Open Source Community Research Articles

NURTURING DOMESTIC OPEN-SOURCE TALENTS TO EMPOWER NEW-QUALITY PRODUCTIVITY: CONNOTATIVE CHARACTERISTICS AND DIRECTION OF PROMOTION

The domestic open source industry is showing a vigorous development trend, with many industrial-level open source projects springing up, injecting new vitality into China's independent innovation and industrial development. However, in the face of global competition, we must face up to the challenges we are currently facing. China's open source community is still in a disadvantageous position on the international stage. There is a significant gap between the performance of enterprises in the open source field and that of foreign countries, and there is also a serious shortage of open source talent reserves in domestic universities. In view of this, this article deeply analyzes the shortcomings in the development of open source industry talents, focuses on how to inject strong impetus into new productivity, and deeply explores the connotation characteristics and promotion path of open source talent cultivation, emphasizing the key role of open source talents in the formation of new productivity. Based on these analyses, this article proposes a series of countermeasures and suggestions, including supporting the development of open source communities, strengthening the construction of open source talent teams in enterprises, strengthening the cultivation and echelon construction of open source talents in universities, integrating talent elements and scientific and educational resources, and exerting the siphon effect of talents, aiming to help China achieve a leading position in the open source industry and promote the sustainable development of new productivity.

Blockchain Handshaking with Software Assurance: Version++ Protocol for Bitcoin Cryptocurrency

Cryptocurrency software implements cryptocurrency operations (including the distributed consensus protocol and peer-to-peer networking) and often involves the open-source community. We design a software assurance scheme for cryptocurrency and advance the cryptocurrency handshaking protocol by providing the verification capability of the Bitcoin software by peers and preventing any potential peer from establishing a connection with modified Bitcoin software. Since we focus on Bitcoin (the most popular cryptocurrency) for implementation and integration, we call our scheme Version++, built on and advancing the current Bitcoin handshaking protocol based on the Version message. Our Version++ protocol providing software assurance is distinguishable from previous research because it is permissionless, distributed, and lightweight for its cryptocurrency application. Our scheme is permissionless since it does not require a centralized trusted authority (unlike the remote software attestation techniques from trusted computing); it is distributed since the peer checks the software assurances of its own peer connections; and it is designed for efficiency/lightweight to support the dynamic nature of the peer connections and large-scale broadcasting in cryptocurrency networking. Utilizing Merkle Tree for the efficiency of the proof verification, we implement and test Version++ on Bitcoin software and conduct experiments in an active Bitcoin node prototype connected to the Bitcoin Mainnet. Our prototype-based performance analyses demonstrate the lightweight design of Version++. The peer-specific verification grows logarithmically with the number of software files in processing time and in storage. Furthermore, the Version++ verification overhead is small compared to the version-verack handshaking process; we measure the overhead to be 0.524% in our local networking environment between virtual machines and between 0.057% and 0.282% (depending on the peer location) in our more realistic cloud-based experiments with remote peer machines.

SzCORE: Seizure Community Open-Source Research Evaluation framework for the validation of electroencephalography-based automated seizure detection algorithms.

The need for high-quality automated seizure detection algorithms based on electroencephalography (EEG) becomes ever more pressing with the increasing use of ambulatory and long-term EEG monitoring. Heterogeneity in validation methods of these algorithms influences the reported results and makes comprehensive evaluation and comparison challenging. This heterogeneity concerns in particular the choice of datasets, evaluation methodologies, and performance metrics. In this paper, we propose a unified framework designed to establish standardization in the validation of EEG-based seizure detection algorithms. Based on existing guidelines and recommendations, the framework introduces a set of recommendations and standards related to datasets, file formats, EEG data input content, seizure annotation input and output, cross-validation strategies, and performance metrics. We also propose the EEG 10-20 seizure detection benchmark, a machine-learning benchmark based on public datasets converted to a standardized format. This benchmark defines the machine-learning task as well as reporting metrics. We illustrate the use of the benchmark by evaluating a set of existing seizure detection algorithms. The SzCORE (Seizure Community Open-Source Research Evaluation) framework and benchmark are made publicly available along with an open-source software library to facilitate research use, while enabling rigorous evaluation of the clinical significance of the algorithms, fostering a collective effort to more optimally detect seizures to improve the lives of people with epilepsy.

Software bug prediction using graph neural networks and graph-based text representations

While open-source communities keep growing at an impressive pace, the corresponding platforms where software engineers share their work, deliberate about software-related issues, point out undetected bugs and suggest potential features that could be incorporated, usually suffer from information overload. This paper proposes a hybrid approach that builds on the strengths of word embeddings techniques, graph-based representation of textual data and graph neural networks for the prediction of software bugs. Existing approaches aim to improve each of these components individually, thus neglecting structural or semantical underlying information. On the contrary, the approach presented in this paper aims to leverage both types of information by representing each text as a graph and utilizing Graph Attention Networks (GATs). The evaluation carried out in this paper demonstrates an overall improvement for a variety of metrics (i.e., accuracy, precision and recall), in comparison with a plethora of graph-based machine learning models. The experiments took place using four datasets of short-text GitHub and Jira issues, which are publicly available on the kaggle.com and zenodo.com platforms respectively.

Dynamics of Competition on Openness Strategies and Software Maintenance

Software firms are increasingly adopting an open source strategy, allowing them to leverage the effort exerted by the open source community toward improving software quality. In addition to embracing a proprietary or fully open source strategy, several firms choose a partial openness strategy wherein only certain parts of the code are open source while the rest is proprietary. Specifically, when adopting a partial openness strategy, a firm may choose to make the core software code open source while keeping the extension software code proprietary or keep the core proprietary and make the extension open source. When making decisions related to different openness strategies, firms need to take into account the level of effort they are exerting toward the improvement of the quality of software, the level of engagement of the open source community, and pricing. Hence, the decisions related to a firm’s openness strategy are not straightforward. While this is an important question for many firms, it has not been analyzed in the literature. In this research, we attempt to fill this important gap by analyzing different openness strategies in the context of resource allocation for fixing defects. Specifically, using a game-theoretic model, we explore when a firm should make its software fully open source or partially open source and when it should keep it proprietary. Our results show that when the baseline demand for the firm increases with the extent of openness, the firm should either make its software fully open or keep it proprietary and, importantly, should not rely on partial openness. Next, in scenarios where customers are highly sensitive to security risks, the demand loss to efficiency gain ratio has an important role in determining a firm’s optimal openness strategies. These findings provide important insights to firms on how to effectively plan their openness strategies and also establish a basis for future research on the topic of partial openness.

Factors influencing students’ sustained use of open source communities

Factors influencing students’ sustained use of open source communities

A Web Semantic Mining Method for Fake Cybersecurity Threat Intelligence in Open Source Communities

In order to overcome the challenges of inadequate classification accuracy in existing fake cybersecurity threat intelligence mining methods and the lack of high-quality public datasets for training classification models, we propose a novel approach that significantly advances the field. We improved the attention mechanism and designed a generative adversarial network based on the improved attention mechanism to generate fake cybersecurity threat intelligence. Additionally, we refine text tokenization techniques and design a detection model to detect fake cybersecurity threats intelligence. Using our STIX-CTIs dataset, our method achieves a remarkable accuracy of 96.1%, outperforming current text classification models. Through the utilization of our generated fake cybersecurity threat intelligence, we successfully mimic data poisoning attacks within open-source communities. When paired with our detection model, this research not only improves detection accuracy but also provides a powerful tool for enhancing the security and integrity of open-source ecosystems.

Similaridade Semântica

In the rapidly evolving field of Natural Language Processing (NLP), understanding the domain of semantic similarity is of paramount importance for both academic and industrial applications. This article presents a comprehensive domain analysis of semantic similarity, integrating a multidisciplinary approach that encompasses key concepts, interrelations among these facets, stakeholders, information practices, and existing classification systems. We elucidate the core ideas, such as lexical and syntactic similarity, embeddings, and various similarity metrics, and demonstrate their interrelatedness. The paper also identifies and characterizes the diverse array of stakeholders involved in this domain, from academic researchers and tech leads to policymakers and open-source communities. Furthermore, we explore how information is disseminated and used within this domain, including an examination of research publication trends and industry reports. Lastly, the article assesses existing classification systems and ontologies that structure the knowledge in this field. Our findings serve as a foundational framework for future research, development, and ethical considerations in the semantic similarity domain. This in-depth analysis aspires to guide both newcomers and seasoned experts through the intricate landscape of semantic similarity, thereby contributing to the field's holistic advancement.

Free open source communities sustainability: Does it make a difference in software quality?

ContextFree and Open Source Software (FOSS) communities’ ability to stay viable and productive over time is pivotal for society as they maintain the building blocks that digital infrastructure, products, and services depend on. Sustainability may, however, be characterized from multiple aspects, and less is known how these aspects interplay and impact community outputs, and software quality specifically.ObjectiveThis study, therefore, aims to empirically explore how the different aspects of FOSS sustainability impact software quality.Method16 sustainability metrics across four categories were sampled and applied to a set of 217 OSS projects sourced from the Apache Software Foundation Incubator program. The impact of a decline in the sustainability metrics was analyzed against eight software quality metrics using Bayesian data analysis, which incorporates probability distributions to represent the regression coefficients and intercepts.ResultsFindings suggest that selected sustainability metrics do not significantly affect defect density or code coverage. However, a positive impact of community age was observed on specific code quality metrics, such as risk complexity, number of very large files, and code duplication percentage. Interestingly, findings show that even when communities are experiencing sustainability, certain code quality metrics are negatively impacted.ConclusionFindings imply that code quality practices are not consistently linked to sustainability, and defect management and prevention may be prioritized over the former. Results suggest that growth, resulting in a more complex and large codebase, combined with a probable lack of understanding of code quality standards, may explain the degradation in certain aspects of code quality.

Open Archaeology, Open Source? Collaborative practices in an emerging community of archaeological software engineers

In this article, we investigate modes of collaboration in this emerging community of practice using 'open-archaeo ', a curated list of archaeological software, and data on the activity of associated GitHub repositories and users. We conduct an exploratory quantitative analysis to characterise the nature and intensity of these collaborations and map the collaborative networks that emerge from them. We document uneven adoption of open source collaborative practices beyond the basic use of git as a version control system and GitHub to host source code. Most projects do make use of collaborative features and, through shared contributions, we can trace a collaborative network that includes the majority of archaeologists active on GitHub. However, a majority of repositories have 1-3 contributors, with only a few projects distinguished by an active and diverse developer base. Direct collaboration on code or other repository content - as opposed to the more passive, social media-style interaction that GitHub supports – remains very limited. In other words, there is little evidence that archaeologists ' adoption of open-source tools (git and GitHub) has been accompanied by the decentralised, participatory forms of collaboration that characterise other open-source communities. On the contrary, our results indicate that research software engineering in archaeology remains largely embedded in conventional professional norms and organisational structures of academia.

Automated Commit Intelligence by Pre-training

GitHub commits, which record the code changes with natural language messages for description, play a critical role in software developers’ comprehension of software evolution. Due to their importance in software development, several learning-based works are conducted for GitHub commits, such as commit message generation and security patch identification. However, most existing works focus on customizing specialized neural networks for different tasks. Inspired by the superiority of code pre-trained models, which has confirmed their effectiveness across different downstream tasks, to promote the development of open-source software community, we first collect a large-scale commit benchmark including over 7.99 million commits across 7 programming languages. Based on this benchmark, we present CommitBART, a pre-trained encoder-decoder Transformer model for GitHub commits. The model is pre-trained by three categories (i.e., denoising objectives, cross-modal generation, and contrastive learning) for six pre-training tasks to learn commit fragment representations. Our model is evaluated on one understanding task and three generation tasks for commits. The comprehensive experiments on these tasks demonstrate that CommitBART significantly outperforms previous pre-trained works for code. Further analysis also reveals that each pre-training task enhances the model performance.

Large language models from OpenAI, Google, Meta, X and Co. : The role of "closed" and "open" models in radiology

In 2023, the release of ChatGPT triggered an artificial intelligence (AI) boom. The underlying large language models (LLM) of the nonprofit organization "OpenAI" are not freely available under open-source licenses, which does not allow on-site implementation inside secure clinic networks. However, efforts are being made by open-source communities, start-ups and large tech companies to democratize the use of LLMs. This opens up the possibility of using LLMs in adata protection-compliant manner and even adapting them to our own data. This paper aims to explain the potential of privacy-compliant local LLMs for radiology and to provide insights into the "open" versus "closed" dynamics of the currently rapidly developing field of AI. PubMed search for radiology articles with LLMs and subjective selection of references in the sense of anarrative key topic article. Various stakeholders, including large tech companies such as Meta, Google andX, but also European start-ups such as Mistral AI, contribute to the democratization of LLMs by publishing the models (open weights) or by publishing the model and source code (open source). Their performance is lower than current "closed" LLMs, such as GPT‑4 from OpenAI. Despite differences in performance, open and thus locally implementable LLMs show great promise for improving the efficiency and quality of diagnostic reporting as well as interaction with patients and enable retrospective extraction of diagnostic information for secondary use of clinical free-text databases for research, teaching or clinical application.

Infrastructural justice for responsible software engineering,

In recent years, we have seen many examples of software products unintentionally causing demonstrable harm. Many guidelines for ethical and responsible computing have been developed in response. Dominant approaches typically attribute liability and blame to individual companies or actors, rather than understanding how the working practices, norms, and cultural understandings in the software industry contribute to such outcomes. In this paper, we propose an understanding of responsibility that is infrastructural, relational, and cultural; thus, providing a foundation to better enable responsible software engineering into the future. Our approach draws on Young's (2006) social connection model of responsibility and Star and Ruhleder's (1994) concept of infrastructure. By bringing these theories together we introduce a concept called infrastructural injustice, which offers a new way for software engineers to consider their opportunities for responsible action with respect to society and the planet. We illustrate the utility of this approach by applying it to an Open-Source software communities’ development of Deepfake technology, to find key leverage points of responsibility that are relevant to both Deepfake technology and software engineering more broadly.

OPEN SOURCE DEVELOPER SUPPORT TOOL

Abstract : This research paper explores the development and implementation of an open-source developer support tool aimed at providing financial and motivational support to open-source developers. By creating a platform where users can post questions with optional bounties, this project seeks to address the longstanding issue of uncompensated efforts within the open-source community, which has been a significant barrier to the sustainability and growth of many open-source projects. The platform leverages cutting-edge technologies such as Next.js, PostgreSQL, and Stripe to create a seamless and secure environment for both users and developers. Next.js is utilized for building the front-end and back-end of the application, providing server-side rendering and static site generation for improved performance and user experience. PostgreSQL serves as the robust and scalable database management system, ensuring data integrity and efficient query handling. Stripe is integrated for secure authentication and payment processing, enabling financial transactions between users and developers. The motivation behind this project stems from the recognition that open-source software is a critical component of modern technology infrastructure, yet the contributors often lack adequate financial and motivational support. This research delves deeply into the problem definition, outlining the specific challenges faced by open-source developers, including financial instability, lack of recognition, and limited feedback. The objectives of the project are clearly defined, aiming to create a sustainable support system that fosters community engagement and rewards contributors for their valuable work. This paper also details the software and hardware specifications required for the development and deployment of the platform, ensuring that the solution is both practical and scalable. Project planning is a crucial element of this research, with a comprehensive roadmap that includes requirement analysis, design, development, testing, and deployment phases. Each phase is meticulously planned to address potential challenges and ensure the successful implementation of the support tool. Finally, the future scope of the project is discussed, emphasizing the importance of continuous improvement and adaptation to meet the evolving needs of the open-source community. This includes potential enhancements such as advanced spam detection, enhanced user feedback mechanisms, and the integration of additional features to further support and motivate open-source contributors. By addressing these critical aspects, this research underscores the significance of supporting open-source contributors and highlights the potential of the proposed platform to make a meaningful impact on the sustainability and growth of open-source projects. IndexTerms – Open Source, Developer Support, Bounty System, Next.js, PostgreSQL, Stripe, Community Engagement.

CIME4R: Exploring iterative, AI-guided chemical reaction optimization campaigns in their parameter space

Chemical reaction optimization (RO) is an iterative process that results in large, high-dimensional datasets. Current tools allow for only limited analysis and understanding of parameter spaces, making it hard for scientists to review or follow changes throughout the process. With the recent emergence of using artificial intelligence (AI) models to aid RO, another level of complexity has been added. Helping to assess the quality of a model’s prediction and understand its decision is critical to supporting human-AI collaboration and trust calibration. To address this, we propose CIME4R—an open-source interactive web application for analyzing RO data and AI predictions. CIME4R supports users in (i) comprehending a reaction parameter space, (ii) investigating how an RO process developed over iterations, (iii) identifying critical factors of a reaction, and (iv) understanding model predictions. This facilitates making informed decisions during the RO process and helps users to review a completed RO process, especially in AI-guided RO. CIME4R aids decision-making through the interaction between humans and AI by combining the strengths of expert experience and high computational precision. We developed and tested CIME4R with domain experts and verified its usefulness in three case studies. Using CIME4R the experts were able to produce valuable insights from past RO campaigns and to make informed decisions on which experiments to perform next. We believe that CIME4R is the beginning of an open-source community project with the potential to improve the workflow of scientists working in the reaction optimization domain.Scientific contributionTo the best of our knowledge, CIME4R is the first open-source interactive web application tailored to the peculiar analysis requirements of reaction optimization (RO) campaigns. Due to the growing use of AI in RO, we developed CIME4R with a special focus on facilitating human-AI collaboration and understanding of AI models. We developed and evaluated CIME4R in collaboration with domain experts to verify its practical usefulness.

Trust and involvement of Cameroonian software developers in open-source projects

Trust and involvement of Cameroonian software developers in open-source projects

A Benchmark of Facial Recognition Pipelines and Co-Usability Performances of Modules

Researchers from leading technology companies, prestigious universities worldwide, and the open-source community have made substantial strides in the field of facial recognition studies in recent years. Experiments indicate that facial recognition approaches have not only achieved but surpassed human-level accuracy. A contemporary facial recognition process comprises four key stages: detection, alignment, representation, and verification. Presently, the focus of facial recognition research predominantly centers on the representation stage within the pipelines. This study conducted experiments exploring alternative combinations of nine state-of-the-art facial recognition models, six cutting-edge face detectors, three distance metrics, and two alignment modes. The co-usability performances of implementing and adapting these modules were assessed to precisely gauge the impact of each module on the pipeline. Theoretical and practical findings from the study aim to provide optimal configuration sets for facial recognition pipelines.

Reservoir Assessment Tool version 3.0: a scalable and user-friendly software platform to mobilize the global water management community

Abstract. In the modern world, dams and the artificial reservoirs behind them serve the increasing demand for water across diverse needs such as agriculture, energy production, and drinking water. As dams continue to proliferate, monitoring water availability influenced by reservoir operations is now of paramount importance. The Reservoir Assessment Tool (RAT) is a data-driven software platform that integrates satellite remote sensing with hydrological models, enabling the estimation of key reservoir parameters such as inflow, outflow, surface area, evaporation, and storage changes. The earliest version of RAT (version 1.0) was set up for 1598 reservoirs around the world with limitations in functional robustness, updating frequency, and scalability. Some of these limitations on updating frequency and functional robustness were addressed in version 2.0 that was later made operational for the intergovernmental agency of the Mekong River Commission. Recognizing the need for scalability to mobilize the global water management community to benefit from satellite remote sensing, we hereby introduce RAT version 3.0. This version is optimized for accelerating open collaboration among users for continuous improvement and customization of RAT to enable reservoir management breakthroughs. RAT 3.0 represents a wholesale overhaul from the previous versions to empower the global community of users and developers in the spirit of the open-source movement. RAT 3.0 allows reservoir monitoring advancements and new functional developments that can be freely exchanged and seamlessly integrated for continuous evolution of the software. A centralized web application has also been established to facilitate the storage and dissemination of global reservoir monitoring information along with comprehensive training resources. RAT 3.0 aspires to bridge the traditional practices of water management community with the capabilities of satellite remote sensing. The global impact of the software can be expected to increase as uptake spreads, enabling a more sustainable and equitable utilization of our planet's water resources.

DEPLOYMENT MODEL FOR SOFTWARE PROCESSES IN COLLABORATIVE AND DISTRIBUTED ENVIRONMENTS TYPICAL OF FREE AND OPEN-SOURCE FLOSS COMMUNITIES

This article presents the development of a model for deploying software processes within the context of free and open-source software FLOSS communities, characterized by the voluntary participation of people geographically distributed in different places and with diverse profiles and interests in a software development project. The Delphi method validated the model, in which 15 people interested in FLOSS communities participated. It was found that the model promotes communication and motivation of the participants in more than 80% and favors the participation of new community members, according to the answers given by the participants. The communication and motivation skills of the members of a FLOSS community are fundamental for the proper development of free software construction projects.

Cultivating Community Health and Well-being in Open Source: Mitigating Burnout and Prioritizing Mental Health

Purpose: This whitepaper addresses the critical but often overlooked mental health challenges within open-source communities, including stress and burnout. The collaborative spirit of open-source communities has undoubtedly accelerated technological innovation, but the mental well-being of their contributors is frequently neglected. The objective is to uncover the root causes of these mental health issues and evaluate their impact, thereby underscoring the need for supportive and inclusive community practices.
 Methodology: This paper uses the ‘Mixed methods’ research approach, analyzing data from industry surveys and extracting insights from personal narratives and experiences documented by contributors in blogs and forums. This comprehensive strategy allows for a detailed exploration of mental health within the open-source environment, facilitating the identification of key stress factors and mental health challenges associated with community participation.
 Findings: The investigation reveals a direct link between the operational dynamics of open-source projects and the prevalence of mental health problems among participants. Key stressors include high expectations regarding workload, the predominantly volunteer nature of contributions, social isolation, and a lack of formal support structures. Additionally, the study highlights a significant lack of awareness of these issues and the availability of intervention strategies, further increasing the risk of burnout and stress among community members.
 Unique Contribution to Theory, Practice, and Policy: This paper makes a novel contribution to understanding mental health in open-source communities by merging analytical data with qualitative insights from contributor narratives. It offers recommendations for a wide range of stakeholders, from contributors to leaders and policymakers, to cultivate a culture that supports mental well-being, diversity, and sustainability. This paper presents a comprehensive blueprint for nurturing healthier, more resilient open-source communities.