Software Community Research Articles

Tools shape practice shapes tools...

This article narrates a partial history of two decades of doing graphic design with free, libre and open-source tools. We follow the development of tools and practices that allowed graphic design practice to shift, such as layouts being generated by scripts, books being rendered out of webpages and editorial workflows being transformed into collaborative environments. It threads through a body of shared knowledge and modes of designing with software, focusing on sites that are key in these design practices: layout tools, open fonts and publishing platforms. As practitioners and active participants, the authors provide reflections on the entangled positions of these tools and the possibilities for engagement that they offer. By following their uptake by groups and collectives in Europe through time, we end with an analysis of the way that changes in the wider free software community and digital practice affect these still ongoing initiatives.

Computational diplomacy: how 'hackathons for good' feed a participatory future for multilateralism in the digital age.

This article explores the role of hackathons for good in building a community of software and hardware developers focused on addressing global sustainable development goal (SDG) challenges. We theorize this movement as computational diplomacy: a decentralized, participatory process for digital governance that leverages collective intelligence to tackle major global issues. Analysing Devpost and GitHub data reveals that 30% of hackathons since 2010 have addressed SDG topics, employing diverse technologies to create innovative solutions. Hackathons serve as crucial kairos moments, sparking innovation bursts that drive both immediate project outcomes and long-term production. We propose that these events harness the neurobiological basis of human cooperation and empathy, fostering a collective sense of purpose and reducing interpersonal prejudice. This bottom-up approach to digital governance integrates software development, human collective intelligence and collective action, creating a dynamic model for transformative change. By leveraging kairos moments, computational diplomacy promotes a more inclusive and effective model for digital multilateral governance of the future. This article is part of the theme issue 'Co-creating the future: participatory cities and digital governance'.

Physiological signal analysis and open science using the Julia language and associated software

In this mini review, we propose the use of the Julia programming language and its software as a strong candidate for reproducible, efficient, and sustainable physiological signal analysis. First, we highlight available software and Julia communities that provide top-of-the-class algorithms for all aspects of physiological signal processing despite the language’s relatively young age. Julia can significantly accelerate both research and software development due to its high-level interactive language and high-performance code generation. It is also particularly suited for open and reproducible science. Openness is supported and welcomed because the overwhelming majority of Julia software programs are open source and developed openly on public platforms, primarily through individual contributions. Such an environment increases the likelihood that an individual not (originally) associated with a software program would still be willing to contribute their code, further promoting code sharing and reuse. On the other hand, Julia’s exceptionally strong package manager and surrounding ecosystem make it easy to create self-contained, reproducible projects that can be instantly installed and run, irrespective of processor architecture or operating system.

Comparing Performance in the Codes AREPO, GIZMO and GAMER-2

Modern astrophysical simulation is the main aspect of human getting insight from the cosmos. Numerical simulations became irreplaceable tools for generating various phases of the universe, the formation of galaxies, and other astrophysical phenomena. As the advances of computational power, validity and diversity of astrophysical modeling have been growing. In addition to the development of the processor performance, the diversity of the software community is also rising therefore, research between various simulation codes is worthwhile. It allowed researchers to know the nature of different simulation techniques linked to their basic algorithms. This study summarizes the basics of common simulation methods used by researchers including N-body Simulations, Smoothed Particle Hydrodynamics, Monte Carlo Methods, Finite Difference and Finite Element Methods. Furthermore, this research analyzes the basics of AREPO, GIZMO, and GAMER-2 codes, and compared the performance of these codes. Last, according to the analysis, this study discusses and demonstrates the advantages and limitations of these codes. These results shed light on guiding further exploration of cosmology simulations.

Comparative Study on the Interest in Non-Uniform Rational B-Splines Representation versus Polynomial Surface Description in a Freeform Three-Mirror Anastigmat

Novel freeform optical design methods can be classified in two categories, depending on whether they focus on the generation of a starting point or the development of new optimization tools. In this paper, we design a freeform three-mirror anastigmat (TMA) and compare different surface representations using either a differential ray tracer as a new optimization tool or a commercial ray tracer (ANSYS-ZEMAX OpticStudio). For differential ray tracing, we used FORMIDABLE (Freeform Optics Raytracer with Manufacturable Imaging Design cApaBiLitiEs), an optical design library with differential ray tracing and Non-Uniform Rational B-Splines (NURBS) optimization capabilities, available under the European Software Community License (ESCL). NURBS allow a freeform surface to be represented without needing any prior knowledge of the surface, such as the polynomial degree in polynomial descriptions. OpticStudio and other commercial optical design software are designed to optimize polynomial surfaces but are not well-suited to optimize NURBS surfaces, requiring a custom optical design library. In order to demonstrate the interest in using NURBS representation, we designed and independently optimized two freeform telescopes over different iteration cycles; with NURBS using FORMIDABLE or with XY polynomials using OpticStudio. We then compared the resulting systems using their root mean square field maps to assess the optimization quality of each surface representation. We also provided a full-system comparison, including mirror freeform departures. This study shows that NURBS can be a relevant alternative to XY polynomials for the freeform optimization of reflective three-mirror telescopes as it achieves more a uniform imaging quality in the field of view.

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.

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.

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.

Navigating the cyber forensics landscape a review of recent innovations

<p><span>The extensive relevance of digital forensics in today's data-driven environment has been emphasized in this article. The free software and the commercial software community are debatable, despite users and developers often differing views on important topics like software safety and usability. This article primarily uses pre-defined criteria and a platform-oriented approach to examine promising freeware (Magnet Forensics and Sleuth Kit) vs. profitable (ProDiscover and Oxygen Forensic Suite) mobile forensics tools. Under diverse settings, the tools' capacity to develop and analyze forensically sound digital forensic media sources is validated. After erasing data, each media type was tested again after formatting. The study concludes with a comparison matrix that may aid in determining the best-fit option for the investigation's requirements among the tools. The findings indicate the potential for freeware to supplant numerous proprietary applications, as users can opt for freeware instead of incurring costs associated with proprietary software. Furthermore, this perception can be put into practice.</span></p>

Reporting on artificial intelligence use in entrepreneurship research: Using a model card

The study of artificial intelligence is of increasing importance in the entrepreneurial domain. Despite the popularity of many artificial intelligence models, experimental studies in entrepreneurship that apply models are subject to replicability issues if they are not properly reported on. This note is a call to adopt a method of reporting on artificial intelligence models commonly used in the open source software community to ensure progress in future studies and to offer researchers a reflective opportunity to consider the appropriateness of models they use in experimental studies.

CMaize: Simplifying inter-package modularity from the build up.

There is a growing desire for inter-package modularity within the chemistry software community to reuse encapsulated code units across a variety of software packages. Most comprehensive efforts at achieving inter-package modularity will quickly run afoul of a very practical problem, being able to cohesively build the modules. Writing and maintaining build systems has long been an issue for many scientific software packages that rely on compiled languages such as C/C++. The push for inter-package modularity compounds this issue by additionally requiring binary artifacts from disparate developers to interoperate at a binary level. Thankfully, the de facto build tool for C/C++, CMake, is more than capable of supporting the myriad of edge cases that complicate writing robust build systems. Unfortunately, writing and maintaining a robust CMake build system can be a laborious endeavor because CMake provides few abstractions to aid the developer. The need to significantly simplify the process of writing robust CMake-based build systems, especially in inter-package builds, motivated us to write CMaize. In addition to describing the architecture and design of CMaize, the article also demonstrates how CMaize is used in production-level software.

The drift handling framework for open radio access networks: An experimental evaluation

The Open Radio Access Network (i.e., Open RAN) aims to transform the inflexible, proprietary RAN into one that is flexible, programmable, and disaggregated to provide intelligent solutions at the network edge for delivering the highly dynamic service demands of Beyond 5G (B5G) networks. Open RAN Working Group-2 (WG2) focuses on the architecture and standards of Artificial Intelligence/Machine Learning (AI/ML) to meet the Service Level Agreement (SLA) requirements for emerging use cases. The inherent dynamic nature of B5G networks and their applications often leads to AI/ML model performance degradation (i.e., drift), resulting in violations of SLAs, over- or under-provisioning of resources, etc.This paper proposes a drift handling framework which includes drift detection, analysis, and its adaptation to minimize the SLA violations and efficient resource utilization. The proposed drift handling framework is evaluated across three diverse use cases: (i) Quality of Service prediction, (ii) Channel Quality Indicator prediction, and (iii) User traffic prediction. The experimental evaluations are performed by using the Open RAN Software Community, and OpenAirInterface FlexRIC platforms with a real-time dataset. Results demonstrate that the proposed drift handling framework outperforms all other considered state-of-the art frameworks for all the scenarios in terms of the drift detection time, accuracy, precision, F1-score, etc.

Addressing durability in collaborative organising: Event atmospheres and polyrhythmic affectivity

Collaborative organising is known to burn like a rocket: it thrives on intense passion, relationality and creativity but quickly falls into pieces. This article explores the underestimated role of events and their affective atmospheres to sustain collaborative work. Drawing insights from two ethnographic field studies within an open-source software community and a network of impact entrepreneurs, we introduce the notion of ‘polyrhythmic affectivity’ at the core of polycentric governance. It encapsulates how frictional reverberances between three atmospherically experienced affective intensities – togetherness, dissonance and mutuality – are able to maintain emergent yet enduring order. We argue that the collective motivational force of collaborative organising, can be stabilised through a process of ‘affective commoning’ to sustain collaborative atmospheres as shared creative resources.

Machine learning based software effort estimation using development-centric features for crowdsourcing platform

Crowd-Sourced software development (CSSD) is getting a good deal of attention from the software and research community in recent times. One of the key challenges faced by CSSD platforms is the task selection mechanism which in practice, contains no intelligent scheme. Rather, rule-of-thumb or intuition strategies are employed, leading to biasness and subjectivity. Effort considerations on crowdsourced tasks can offer good foundation for task selection criteria but are not much investigated. Software development effort estimation (SDEE) is quite prevalent domain in software engineering but only investigated for in-house development. For open-sourced or crowdsourced platforms, it is rarely explored. Moreover, Machine learning (ML) techniques are overpowering SDEE with a claim to provide more accurate estimation results. This work aims to conjoin ML-based SDEE to analyze development effort measures on CSSD platform. The purpose is to discover development-oriented features for crowdsourced tasks and analyze performance of ML techniques to find best estimation model on CSSD dataset. TopCoder is selected as target CSSD platform for the study. TopCoder’s development tasks data with development-centric features are extracted, leading to statistical, regression and correlation analysis to justify features’ significance. For effort estimation, 10 ML families with 2 respective techniques are applied to get broader aspect of estimation. Five performance metrices (MSE, RMSE, MMRE, MdMRE, Pred (25) and Welch’s statistical test are incorporated to judge the worth of effort estimation model’s performance. Data analysis results show that selected features of TopCoder pertain reasonable model significance, regression, and correlation measures. Findings of ML effort estimation depicted that best results for TopCoder dataset can be acquired by linear, non-linear regression and SVM family models. To conclude, the study identified the most relevant development features for CSSD platform, confirmed by in-depth data analysis. This reflects careful selection of effort estimation features to offer good basis of accurate ML estimate.

Software Maintainability and Refactorings Prediction Based on Technical Debt Issues

Software maintainability is a crucial factor impacting cost, time and resource allocation for software development. Code refactorings greatly enhance code quality, readability, understandability and extensibility. Hence, accurate prediction methods for both maintainability and refactorings are vital for long-term project sustainability and success, offering substantial benefits to the software community as a whole. This article focuses on prediction of software maintainability and the number of needed code refactorings using technical debt data. Two approaches were explored, one compressing technical debt issues per software component and employing machine learning algorithms such as ExtraTrees, Random Forest, Decision Trees, which all obtained a high accuracy and performance. The second approach retained multiple debt issue entries and utilized a Recurrent Neural Network, although less effectively. In addition to the prediction of the requisite number of code refactorings and software maintainability for individual software components, a comprehensive analysis of technical debt issues was conducted before and after the refactoring process. The outcomes of this study contribute to the advancement of a dependable prediction system for maintainability and refactorings, presenting potential advantages to the software community in effectively managing maintenance resources. Of all the employed models, the ExtraTrees model yielded the most optimal predictive outcomes. To the best of our knowledge no other approaches of using ML techniques for this problem have been reported in the literarture. Keywords and phrases: Software Quality, Sonarqube, Refactoring, Code Smells.

A Graph-Based Approach for Modelling Quantum Circuits

A crucial task for the systematic application of model-driven engineering techniques in the development of quantum software is the definition of metamodels, as a first step towards automatic code generation and integration with other tools. The importance is even greater when considering recent work where the first extensions to UML for modelling quantum circuits are emerging and the characterisation of these extensions in terms of their suitability for a model-driven approach becomes unavoidable. After reviewing the related work, this article proposes a unified metamodel for modelling quantum circuits, together with five strategies for its use and some examples of its application. The article also provides a set of constraints for using the identified strategies, a set of procedures for transforming the models between the strategies, and an analysis of the suitability of each strategy for performing common tasks in a model-driven quantum software development environment. All of these resources will enable the quantum software community to speak the same language and use the same set of abstractions, which are key to furthering the development of tools to be built as part of future model-driven quantum software development frameworks.

The Factors Affecting User Experience Maturity in Free and Open Source Software Community: An Empirical Study

Assessing User eXperience (UX) maturity is mandatory in the free and open-source software (FOSS) community to avoid wasting resources on projects that may fall short of expectations. Best practices suggest employing UX Capability Maturity Models (UXCMMs), which often specify an evolutionary plateau toward developing a UX-matured system and quantify organizational UX maturity into maturity levels ranging from no or ad hoc improvement to integrated continuous improvement. Numerous generically developed UXCMMs exist. However, none is created for the FOSS community, and less information is available to support their perfect match. Thus, raise suspicions if they have proper UX maturity influencing factors (UXMIFs) for the FOSS community or measure the right thing. The FOSS community differs from traditional software-developing communities and may have different dynamics and UXMIFs, necessitating unique and new methodologies for their discovery. This study, therefore, aims to identify the FOSS community’s UXMIFs and evaluate the community’s knowledge of UX maturity-related concepts. Its findings may help future researchers, practitioners, and other FOSS developers to develop UXCMMs exclusive to the FOSS community. The study’s design is both qualitative and quantitative. It adopted a systematic literature review, interviews, fuzzy Delphi Method, and thematic analysis to collect and analyze data and present the findings. The study’s sample included sixty-two active FOSS projects, fifteen FOSS stakeholders, and twelve UX experts. The outcome shows that 84% of UX experts agreed on the thirty-six FOSS’s UXMIFs with threshold d = 0.143 and crisp values greater than α-cut = 0.5. User feedback and adopted technologies were ranked first, while learnability and use speed were ranked last. Similarly, FOSS stakeholders have shown a shared understanding of UX maturity, connected concepts, and impacting factors.

From Feminist Servers to Feminist Federation

Situated within the technofeminist care practices of feminist servers, this text explores the possibilities of feminist federation. Speaking from our collective practice of system administration, we start by introducing Systerserver, laying out the feminist pedagogies that inform our practice of learning and doing together with technologies and the politics of maintenance and care. We then revisit the identity politics of feminist servers as more than safe/r spaces in the cis-male-dominated domain of free/libre and open source software communities. Finally, we reflect on our experiences of building and federating a feminist video platform with the PeerTube software on Systerserver. Facing the techno-social challenges around the protocol of federation and adapting the software alongside our federating practice, we focus on sustainable and care-oriented alternatives to ‘scaling up’ the affective infrastructures of our feminist servers.

Embedded Systems and Software: Enabling Innovation in the Digital Age

This article explores the pivotal role of embedded systems and software in driving technological advancements across various industries. Embedded systems, characterized by their integration into hardware devices and their ability to perform specific tasks with precision, have become ubiquitous in our daily lives. Their applications span across diverse fields such as automotive, healthcare, consumer electronics, and industrial automation. This article delves into the fundamental concepts of embedded systems, highlights their importance, discusses the challenges faced in their development, and explores the latest trends and innovations in embedded software. We are committed to using our findings from this exploration to help others in the embedded systems and software community. We believe that by sharing our knowledge, we can help to accelerate innovation in this field.

The effect of perceived justice on users’ contribution in open source software communities

Users’ voluntary contribution of codes is crucial to the success of open source software (OSS) communities. However, users often lack the contribution motivation. The purpose of this research is to examine users’ contribution in OSS communities from a perceived justice perspective. We adopted a mixed method of structural equation modeling (SEM) and artificial neural network (ANN) to conduct data analysis. The results indicated that perceived justice, which includes distributive justice, procedural justice and interactional justice, has significant effects on trust and satisfaction, both of which further determine contribution intention. The results imply that OSS communities need to improve users’ perceived justice in order to facilitate their contribution behavior.