计算机科学技术学报 ›› 2019,Vol. 34 ›› Issue (5): 1063-1078.doi: 10.1007/s11390-019-1960-6

所属专题： Software Systems

• • 上一篇    下一篇

软件度量元阈值提取框架

Mohammed Alqmase, Mohammad Alshayeb*, Lahouari Ghouti   

  1. Information and Computer Science Department, King Fahd University of Petroleum and Minerals Dhahran 31261, Saudi Arabia
  • 收稿日期:2018-10-13 修回日期:2019-03-23 出版日期:2019-08-31 发布日期:2019-08-31
  • 通讯作者: Mohammad Alshayeb E-mail:alshayeb@kfupm.edu.sa
  • 作者简介:Mohammed Alqmase received his M.S. degree in computer science from King Fahd University of Petroleum and Minerals, Dhahran, in 2019, and his B.S. degree in information technology (IT) from King Abdul-Aziz University, Jeddah, in 2013. He worked as a content management system analyst for Hippo CMS in 2017. He also worked as an instructor in Sana'a Community College, Sana, Yemen, from 2013 to 2015. His research interests include sentiment analysis, natural language processing, machine learning, algorithms and software engineering.

Threshold Extraction Framework for Software Metrics

Mohammed Alqmase, Mohammad Alshayeb*, Lahouari Ghouti   

  1. Information and Computer Science Department, King Fahd University of Petroleum and Minerals Dhahran 31261, Saudi Arabia
  • Received:2018-10-13 Revised:2019-03-23 Online:2019-08-31 Published:2019-08-31
  • Contact: Mohammad Alshayeb E-mail:alshayeb@kfupm.edu.sa
  • About author:Mohammed Alqmase received his M.S. degree in computer science from King Fahd University of Petroleum and Minerals, Dhahran, in 2019, and his B.S. degree in information technology (IT) from King Abdul-Aziz University, Jeddah, in 2013. He worked as a content management system analyst for Hippo CMS in 2017. He also worked as an instructor in Sana'a Community College, Sana, Yemen, from 2013 to 2015. His research interests include sentiment analysis, natural language processing, machine learning, algorithms and software engineering.

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软件度量元用来度量软件的不同属性。事实上，为了使用这些度量元来度量软件属性，度量阈值不必可少。很多研究人员曾试图基于个人经验来确定这些阈值。但是，由于个人经历的可变性和人的观点的主观性，基于经验产生的阈值难以推广。本文旨在提出一个自动聚类框架，它基于期望最大化（EM）算法，在此，利用一个简化的3个度量元集（LOC，LCOM，CBO）来聚类。基于这些类，我们系统地确定不同软件度量元阈值等级，使得每个阈值反应软件质量的特定等级。本文所提的框架分为两个步骤：聚类和阈值提取。前者中，软件质量历史数据集被使用EM算法，分解成特定的聚类集合；后者则是通过统计数据，例如每个度量元的平均值和标准方差来估算针对所得到的类中，每个软件度量元的阈值。本文研究结果凸显了基于EM的聚类方法的能力，它能使用最小度量集，根据不同质量等级，对软件质量数据库进行组合。

关键词: 度量元阈值, 期望最大化, 实证研究

Abstract: Software metrics are used to measure different attributes of software. To practically measure software attributes using these metrics, metric thresholds are needed. Many researchers attempted to identify these thresholds based on personal experiences. However, the resulted experience-based thresholds cannot be generalized due to the variability in personal experiences and the subjectivity of opinions. The goal of this paper is to propose an automated clustering framework based on the expectation maximization (EM) algorithm where clusters are generated using a simplified 3-metric set (LOC, LCOM, and CBO). Given these clusters, different threshold levels for software metrics are systematically determined such that each threshold reflects a specific level of software quality. The proposed framework comprises two major steps:the clustering step where the software quality historical dataset is decomposed into a fixed set of clusters using the EM algorithm, and the threshold extraction step where thresholds, specific to each software metric in the resulting clusters, are estimated using statistical measures such as the mean (μ) and the standard deviation (σ) of each software metric in each cluster. The paper's findings highlight the capability of EM-based clustering, using a minimum metric set, to group software quality datasets according to different quality levels.

Key words: metric threshold, expectation maximization, empirical study

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