Open Source Software Success Measurement Method Based on Mining Software Repository

NING De-jun; YE Pei-gen; QIN Liu; MEI Li

doi:10.3969/j.issn.0372-2112.2018.12.015

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Open Source Software Success Measurement Method Based on Mining Software Repository

更新时间：2025-07-16

- Open Source Software Success Measurement Method Based on Mining Software Repository
- Acta Electronica Sinica Vol. 46, Issue 12, Pages: 2930-2935(2018)
- 作者机构：
  
  1. 同济大学软件学院,上海,201210
  2. 上海大学通信与信息工程学院,上海,201210
  3. 中国科学院上海高等研究院智慧城市研究中心,上海,201210
  4. 同济大学软件学院,上海,201210
  5. 上海大学通信与信息工程学院,上海,201210
  6. 中国科学院上海高等研究院智慧城市研究中心,上海,201210
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2018.12.015
  CLC： TP311
- Published Online：25 December 2018，
  
  Published：2018
- 稿件说明：
移动端阅览
NING De-jun, YE Pei-gen, QIN Liu, et al. Open Source Software Success Measurement Method Based on Mining Software Repository[J]. Acta Electronica Sinica, 2018, 46(12): 2930-2935.
DOI：

NING De-jun, YE Pei-gen, QIN Liu, et al. Open Source Software Success Measurement Method Based on Mining Software Repository[J]. Acta Electronica Sinica, 2018, 46(12): 2930-2935. DOI： 10.3969/j.issn.0372-2112.2018.12.015.

摘要

开源软件已广泛应用于各软件领域，例如操作系统、容器等，但目前尚没有一种能够综合度量开源软件的方法.我们在用户兴趣度量和开发参与度量的基础上，提出了一种能够克服度量维度单一的局限性的度量方法.基于DM模型、软件生存力模型等相关文献研究和开源软件存储库数据挖掘，通过对项目过程数据进行聚类、主成分分析、回归分析和对开发过程的思考，本文提出一种基于存储库数据和统计学算法的开源软件成功度度量模型.并通过与用户兴趣度量结果和开发参与度量结果对比分析，证明本文的度量模型能够基于可自动无扰采集的存储库数据，更全面的衡量开源软件项目的成功.度量模型可应用于企业选择优质开源项目、学术研究、智能项目推荐等领域.

Abstract

The open source software (OSS) is widely used in various software fields

such as operating system

container

etc. But there is no method to measure open source software comprehensively. Based on the measurement of user interest and the development of participation metrics

we propose a method that can overcome the single limitation of the metric dimension. Based on DM model

software viability model and other relevant literature research

mining of software repository

we consider the OSS development process and propose an OSS success evaluation model through clustering

principal component analysis and regression analysis. By comparing the metric score of user interest method and development participation method

the evaluation model can be used to measure the success of OSS projects based on the data collected automatically without interference. The evaluation model can be applied to select high quality open source projects

academic research

intelligent project recommendation

etc.

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