Design Signals: The Role of Software Architecture and Paradigms in Design Thinking and Practice

Does the ontology of design software influence built form? Panagiotis Michalatos , lecturer in architectural technology at Harvard Graduate School of Design and a principal research engineer at Autodesk, Inc, here looks at how the move from drawing typologies to digital modelling since the mid-1990s has affected architectural aesthetics. He goes on to examine two more recent developments that may announce a further paradigm shift: granular tracking of design input, and data organisation practices in biomedics which, transferred to architecture, could allow for a more spatio-temporal approach.     

基于多维特征的开源项目个性化推荐方法

随着软件协同开发技术与社交网络的深度融合,社交化开发范式已成为当前软件创作与生产的重要方式。这一软件开发模型的灵活性与开放性,吸引了大规模的外围贡献者加入到开源社区中,形成了巨大的软件生产力。在开源社区中,这些分布广泛、规模巨大的外围贡献者主要以一种无组织的松散方式进行协同。他们需要花费大量的时间和精力,在海量的开源项目中寻找到自己真正感兴趣的项目并进行长期贡献。为了提高大规模群体协同的效率,本文提出一种基于多维特征的开源项目个性化推荐方法（即RepoLike）。该方法从开源项目自身流行度、关联项目技术相关度以及大众贡献者之间的社交关联度等三个维度度量开发者和开源项目之间的关联关系,并利用线性组合和Learning To Rank方法构建推荐模型,从而为开发者提供个性化的项目推荐服务。通过大规模的实证实验表明,RepoLike在推荐20个候选项目时的推荐命中率超过25%,能够有效地为开发人员提供有价值的推荐服务。     

GitHub中基于CNN-LSTM的开发者项目推荐模型

作为一个开源项目托管平台,GitHub以多开发者协同参与进行开源项目的开发,开发者作为GitHub的核心元素,保证了整个系统的活跃性,然而,很多新项目在短时间内无法找到合适的协同开发者而被拖延开发周期.针对这个问题,本文提出了一种基于Word2Vec的CNN-LSTM开发者项目推荐模型,该模型以Word2Vec训练开发者访问项目的序列,并将项目进行向量化表示,结合CNN-LSTM模型计算项目相似度并为开发者推荐合适的项目序列.通过提取GitHub中62,031个开发者在2015全年的项目访问数据进行项目预测和相似项目发现实验,实验结果表明,该模型推荐效果较佳,并且可以帮助开发者发现感兴趣的相似项目.     

基于GitHub的ICT相关领域发展趋势的分析

一定规模的用户对某类项目的关注度是评价该类项目所属行业发展趋势的重要参数之一,是行业发展趋势研究的数据基础。针对当前研究对象数据源获取的局限性问题,设计并实现了一个数据获取与分析系统。该系统可以根据关键词获取GitHub上指定行业的项目数据,以项目数量、关键项目的星标数量、复刻数量和提问数量为依据对项目数量和关注度的变化进行多维度分析,利用百度ECharts实现数据可视化,为研究行业发展的整体趋势提供参考。     

GitHub开源软件开发过程中影响因素的相关性分析

截至到目前为止,在GitHub开源软件托管平台上面的项目超过1200万,现有很多研究对GitHub开源软件的开发过程中的影响因素进行了分析,缺乏对影响因素间的相关性进行研究.本文通过分析GitHub开源软件的开发过程,提出了问题解决速度、问题增加速度等影响因素,并对这些影响因素间的相关性进行了分析.经过实验证明了有些影响因素之间存在一定的相关性.同时根据实验的结果还给出了针对GitHub开源软件开发过程的一些建议.     

Who to follow recommendation in large-scale online development communities

ContextOpen source development allows a large number of people to reuse and contribute source code to the community. Social networking features open opportunities for information discovery, social collaborations, and improved recommendations of potential collaborators.ObjectiveOnline community and development platforms rely on social network features to increase awareness and attention among community members for improved collaborations. The objective of this work is to introduce an approach for recommending relevant users to follow. Follower networks provide means for informal information propagation. The efficiency and effectiveness of such information flows is impacted by the network structure. Here, we aim to understand the resilience of networks against random or strategic node removal.MethodSocial network features of online software development communities present a new opportunity to enhance online collaboration. Our approach is based on the automatic analysis of user behavior and network structure. The proposed ‘who to follow’ recommendation algorithm can be parametrized for specific contexts. Link-analysis techniques such as PageRank/HITS provide the basis for a novel ‘who to follow’ recommendation model.ResultsWe tested the approach using a GitHub-based dataset. Currently, users follow popular community members to get updates regarding their activities instead of maintaining personal relations. Thus, social network features require further improvements to increase reciprocity. The application of our ‘who to follow’ recommendation model using the GitHub dataset shows excellent results with respect to context-sensitive following recommendations. The sensitivity of GitHub’s follower network to random node removal is comparable with other social networks but more sensitive to follower authority based node removal.ConclusionLink-based algorithm can be used for context-sensitive ‘who to follow’ recommendations. GitHub is highly sensitive to authority based node removal. Information flow established through follower relations will be strongly impacted if many authorities are removed from the network. This underpins the importance of ‘central’ users and the validity of focusing the ‘who to follow’ recommendations on those users.     

全球开源生态发展现状研究

在开源日益重要的今天,建立在全域开源大数据基础上的相对完整、可以反复进行推演的数据分析尤为重要.使用2019年全年GitHub的日志进行统计,总日志条数约5.46亿,通过分析GitHub全网的开发者行为日志,从数据的视角,来观察全球范围内的开源现状、进展趋势、演化特征,以及未来挑战等问题,除了展现目前开源世界全貌之外,还特别关注中国的开发者和企业组织在整个开源产业中的表现.     

An Art of Connectivity

Do building information modelling (BIM) systems sufficiently cater for the subtleties of the architect's role? Tristan Gobin, Sebastian Andraos and Thibault Schwartz of London-based robot control specialists HAL Robotics think not. They advocate the development of systems that offer more levels of abstraction, allowing architects, engineers and others who are not necessarily familiar with source code to assist in the elaboration of platform languages. Only then will they become true tools for creativity that reinforce continuity between the various actors involved in building design and construction.