Open Source Software Development Research Articles

Autonomous model-based experimental design for rapid reaction development

To automate and democratize model-based experimental design for flow chemistry applications, we report the development of open-source software, Optipus. Reaction models are built in an iterative and automated fashion, for rapid reaction development.

A Motivation-Hygiene Model of Open Source Software Code Contribution and Growth

The success of open source software (OSS) projects depends on sustained contributions by developers who often display a wide variety of contribution patterns. Project leaders and stakeholders would strongly prefer that developers not only maintain but preferably increase their contributions over time as they gain experience. Corporations increasingly complement OSS developer motivations (such as fit in terms of shared values with the project community) by paying them to sustain contributions. However, practitioners argue whether payment helps or hurts projects because monetary compensation may dampen developer motivation in the long run, making it difficult for project leaders to understand what to expect from developers over time. Using Herzberg motivation-hygiene framework, we explore how developer perceptions of value fit with the project and being paid interact to determine the level of code contribution and its rate of change over time (i.e., growth). Using a survey of 564 developers across 431 projects on GitHub, we build a three-level growth model explaining the code contribution and its growth over a six-month period. We find that value fit with the project positively influences both the level and growth of code contribution. However, there are notable differences among paid and unpaid developers in the impact of value fit on their level and growth in code contributions over time. The implications of our work will be of interest to researchers, practitioners, and organizations investing in open source projects.

Defining success in open source hardware development projects: a survey of practitioners

Abstract Recent years have seen the rise of citizens as contributors to hardware product creation. This trend has increased attention to open source hardware (OSH): a phenomenon that extends the intellectual property management and development practices in open source software (OSS) into the design of physical objects. OSH projects are different from OSS projects due to product type, and distinct from traditional closed source new product development (NPD) ones due to their openness. These differences challenge the degree of applicability of existing project success definitions in the OSH context. To investigate project success in OSH, we conducted a qualitative survey with practitioners. We report characteristics of successful OSH projects through three identified themes: (a) value creation – the big-picture impact, (b) quality of output – the quality of the hardware and accompanying documentation and (c) project process – activities that contribute to success. We contextualise by comparing OSH with selected literature on the success of OSS and NPD project management. While our study confirms a similarity between OSS and OSH in defining project success, it also highlights themes that are uniquely important to the latter. These findings are helpful for OSH development practice and could provide lessons for OSS development and closed source NPD.

Optimizing a Variational Quantum Classifier through the Behavior Analysis of its Components

Quantum computer introduces a novel approach to process information. In quantum information processing, the law of quantum mechanics is applied to solve many practical computational problems. Classification is one such problem that can be resolved efficiently with the gate model quantum computer. There are several types of classifiers available in quantum domain, such as- variational quantum classifier (VQC), Quantum Support Vector Machine (QSVM) with Kernel Approximation, Hybrid Quantum Neural Network (QNN) etc. However, in this study, the mathematical similarities between VQC and classical support vector machine (SVM) and the components of the VQC are analyzed to optimize the performance of the classifier. For the convenience of the study, publicly available datasets, such as- IRIS dataset and Breast cancer dataset, are used in the experiments. IRIS dataset is brought into play for the testing and breast cancer dataset dimension reduced by Principle component analysis (PCA) is for validity test of the optimized VQC. After studying the VQC components in detail, it is found that the optimized VQC outperforms some of the classical machine learning algorithms or sometimes works as similar as classical SVM. The optimized VQC algorithm classifies IRIS dataset with 100% of accuracy and PCA dimension reduced Breast cancer dataset with 90% of accuracy. All of these studies are conducted with the help of Qiskit- an open-source software development kit (SKD) which is developed by IBM. So, the quantum device is considered to be ideal in every experiment.

An Open-Source Workflow for Spatiotemporal Studies with COVID-19 as an Example

Many previous studies have shown that open-source technologies help democratize information and foster collaborations to enable addressing global physical and societal challenges. The outbreak of the novel coronavirus has imposed unprecedented challenges to human society. It affects every aspect of livelihood, including health, environment, transportation, and economy. Open-source technologies provide a new ray of hope to collaboratively tackle the pandemic. The role of open source is not limited to sharing a source code. Rather open-source projects can be adopted as a software development approach to encourage collaboration among researchers. Open collaboration creates a positive impact in society and helps combat the pandemic effectively. Open-source technology integrated with geospatial information allows decision-makers to make strategic and informed decisions. It also assists them in determining the type of intervention needed based on geospatial information. The novelty of this paper is to standardize the open-source workflow for spatiotemporal research. The highlights of the open-source workflow include sharing data, analytical tools, spatiotemporal applications, and results and formalizing open-source software development. The workflow includes (i) developing open-source spatiotemporal applications, (ii) opening and sharing the spatiotemporal resources, and (iii) replicating the research in a plug and play fashion. Open data, open analytical tools and source code, and publicly accessible results form the foundation for this workflow. This paper also presents a case study with the open-source spatiotemporal application development for air quality analysis in California, USA. In addition to the application development, we shared the spatiotemporal data, source code, and research findings through the GitHub repository.

Enhancing Hybrid OSS Development Through Agile Methods and High Media Synchronicity

Open source software (OSS) is increasingly being developed by hybrid teams that consist of a mix of company employees and volunteer developers. While hybrid OSS teams are becoming more prevalent, they also face unique challenges due to the involvement of different constituents. To address those challenges, this paper develops and validates a new organizing model. Specifically, the paper draws on media synchronicity theory (MST) to theorize that hybrid OSS teams would benefit from adopting an organizing model that involves practicing agile methods and using communication tools with multiple symbols sets and high transmission velocity. The paper also extends MST by conceptualizing the theory's key concept of communication convergence as consisting of two distinct dimensions: affective and cognitive convergence. Using primary survey data from hybrid OSS teams, the paper presents empirical evidence that such an organizing model can enhance those teams' affective convergence and cognitive convergence and, in turn, their development productivity and the quality of the software. In addition, the results show that affective convergence has a stronger impact on hybrid OSS teams' performance than cognitive convergence.

An Overview of Remote Sensing Data Applications in Peatland Research Based on Works from the Period 2010–2021

In the 21st century, remote sensing (RS) has become increasingly employed in many environmental studies. This paper constitutes an overview of works utilising RS methods in studies on peatlands and investigates publications from the period 2010–2021. Based on fifty-nine case studies from different climatic zones (from subarctic to subtropical), we can indicate an increase in the use of RS methods in peatland research during the last decade, which is likely a result of the greater availability of new remote sensing data sets (Sentinel 1 and 2; Landsat 8; SPOT 6 and 7) paired with the rapid development of open-source software (ESA SNAP; QGIS and SAGA GIS). In the studied works, satellite data analyses typically encompassed the following elements: land classification/identification of peatlands, changes in water conditions in peatlands, monitoring of peatland state, peatland vegetation mapping, Gross Primary Productivity (GPP), and the estimation of carbon resources in peatlands. The most frequently employed research methods, on the other hand, included: vegetation indices, soil moisture indices, water indices, supervised classification and machine learning. Remote sensing data combined with field research is deemed helpful for peatland monitoring and multi-proxy studies, and they may offer new perspectives on research at a regional level.

Refrapy: A Python program for seismic refraction data analysis

The development and use of open-source software in geophysics over the last decade have grown considerably. However, there are few open-software alternatives for seismic methods and even fewer regarding seismic refraction data processing and inversion. We present Refrapy, an open-source package for seismic refraction data analysis written in Python, using some of the main libraries for scientific computing and geosciences. Divided into two main programs (Refrapick and Refrainv), it is possible to perform basic waveform processing, picking of first breaks, and inversion through time-terms analysis or traveltimes tomography, all with GUIs interaction. To evaluate and validate the products obtained using Refrapy, we analyzed sets of synthetic data and real field data. The calculated models recovered the geometry and velocities values of the actual synthetic models satisfactorily. The joint interpretation of the results obtained with both available inversion techniques permitted a more detailed interpretation. As for the analysis of real data, the results presented a compatible response compared to the model obtained by a well-established commercial software, satisfactorily representing the geological context interpreted initially. The software presented in this work is expected to contribute to the activities of researchers, practitioners, and students regarding the analysis of seismic refraction data as a free, open, and user-friendly alternative.

Open-source Software Sustainability Models: Initial White Paper From the Informatics Technology for Cancer Research Sustainability and Industry Partnership Working Group.

BackgroundThe National Cancer Institute Informatics Technology for Cancer Research (ITCR) program provides a series of funding mechanisms to create an ecosystem of open-source software (OSS) that serves the needs of cancer research. As the ITCR ecosystem substantially grows, it faces the challenge of the long-term sustainability of the software being developed by ITCR grantees. To address this challenge, the ITCR sustainability and industry partnership working group (SIP-WG) was convened in 2019.ObjectiveThe charter of the SIP-WG is to investigate options to enhance the long-term sustainability of the OSS being developed by ITCR, in part by developing a collection of business model archetypes that can serve as sustainability plans for ITCR OSS development initiatives. The working group assembled models from the ITCR program, from other studies, and from the engagement of its extensive network of relationships with other organizations (eg, Chan Zuckerberg Initiative, Open Source Initiative, and Software Sustainability Institute) in support of this objective.MethodsThis paper reviews the existing sustainability models and describes 10 OSS use cases disseminated by the SIP-WG and others, including 3D Slicer, Bioconductor, Cytoscape, Globus, i2b2 (Informatics for Integrating Biology and the Bedside) and tranSMART, Insight Toolkit, Linux, Observational Health Data Sciences and Informatics tools, R, and REDCap (Research Electronic Data Capture), in 10 sustainability aspects: governance, documentation, code quality, support, ecosystem collaboration, security, legal, finance, marketing, and dependency hygiene.ResultsInformation available to the public reveals that all 10 OSS have effective governance, comprehensive documentation, high code quality, reliable dependency hygiene, strong user and developer support, and active marketing. These OSS include a variety of licensing models (eg, general public license version 2, general public license version 3, Berkeley Software Distribution, and Apache 3) and financial models (eg, federal research funding, industry and membership support, and commercial support). However, detailed information on ecosystem collaboration and security is not publicly provided by most OSS.ConclusionsWe recommend 6 essential attributes for research software: alignment with unmet scientific needs, a dedicated development team, a vibrant user community, a feasible licensing model, a sustainable financial model, and effective product management. We also stress important actions to be considered in future ITCR activities that involve the discussion of the sustainability and licensing models for ITCR OSS, the establishment of a central library, the allocation of consulting resources to code quality control, ecosystem collaboration, security, and dependency hygiene.

Relasi kuasa dalam pengembangan perangkat lunak open source perpustakaan perguruan tinggi di Bali

Introductions. Discussions on critical perspectives of open source software is limited as most studies focus on technical aspects. This study examines aspects of process, form, ideology, and implications of power relations in the development of open source software for university libraries in Bali, observed from non-technical aspects of the system.
 Research methods. The paper used cultural studies method with semi-structured interviews, observation, and documentation as data collection methods.
 Data analysis. This study used genealogy and deconstruction methods involving Foucault and Derrida theory.
 Results and discussion. The process of power relations in the development of open source software for university libraries in Bali is not only comes in parallel, but also in hegemonic relations. These relations are based on knowledge and influenced by the ideology of librarianship, free, open source, and capitalist which have implications for libraries, librarians, and users.
 Conclusions and suggestions. The practice of power relations in the development of open source software for university libraries in Bali occurred through knowledge and hegemonic power. Therefore, human resources should also improve their communication skills.

Measuring and Modeling Group Dynamics in Open-Source Software Development: A Tensor Decomposition Approach

Many open-source software projects depend on a few core developers, who take over both the bulk of coordination and programming tasks. They are supported by peripheral developers, who contribute either via discussions or programming tasks, often for a limited time. It is unclear what role these peripheral developers play in the programming and communication efforts, as well as the temporary task-related sub-groups in the projects. We mine code-repository data and mailing-list discussions to model the relationships and contributions of developers in a social network and devise a method to analyze the temporal collaboration structures in communication and programming, learning about the strength and stability of social sub-groups in open-source software projects. Our method uses multi-modal social networks on a series of time windows. Previous work has reduced the network structure representing developer collaboration to networks with only one type of interaction, which impedes the simultaneous analysis of more than one type of interaction. We use both communication and version-control data of open-source software projects and model different types of interaction over time. To demonstrate the practicability of our measurement and analysis method, we investigate 10 substantial and popular open-source software projects and show that, if sub-groups evolve, modeling these sub-groups helps predict the future evolution of interaction levels of programmers and groups of developers. Our method allows maintainers and other stakeholders of open-source software projects to assess instabilities and organizational changes in developer interaction and can be applied to different use cases in organizational analysis, such as understanding the dynamics of a specific incident or discussion.

Moving beyond velocity: Opportunities and challenges to quantify immune cell behavior.

The analysis of cellular behavior using intravital multi-photon microscopy has contributed substantially to our understanding of the priming and effector phases of immune responses. Yet, many questions remain unanswered and unexplored. Though advancements in intravital imaging techniques and animal models continue to drive new discoveries, continued improvements in analysis methods are needed to extract detailed information about cellular behavior. Focusing on dendritic cell (DC) and T cell interactions as an exemplar, here we discuss key limitations for intravital imaging studies and review and explore alternative approaches to quantify immune cell behavior. We touch upon current developments in deep learning models, as well as established methods from unrelated fields such as ecology to detect and track objects over time. As developments in open-source software make it possible to process and interactively view larger datasets, the challenge for the field will be to determine how best to combine intravital imaging with multi-parameter imaging of larger tissue regions to discover new facets of leukocyte dynamics and how these contribute to immune responses.

On the analysis of non-coding roles in open source development

The role of non-coding contributors in Open Source Software (OSS) is poorly understood. Most of current research around OSS development focuses on the coding aspects of the project (e.g., commits, pull requests or code reviews) while ignoring the potential of other types of contributions. Often, due to the assumption that these other contributions are not significant in number and that, in any case, they are handled by the same people that are also part of the “coding team”. This paper aims to investigate whether this is actually the case by analyzing the frequency and diversity of non-coding contributions in OSS development. As a sample of projects for our study we have taken the 100 most popular projects in the ecosystem of NPM, a package manager for JavaScript. Our results validate the importance of dedicated non-coding contributors in OSS and the diversity of OSS communities as, typically, a contributor specializes in a specific subset of roles. We foresee that projects adopting explicit policies to attract and onboard them could see a positive impact in their long-term sustainability providing they also put in place the right governance strategies to facilitate the migration and collaboration among the different roles. As part of this work, we also provide a replicability package to facilitate further quantitative role-based analysis by other researchers.

Social Exchange and the Reciprocity Roller Coaster: Evidence from the Life and Death of Virtual Teams

Organizations are riddled with cooperation problems, that is, instances in which workers need to voluntarily exert effort to achieve efficient collective outcomes. To sustain high levels of cooperation, the experimental literature demonstrates the centrality of reciprocal preferences but has also overlooked some of its negative consequences. In this paper, we ran lab-in-the-field experiments in the context of open-source software development teams to provide the first field evidence that highly reciprocating groups are not necessarily more successful in practice. Instead, the relationship between high reciprocity and performance can be more accurately described as U-shaped. Highly reciprocal teams are generally more likely to fail and only outperform other teams conditional on survival. We use the dynamic structure of our data on field contributions to demonstrate the underlying theoretical mechanism. Reciprocal preferences work as a catalyst at the team level: they reinforce the cooperative equilibrium in good times but also make it harder to recover from a negative signal (the project dies). Our results call into question the idea that strong reciprocity can shield organizations from cooperation breakdowns. Instead, cooperation needs to be dynamically managed through relational contracts. History: This paper has been accepted for the Organization Science Special Issue on Experiments in Organizational Theory. Funding: This research was funded by the consolidator grant agreement [Grant 647870] from the European Research Council, European Union’s Horizon 2020 Research and Innovation Programme (Yann Algan). Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2021.1515 .

Development of open-source software for free-hand 3D vascular ultrasound: Dialysis fistula application.

The arteriovenous fistula (AVF) is the preferred vascular access for End Stage Renal Disease, having superior patency and lower infection risks than prosthetic graft and catheter access. When AVF dysfunction or delayed maturation does occur, the gold standard for diagnosis is the fistula angiogram (a.k.a. fistulogram). 3D ultrasound is available for obstetrical and other specialized uses, but it is cost prohibitive and has a field of view that is too small to cover the region of interest for the dialysis fistula application. We sought to develop a point of care 3D solution using freehand 2D ultrasound data acquisition. We developed open-source software for 3D image reconstruction and projection of an angiogram-like image of the vascular access using a 2D freehand ultrasound scanner. We evaluated this software by comparing the ultrasound "sono-angiogram" images to fistulogram images in five subjects, using visual inspection and by applying the Percent of Exact Match (PEM) as a statistic test. The sono-angiograms showed identifiable characteristics that matched the fistulogram results in all five subjects. The PEM ranged between 42.8% and 77.0%, with Doppler and grayscale ultrasound data, showing complementary advantages and disadvantages when used for sono-angiogram image construction. Motion from freehand ultrasound acquisition was a significant source of mismatch. 3D image generation is a potential advantage with ultrasound data. While further work is needed to improve the accuracy with free hand scanning, fistulogram-like "sono-angiograms" can be generated using point of care 2D ultrasound. Methods such as these may be able to assist in point-of-care diagnosis in the future. The software is open-source, and importantly, the ultrasound data used are non-proprietary and available from any standard ultrasound machine. The simplicity and accessibility of this approach warrant further study.

Predicting Response of Lung Cancer with Definitive Radiation Therapy: A Radiomics-Based Prediction Model

We hypothesize that radiomic features provide information for predicting early treatment response of lung cancer with definitive radiation therapy (RT). This study aims to develop and validate a radiomics-based model for predicting early treatment response of lung cancer with definitive radiation therapy (RT).From January 2018 to March 2020, 128 lung cancer patients with definitive radiation therapy (RT) had enrolled retrospectively in the study and randomly divided into training set (n = 90) and test set(n = 38). Based on the Response Evaluation Criteria in Solid Tumors1.1 (RECIST1.1), response of lung cancer with definitive radiation therapy (RT) was dichotomized that treatment was effective if tumor volume decreased over 30༅ at the first follow-up CT after definitive radiation therapy (RT). Radiomic features were extracted using an open-source software development tool from the Gross Tumor Volume (GTV) of planning CT images. Specifically, a total of 107 radiomics features are extracted that include the first-order statistic features, the second-order texture features, and 2D-3D shape-based features. To reduce the feature dimensionality, we used the RFE (Recursive Feature Elimination) method to filter the features and obtained 13 out of 107 features that are most effective for final classification. By analyzing the data and comparing the experimental validation, we employed Random Forest classifier for the response prediction.All patients received definitive radiation therapy (56-70Gy) and no operation. 36 patients received Intensity-modulated radiation therapy (IMRT) and 92 patients received Volumetric Intensity Modulated Arc Therapy (VMAT) (P = 0.951). 103 patients are men and 25 patients are women (P = 0.969). In addition to the five-fold cross-validation, we evaluated the classification results using the independent test set. The AUC for five-fold cross validation is 0.798, and the AUC for the independent test set is 0.771.This study demonstrated that radiomic model shows a good prediction value of early treatment response of lung cancer with definitive radiation therapy (RT). Radiomic features extracted from the Gross Tumor Volume (GTV) of planning CT images may predict response of radiation and might be helpful for individual treatments. The result still needs to be further validated by increasing the amount of data, especially prospective data.

An analysis of open source software licensing questions in Stack Exchange sites

Free and open source software is widely used in the creation of software systems, whereas many organizations choose to provide their systems as open source. Open source software carries licenses that determine the conditions under which the original software can be used. Appropriate use of licenses requires relevant expertise by the practitioners, and has an important legal angle. Educators and employers need to ensure that developers have the necessary training to understand licensing risks and how they can be addressed. At the same time, it is important to understand which issues practitioners face when they are using a specific open source license, when they are developing new open source software products or when they are reusing open source software. In this work, we examine questions posed about open source software licensing using data from the following Stack Exchange sites: Stack Overflow, Software Engineering, Open Source and Law. We analyze the indication of specific licenses and topics in the questions, investigate the attention the posts receive and trends over time, whether appropriate answers are provided and which type of questions are asked. Our results indicate that practitioners need, among other, clarifications about licensing specific software when other licenses are used, and for understanding license content. The results of the study can be useful for educators and employers, organizations that are authoring open source software licenses and developers for understanding the issues faced when using licenses, whereas they are relevant to other software engineering research areas, such as software reusability.

The "Shut the f**k up" Phenomenon: Characterizing Incivility in Open Source Code Review Discussions

Code review is an important quality assurance activity for software development. Code review discussions among developers and maintainers can be heated and sometimes involve personal attacks and unnecessary disrespectful comments, demonstrating, therefore, incivility. Although incivility in public discussions has received increasing attention from researchers in different domains, the knowledge about the characteristics, causes, and consequences of uncivil communication is still very limited in the context of software development, and more specifically, code review. To address this gap in the literature, we leverage the mature social construct of incivility as a lens to understand confrontational conflicts in open source code review discussions. For that, we conducted a qualitative analysis on 1,545 emails from the Linux Kernel Mailing List (LKML) that were associated with rejected changes. We found that more than half (66.66%) of the non-technical emails included uncivil features. Particularly, frustration, name calling, and impatience are the most frequent features in uncivil emails. We also found that there are civil alternatives to address arguments, while uncivil comments can potentially be made by any people when discussing any topic. Finally, we identified various causes and consequences of such uncivil communication. Our work serves as the first study about the phenomenon of in(civility) in open source software development, paving the road for a new field of research about collaboration and communication in the context of software engineering activities.

Enabling Collaborative Data Science Development with the Ballet Framework

While the open-source software development model has led to successful large-scale collaborations in building software systems, data science projects are frequently developed by individuals or small teams. We describe challenges to scaling data science collaborations and present a conceptual framework and ML programming model to address them. We instantiate these ideas in Ballet, the first lightweight framework for collaborative, open-source data science through a focus on feature engineering, and an accompanying cloud-based development environment. Using our framework, collaborators incrementally propose feature definitions to a repository which are each subjected to software and ML performance validation and can be automatically merged into an executable feature engineering pipeline. We leverage Ballet to conduct a case study analysis of an income prediction problem with 27 collaborators, and discuss implications for future designers of collaborative projects.

Open source software reliability model with nonlinear fault detection and fault introduction

AbstractIn recent years, open source software (OSS) has been widely used and developed. The reliability of OSS has also become a hot research topic. Due to the complexity, dynamics, and uncertainty of the development and testing process of OSS, it is also a difficult task to accurately evaluate the reliability of OSS. In this paper, we consider the nonlinear changes of fault detection and fault introduction in the development and testing of OSS and propose a reliability model of OSS based on the nonlinear changes of fault detection and fault introduction. Experimental results show that the proposed model is more in line with the actual changes of fault detection and fault introduction in the processes of OSS development and testing. Compared with other models, the proposed model has better fitting and predictive performance. We also give a simple OSS optimal release method considering the testing‐effort and reliability changes in the processes of OSS development and testing. The proposed model and optimized release method can effectively assist OSS developers to evaluate software reliability and determine the optimal release time.