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The idea of applying data mining techniques on software engineering data has existed since mid 1990s, the idea has especially attracted a large amount of interest lately within software engineering

Mining Software Engineering Data

ICSE Companion, pp.172-173, (2007)

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摘要

Software engineering data (such as code bases, exe- cution traces, historical code changes, mailing lists, and bug databases) contains a wealth of information about a project¿s status, progress, and evolution. Using well- established data mining techniques, practitioners and re- searchers can explore the potential of this valuable data i...更多

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简介
  • Software engineering data contains a wealth of information about a software project’s status, progress, and evolution.
  • Working on open source projects, Chen et al [3] have shown that historical information can assist developers in understanding large systems.
  • As a reflection of the great interest in the area and the importance of the MSR work within the context of software engineering, the best papers for three of the major conferences within SE (ICSE, ASE, and ICSM) for 2006 are on applying data mining techniques on SE data.
  • The tutorial will provide participants with an overview of the field of mining software engineering data, as shown in Figure 1.
重点内容
  • Software engineering data contains a wealth of information about a software project’s status, progress, and evolution
  • The idea of applying data mining techniques on software engineering data has existed since mid 1990s [7], the idea has especially attracted a large amount of interest lately within software engineering
  • The tutorial will cover these topics through case studies from recent software engineering conferences
  • The tutorial will provide a good understanding of existing research on mining Software Engineering data
  • We shall discuss what types of data mining techniques are desired in software engineering, and how they should be customized to fit the requirements and characteristics of Software Engineering data
  • We shall summarize several kinds of data mining problems in software engineering that are under active investigation based on three major perspectives: data sources being mined, tasks being assisted, and mining techniques being used
结果
  • Software Engineering: (a) What types of SE data are available to be mined?
  • The tutorial will cover these topics through case studies from recent software engineering conferences.
  • Data mining techniques on their own SE data using advanced data mining analysis tools and algorithms; 4.
  • The tutorial will provide a good understanding of existing research on mining SE data.
  • The tutorial will categorize the existing research [9] in this field into three major perspectives: data sources being mined, tasks being assisted, and mining techniques being used.
  • Figure 1 shows such a categorization with the bottom part as a set of software engineering data being mined, the middle part as a set of mining techniques being used, and the top part as a set of software engineering tasks being assisted.
  • The authors shall elaborate the essential requirements in software engineering, and analyze the differences between mining software engineering data and mining other types of scientific and engineering data.
  • The authors shall discuss what types of data mining techniques are desired in software engineering, and how they should be customized to fit the requirements and characteristics of SE data.
  • The authors intend to understand the current research and development frontier of data mining practice in software engineering.
  • The authors shall summarize several kinds of data mining problems in software engineering that are under active investigation based on three major perspectives: data sources being mined, tasks being assisted, and mining techniques being used.
结论
  • The authors shall review and demonstrate briefly several research prototypes of data-mining systems for software engineering.
  • The participants can understand how to build a testbed for research and development.
  • The authors' overview will help the participants gain a better understanding of available tools.
  • The participants can use such tools in order to explore their data and integrate data mining techniques in their research and day to day work.
引用论文
  • The R Project for Statistical Computing. Available online at http://www.r-project.org/.
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  • Weka 3: Data Mining Software in Java. Available online at http://www.cs.waikato.ac.nz/ml/weka/.
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  • A. Chen, E. Chou, J. Wong, A. Y. Yao, Q. Zhang, S. Zhang, and A. Michail. CVSSearch: Searching through source code using CVS comments. In Proceedings of the 17th International Conference on Software Maintenance, pages 364–374, Florence, Italy, 2001.
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  • A. Mockus, D. M. Weiss, and P. Zhang. Understanding and predicting effort in software projects. In Proceedings of the 25th International Conference on Software Engineering, pages 274–284, Portland, Oregon, 2003.
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  • T. Xie. Bibliography on mining software engineering data. Available online at http://ase.csc.ncsu.edu/dmse/.
    Findings
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