One of the critical differences between Open Source and Proprietary Software is the communication networks . Objective: This paper tries to set a base for understanding how open source teams are structured and how they change. This is vital to understanding Open Source Software Communities. Method: The paper looks into previous research on Social Network Analysis of Open Source Software, using a systematic literature review. Papers were gathered from Scopus, IEEEXplore and ACM Digital Library, and used or discarded based on predetermined inclusion and exclusion criteria. Research which focuses on the success factors of Open Source Software through Network Analysis is also examined. Results: A subjective categorisation is established for the papers: Structure, Lifecycle and Communication. It was found that the structure of a project has a large bearing on project success, with developers having previously worked together being indicative of project success. Other structure indicators of success are having a small but structured hierarchy, a diverse user and developer base, and project prominence. However, it was found that information on how these structures appear and evolve over time is lacking, and future research into temporal data models to determine project success information is suggested. Conclusions: A categorisation of existing research on Social Network Analysis is provided as a basis for further research. Further work into the lifecycle of OSS projects through Social Network Analysis of temporal project information is suggested.

Understanding Users' Contributions in Open Source Software Communities

User contributions are crucial to the success of open source software (OSS) communities. As users conduct frequent interactions between each other, their contribution behaviour may receive the social influence from other members. Drawing on the social influence theory, this research examined user contributions in OSS communities. The results indicated that contribution intention is significantly affected by social identity, which includes cognitive, affective and evaluative identity. In addition, the researchers found that the subjective norm has a negative effect on contribution intention. The results imply that service providers need to enhance user identification with the community in order to facilitate their contribution in OSS communities.