The Organization of Open Source Communities

Embedded industries have invested a lot in the introduction of software product lines in their software development. In addition, using open source software appears to be a profitable way to obtain good software. This is also applicable for organizations doing product line engineering. On the other hand, because of the diverse use of open source software, product line development is an attractive way of working in open source communities. In fact, the configuration mechanisms used in open source communities may be applicable within software product lines as well. In addition, product line organizations are usually involved in distributed development, which works very efficiently within open source communities. However, at present, there is limited interaction between the open source and product line development communities. Our aim is to explore what the two communities can learn from each other and to develop a better understanding of how the two communities can benefit from each other.

The last decades have seen an increasing interest in modeling collective animal behavior. Some studies try to reproduce as accurately as possible the collective dynamics and patterns observed in several animal groups with biologically plausible, individual behavioral rules. The objective is then essentially to demonstrate that the observed collective features may be the result of self-organizing processes involving quite simple individual behaviors. Other studies concentrate on the objective of establishing or enriching links between collective behavior researches and cognitive or physiological ones, which then requires that each individual rule be carefully validated. Here we discuss the methodological consequences of this additional requirement. Using the example of corpse clustering in ants, we first illustrate that it may be impossible to discriminate among alternative individual rules by considering only observational data collected at the group level. Six individual behavioral models are described: They are clearly distinct in terms of individual behaviors, they all reproduce satisfactorily the collective dynamics and distribution patterns observed in experiments, and we show theoretically that it is strictly impossible to discriminate two of these models even in the limit of an infinite amount of data whatever the accuracy level. A set of methodological steps are then listed and discussed as practical ways to partially overcome this problem. They involve complementary experimental protocols specifically designed to address the behavioral rules successively, conserving group-level data for the overall model validation. In this context, we highlight the importance of maintaining a sharp distinction between model enunciation, with explicit references to validated biological concepts, and formal translation of these concepts in terms of quantitative state variables and fittable functional dependences. Illustrative examples are provided of the benefits expected during the often long and difficult process of refining a behavioral model, designing adapted experimental protocols and inversing model parameters.

It is often valuable to know whether a given piece of source code has or hasn’t been used to train a given deep learning model. On one side, it helps avoid data contamination problems that may exaggerate the performance of evaluated models. Conversely, it facilitates copyright protection by identifying private or protected code leveraged for model training without permission. To this end, automated approaches have been proposed for the detection, known as data contamination detection. Such approaches often heavily rely on the confidence of the involved models, assuming that the models should be more confident in handling contaminated data than cleaned data. However, such approaches do not consider the nature of the given data item, i.e., how difficult it is to predict the given item. Consequently, difficult-to-predict contaminated data and easy-to-predict cleaned data are often misclassified. As an initial attempt to solve this problem, this paper presents a naturalness-based approach, called Natural-DaCoDe , for code-completion models to distinguish contaminated source code from cleaned ones. Natural-DaCoDe leverages code naturalness to quantitatively measure the difficulty of a given source code for code-completion models. It then trains a classifier to distinguish contaminated source code according to both code naturalness and the performance of the code-completion models on the given source code. We evaluate Natural-DaCoDe with two pre-trained large language models (e.g., ChatGPT and Claude ) and two code-completion models that we trained from scratch for detecting contamination data. Our evaluation results suggest that Natural-DaCoDe substantially outperformed the state-of-the-art approaches in detecting contaminated data, improving the average accuracy by 61.78%. We also evaluate Natural-DaCoDe with method name suggestion task, and it remains more accurate than the state-of-the-art approaches, improving the accuracy by 54.39%. Furthermore, Natural-DaCoDe was tested on a natural language text benchmark, significantly outperforming the state-of-the-art approaches by 22% . It may suggest that Natural-DaCoDe could be applied to various source code related tasks besides code complete.

In this paper, we conducted a comparative study of how social organization takes place in a wasp colony and OSS developer communities. Both these systems display similar global organization patterns, such as hierarchies and clear labor divisions. As our analysis shows, both systems also define interacting agent networks with similar common features that reflect limited information sharing among agents. As far as we know, this is the first research study analyzing the patterns and functional significance of these systems' weighted-interaction networks. By illuminating the extent to which self-organization is responsible for patterns such as hierarchical structure, we can gain insight into the origins of organization in OSS communities.

The popularity of open source has witnessed significant growth in the past two decades. The sustainability and success of open source projects rely heavily on the active community of contributors, who have the freedom to join or depart voluntarily. The churn of core developers can have detrimental effects on the continuous development and progress of these communities. Therefore, previous studies have aimed to identify the factors that influence developer churn and reveal its impact on open source communities. However, there is currently a lack of research on predicting whether a developer will leave or retain within a given timeframe. This article aims to bridge this gap by introducing a novel model called context-aware dynamic graph neural network (ConDyGNN), which is designed specifically for predicting developer churn in open source communities. By leveraging historical data, this model takes into consideration various factors such as developers’ activities, collaborative networks, and community context information. To capture both temporal dependencies and spatial correlations among different developers, temporal convolutional network (TCN) and graph convolutional network (GCN) modules are employed in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ConDyGNN</i>. Furthermore, a context-aware module based on a multihead attention mechanism is proposed to effectively integrate macrolevel community information and enhance the predictive performance. The superiority of the proposed model is empirically demonstrated through extensive comparative experiments and an ablation study.

This book synthesizes a decade of research by the author into fundamental issues in organization design. The result is a novel micro-structural perspective on organizations, which aims to both expand and narrow current thinking. The new perspective takes an expansive view on the kinds of phenomena that can be studied in terms of organization design- such as cross–functional teams, strategic partnerships, buyer-supplier relations, alliance networks, mega-projects, post-merger integration, business groups, open source communities, and crowdsourcing, besides traditional concerns with bureaucratic organizations. At the same time, this approach narrows focus by abstracting away from the variety and complexity of organizations to a few fundamental and universal problems of organizing (that relate to how they aggregate their members’ efforts), as well as a few reusable building blocks microstructures (which capture common patterns of interaction between members of an organization). The microstructural approach to organizations will be of interest to researchers and PhD students in management, organization science, and strategy

Access to the work of others is something that is too often taken for granted, yet problematic and difficult to be obtained unless someone pays for it. Green and gold open access are claimed to be a solution to this problem. While open access is gaining momentum in some fields, there is a limited and seasoned knowledge about self-archiving in computer science. In particular, there is an inadequate understanding of author-based self-archiving awareness, practice, and inhibitors. This article reports an exploratory study of the awareness of self-archiving, the practice of self-archiving, and the inhibitors of self-archiving among authors in an Italian computer science faculty. Forty-nine individuals among interns, PhD students, researchers, and professors were recruited in a questionnaire (response rate of 72.8%). The quantitative and qualitative responses suggested that there is still work needed in terms of advocating green open access to computer science authors who seldom self-archive and when they do, they often infringe the copyright transfer agreements of the publishers. In addition, tools from the open-source community are needed to facilitate author-based self-archiving, which should comprise of an automatic check of the copyright transfer agreements. The study identified nine factors inhibiting the act of self-archiving among computer scientists. As a first step, this study proposes several propositions regarding author-based self-archiving in computer science that can be further investigated. Recommendations to foster self-archiving in computer science, based on the results, are provided.

One of the significant advances in software design afforded by the internet has been the open source movement, an effort to collaboratively create software and make it widely and freely available to the online community. Although the open source movement started with Unix-like computer operating systems, it has expanded to include a wide variety of software programs, including tools to publish and analyze online surveys. This article introduces the open source movement and then profiles three leading open source survey programs: php Easy Survey Package (phpESP), PHP Surveyor, and the Moodle course management system.

Most research on open source software communities has focused on those that are community founded. More recently, firms have founded their own open source communities. How do sponsored open source communities differ from their autonomous counterparts? With comparative examination of 12 open source projects initiated by corporate sponsors, we identify three design parameters that together help form a participation architecture—the opportunity structure extended to potential external contributors. In exploring sponsors' community design decisions, we found that sponsored open source projects were more likely to offer transparency than they were accessibility and that this had implications for their communities' growth. We contribute theoretical constructs that offer a common basis of comparison for the future study of open source projects and illustrate how the tension between control and growth affects open source community design and creation.

Background: The popularity of the open source software development in the last decade, has brought about an increased interest from the industry on how to use open source components, participate in the open source community, build business models around this type of software development, and learn more about open source development methodologies. Aim: The aim of this study is to review research carried out on usage of open source components and development methodologies by the industry, as well as companies’ participation in the open source community. Method: Systematic review through searches in library databases and manual identification of articles from the open source conference. Results: 19 articles were identified. Conclusions: The articles could be divided into four categories: open source as part of component based software engineering, business models with open source in commercial organization, company participation in open source development communities, and usage of open source processes within a company.

Open source communities bring together a dispersed collection of people, sometimes a large number of them, around the development of open source software. In the absence of enforceable formal structures, like those found in corporate settings, how are the activities of all these participants coordinated? From the outside looking in, it may seem that chaos and disorder rule. It is true that most decisions are made on an individual basis by the participants themselves. Nevertheless, work is coordinated successfully. We first discuss mechanisms that reduce the need for coordination— most notably by striving for modularity and elegance. We then turn to a number of mechanisms that provide coordination even under the difficult conditions that are present in open source communities. We conclude by outlining a number of potential steps for future research.

In these recent years, several metaheuristic optimization algorithms have been developed to solve complex real-world problems. However, according to the no free lunch theorem, there is no metaheuristic algorithm which has the best suited for solving all optimization problems. Therefore, developing new metaheuristic algorithms is an open problem. Two major categories of metaheuristics are swarm intelligence and evolutionary algorithms. Both of them have their own advantages. In this paper, we present a naturally swarm and evolutionary algorithm in which its particles have memory, collaboration, information sharing, and competition inherited from one specific analogy. So, it has the advantages of both swarm and evolutionary algorithms without complications of hybridizing them. We formulate a new metaheuristic optimization algorithm called open source development model algorithm (ODMA) inspired by open source development model and communities, in such a way that each potential solution is considered as a software, and by evolution of the softwares, better solutions of the function that should be optimized are searched. The main operations of the algorithm include employing features and methods of leading softwares, evolution of leading softwares based on their history and forking from leading softwares. After a detailed formulation and explanation of its implementation, the algorithm is evaluated by $$17$$17 well-known benchmark functions, and the results are compared with PSO, ICA, and GA. The results show that ODMA outperforms GA, PSO, and ICA in terms of finding the global optimum and convergence.

The University of Waterloo Map Library is a cartographic and GIS academic resource centre to academics, community organizations and interested members of the local community. With a collection of over 100,000 maps, 49,000 air photos and being the campus’ hub for geospatial data, the library is frequented by many user groups and individuals – serving a variety of interests and purposes. Until most recently, many of these users were able to only access the collection in person, during library hours. Now, one of the library’s most popular paper collection used by the local community, the historical aerial photographs, have become available online for users to view and download from their home and business computers. The Map Library’s air photo collection includes stereoscopic images of the Region of Waterloo taken as early as 1930 to as recent as 1995. The air photos are widely used by students, business contractors, history buffs and home owners who like to study the air photos for land cover, property information, feature identification and changes in these over time. Due to several access limitations, the Map Library wanted to provide the community with easier access, available 24/7 and remotely from the internet. The Map Library scanned, digitized, and georeferenced photos from the 1930s and 1940s and uploaded them to a website that provides easy access for viewing and downloading the imagery. Using GIS technology, the air photos were digitized with geographical coordinate tags for use in GIS software programs including online mapping applications such as Google Earth (GE). By creating and offering downloadable georeferenced images compatible with popular mapping tools, the air photos have gained significant popularity and utilization by not only regular library users but by community groups, organizations and corporations who have never used library resources in the past. The integration of modern technology with traditional paper mapping has proven to be both methods of preservation and increased varying utilization.

“Although considerable research has been devoted to the growth and expansion of open source communities and the comparison between the efficiency of corporate structures and community structures in the field of software development, rather less attention has been paid to their governance structures (control, monitoring, supervision)” (Lattemann and Stieglitz 2005).

In many e-commerce applications, ranging from dynamic Web content presentation, to personalized ad targeting, to individual recommendations to the customers, it is important to build personalized profiles of individual users from their transactional histories. These profiles constitute models of individual user behavior and can be specified with sets of rules learned from user transactional histories using various data mining techniques. Since many discovered rules can be spurious, irrelevant, or trivial, one of the main problems is how to perform post-analysis of the discovered rules, i.e., how to validate user profiles by separating “good” rules from the “bad.” This validation process should be done with an explicit participation of the human expert. However, complications may arise because there can be very large numbers of rules discovered in the applications that deal with many users, and the expert cannot perform the validation on a rule-by-rule basis in a reasonable period of time. This paper presents a framework for building behavioral profiles of individual users. It also introduces a new approach to expert-driven validation of a very large number of rules pertaining to these users. In particular, it presents several types of validation operators, including rule grouping, filtering, browsing, and redundant rule elimination operators, that allow a human expert validate many individual rules at a time. By iteratively applying such operators, the human expert can validate a significant part of all the initially discovered rules in an acceptable time period. These validation operators were implemented as a part of a one-to-one profiling system. The paper also presents a case study of using this system for validating individual user rules discovered in a marketing application.