Software Metric Analysis of Open-Source Business Software

Over the past decade, open-source software use has grown. Today, many companies including Google, Microsoft, Meta, RedHat, MongoDB, and Apache are major participants of open-source contributions. With the increased use of open-source software or integration of open-source software into custom-developed software, the quality of this software component increases in importance. This study examined a sample of open-source applications from GitHub. Static software analytics were conducted, and each application was classified for its risk level. In the analyzed applications, it was found that 90% of the applications were classified as low risk or moderate low risk indicating a high level of quality for open-source applications.

The Measurement of the Software Ecosystem’s Productivity with GitHub

Software productivity has always been one of the most critical metrics for measuring software development.However,with the open-source community(e.g.,GitHub),new software development models are emerging.The traditional productivity metrics do not provide a comprehensive measure of the new software development models.Therefore,it is necessary to build a productivity measurement model of open source software ecosystem suitable for the open-source community’s production activities.Based on the natural ecosystem,this paper proposes concepts related to the productivity of open source software ecosystems,analyses influencing factors of open source software ecosystem productivity,and constructs a measurement model using these factors.Model validation experiments show that the model is compatible with a large portion of open source software ecosystems in GitHub.This study can provide references for participants of the open-source software ecosystem to choose proper types of ecosystems.The study also provides a basis for ecosystem health assessment for researchers interested in ecosystem quality.

Mapping the Flood Risk Exposure Using Open-Source Geospatial Tools and Techniques: A Case of Gampaha Divisional Secretariat Division, Sri Lanka

The use of open-source data and tools in disaster exposure mapping is presented in this paper. Disaster exposure is a collection of the element at risk to potential loss. Gampaha divisional secretariat (DS) is a study area laid on the lower part of the Attanagalu Oya river basin. As the geospatial tools, OpenStreetMap (OSM), Java OpenStreetMap (JOSM), QGIS, GPS Essentials, and Open Map Kit (OMK) are used. The elements of disaster exposure, including the number of people or types of assets, are surveyed and inventoried using the OSM platforms. Local, national, and international agencies produce and evaluate the data. The study developed spatial data for building footprints of 165,000 households, street lengths of 2300 km, hospital units of 16, and utility units of 2300. This could overcome the main challenges of exposure mapping in the area. The procedure developed in the exposure mapping can be used in a data-sparse environment. Exposure mapping is generally used to estimate the impact of hazards or disasters, which are essential in effective disaster management. How are there still remaining challenges in disaster exposure mapping such as less awareness about the mapping procedure, lack of government support, internet access, hardware, and inability to understand the value of exposure mapping?

CBE Clima Tool:A free and open-source web application for climate analysis tailored to sustainable building design

Climate-responsive building design holds immense potential for enhancing comfort,energy efficiency,and environmental sustainability.However,many social,cultural,and economic obstacles might prevent the wide adoption of designing climate-adapted buildings.One of these obstacles can be removed by enabling practitioners to easily access,visualize and analyze local climate data.The CBE Clima Tool(Clima)is a free and open-source web application that offers easy access to publicly available weather files and has been created for building energy simulation and design.It provides a series of interactive visualizations of the variables contained in the EnergyPlus Weather Files and several derived ones like the UTCI or the adaptive comfort indices.It is aimed at students,educators,and practitioners in the architecture and engineering fields.Since its inception,Clima’s user base has exhibited robust growth,attracting over 25,000 unique users annually from across 70 countries.Our tool is poised to revolutionize climate-adaptive building design,transcending geographical boundaries and fostering innovation in the architecture and engineering fields.

Evaluation indicators for open-source software:a review

In recent years,the widespread applications of open-source software(OSS)have brought great convenience for software developers.However,it is always facing unavoidable security risks,such as open-source code defects and security vulnerabilities.To find out the OSS risks in time,we carry out an empirical study to identify the indicators for evaluating the OSS.To achieve a comprehensive understanding of the OSS assessment,we collect 56 papers from prestigious academic venues(such as IEEE Xplore,ACM Digital Library,DBLP,and Google Scholar)in the past 21 years.During the process of the investigation,we first identify the main concerns for selecting OSS and distill five types of commonly used indicators to assess OSS.We then conduct a comparative analysis to discuss how these indicators are used in each surveyed study and their differences.Moreover,we further undertake a correlation analysis between these indicators and uncover 13 confirmed conclusions and four cases with controversy occurring in these studies.Finally,we discuss several possible applications of these conclusions,which are insightful for the research on OSS and software supply chain.

Graph Convolutional Network-Based Repository Recommendation System

GitHub repository recommendation is a research hotspot in the field of open-source software. The current problemswith the repository recommendation systemare the insufficient utilization of open-source community informationand the fact that the scoring metrics used to calculate the matching degree between developers and repositoriesare developed manually and rely too much on human experience, leading to poor recommendation results. Toaddress these problems, we design a questionnaire to investigate which repository information developers focus onand propose a graph convolutional network-based repository recommendation system (GCNRec). First, to solveinsufficient information utilization in open-source communities, we construct a Developer-Repository networkusing four types of behavioral data that best reflect developers’ programming preferences and extract features ofdevelopers and repositories from the repository content that developers focus on. Then, we design a repositoryrecommendation model based on a multi-layer graph convolutional network to avoid the manual formulation ofscoringmetrics. Thismodel takes the Developer-Repository network, developer features and repository features asinputs, and recommends the top-k repositories that developers are most likely to be interested in by learning theirpreferences. We have verified the proposed GCNRec on the dataset, and by comparing it with other open-sourcerepository recommendation methods, GCNRec achieves higher precision and hit rate.

基于Snort的网络链路数据篡改自主防御系统

由于开放性源码造成数据暴露,传统方法无法阻断被攻击数据包传递,导致数据无法自主防御。为此设计了基于Snort的网络链路数据篡改自主防御系统。使用数据包嗅探器捕捉Snort的报文,并通过信息解码模块将分层译码整合成文本信息,通过网络将其发送至系统数据库中,处理大信息量警报数据并进行存储记录;构建基于Snort的网络纵深防御模型,实现对篡改攻击实时探测和自动截获。根据网络中信息包传递特性,计算不同节点间传输距离,确定防御节点位置。推导链路层数据受到篡改攻击时数据传输路径,构建数据篡改自主防御函数,实现数据的自主防御。采用小波去噪数据处理技术,获得时序数据;利用小波逆变换重构,得到去噪后的数据,完成数据篡改自主防御系统设计。由实验结果可知,该系统网络链路数据安全传输密度高,最大密钥恢复成功率可达98%,具有较强的鲁棒性。

RepoLike: a multi-feature-based personalized recommendation approach for open-source repositories

With the deep integration of software collaborative development and social networking, social coding represents a new style of software production and creation paradigm. Because of their good flexibility and openness,a large number of external contributors have been attracted to the open-source communities. They are playing a significant role in open-source development. However, the open-source development online is a globalized and distributed cooperative work. If left unsupervised, the contribution process may result in inefficiency. It takes contributors a lot of time to find suitable projects or tasks from thousands of open-source projects in the communities to work on. In this paper, we propose a new approach called "RepoLike," to recommend repositories for developers based on linear combination and learning to rank. It uses the project popularity, technical dependencies among projects, and social connections among developers to measure the correlations between a developer and the given projects. Experimental results show that our approach can achieve over 25% of hit ratio when recommending 20 candidates, meaning that it can recommend closely correlated repositories to social developers.

入侵检测技术在网络安全中的应用

在对入侵检测技术的原理、方法进行总结归纳的基础上,介绍了两类常见的入侵检测系统,并以snort搭建windows下的入侵检测系统为例,分析了局域网环境下入侵检测系统的构建与使用方法,为使用入侵检测系统实施网络安全防护提供了可行路径。

The global climate monitor system:from climate data-handling to knowledge dissemination

This paper summarizes our work on building a data model and a geovisualization tool that provides access to global climate data:the Global Climate Monitor Web Viewer.Linked to this viewer,a complete set of climate-environmental indicators capable of displaying climate patterns on a global scale that is accessible to any potential user(scientists and laypeople)will be built and published using the same online application.The data currently available correspond to the CRU TS3.21 version of the Climate Research Unit(University of East Anglia)database–a product that provides data at a spatial resolution of half of a degree in latitude and longitude,spanning January 1901 to December 2012,on a monthly basis.Since January 2013,the datasets feeding the system have been the GHCN-CAMS temperature dataset and the Global Precipitation Climatology Centre(GPCC)First Guess precipitation dataset.Climatologists,hydrologists,planners and non-experts users such as media workers,policymakers,non-profit organizations,teachers or students,can access useful climatological information through the Global Climate Monitor system.