PIMan: A Comprehensive Approach for Establishing Plausible Influence among Software Repositories
PIMan: A Comprehensive Approach for Establishing Plausible Influence among Software Repositories
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DOI：
10.1109/asonam55673.2022.10068629
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发表时间：
2022-11
期刊：
影响因子：
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通讯作者：
Md Omar Faruk Rokon;Risul Islam;Md Rayhanul Masud;M. Faloutsos
中科院分区：
文献类型：
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作者：
Md Omar Faruk Rokon;Risul Islam;Md Rayhanul Masud;M. Faloutsos
我们如何量化像Github这样的在线档案中的储存量，确定存储库的影响是理解类似Github的软件档案的动态的重要组成部分。该概念的细微差别并考虑了其潜水的表现。通过关注社会级别的互动，我们介绍了合理的影响的概念，这些概念考虑了三种类型的信息：（a）回购级别的交互，（b）作者级别的交互，以及（c）我们评估的临时考虑并使用GitHub的2089个恶意软件存储库应用大约12年的方法。合理的影响图由合理的影响阈值（PIT）确定，可修改以满足研究的需求。合理的影响图中的组件（pit = 0.25），其中7％的组件包含至少15个存储库。至少有10个存储库，并至少产生了两个存储库的“家族”，结果表明，我们的影响力指标捕获了典型的存储库流行度指标（例如，恒星数）所无法捕获的相互作用的多种方面。总体而言，我们的工作是确定在线软件平台中存储库的影响和血统的基本构建基础。
How can we quantify the influence among repos-itories in online archives like GitHub? Determining repository influence is an essential building block for understanding the dynamics of GitHub-like software archives. The key challenge is to define the appropriate representation model of influence that captures the nuances of the concept and considers its diverse manifestations. We propose PIMan, a systematic approach to quantify the influence among the repositories in a software archive by focusing on the social level interactions. As our key novelty, we introduce the concept of Plausible Influence which considers three types of information: (a) repository level interactions, (b) author level interactions, and (c) temporal considerations. We evaluate and apply our method using 2089 malware repositories from GitHub spanning approximately 12 years. First, we show how our approach provides a powerful and flexible way to generate a plausible influence graph whose density is determined by the Plausible Influence Threshold (PIT), which is modifiable to meet the needs of a study. Second, we find that there is a significant collaboration and influence among the repositories in our dataset. We identify 28 connected components in the plausible influence graph (PIT = 0.25) with 7% of the components containing at least 15 repositories. Furthermore, we find 19 repositories that influenced at least 10 other repositories directly and spawned at least two “families” of repositories. In addition, the results show that our influence metrics capture the manifold aspects of the interactions that are not captured by the typical repository popularity metrics (e.g. number of stars). Overall, our work is a fundamental building block for identifying the influence and lineage of the repositories in online software platforms.