Survey of Various Data Reduction Methods for Effective Bug Report Analysis

Authors

  • Kapil Sahu  M. Tech Scholar, Department of CSE, NIIST Bhopal, Madhya Pradesh, India
  • Dr. Umesh Kumar Lilhore  Head PG, Department of CSE, NIIST Bhopal, Madhya Pradesh, India
  • Prof. Nitin Agarwal  Assistant Professor, Department of CSE, NIIST Bhopal, Madhya Pradesh, India

Keywords:

Data Mining, Bug Data Reduction, Bug Report Analysis, Data Management in Bug Repositories.

Abstract

In software development process testing process ensures quality management of the product by ensuring bugs free product. During software development and testing process, lots of bugs are logged, fixed and reopened. Bugs management is always expensive and time consuming for software companies. Bug reports are essential software artifacts that describe software bugs, especially in open-source software. Lately, due to the availability of a large number of bug reports, a considerable amount of research has been carried out on bug-report analysis, such as automatically checking duplication of bug reports and localizing bugs based on bug reports. In particular, this paper first presents some background for bug reports and gives a small empirical study on the bug reports to motivate the necessity for work on bug-report analysis. Then this paper summaries the existing work on bug-report analysis and points out some possible problems in working with bug-report analysis

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2018-02-28

Issue

Volume 3, Issue 1, January-February-2018

Section

Research Articles

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Copyright (c) IJSRCSEIT

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Kapil Sahu, Dr. Umesh Kumar Lilhore, Prof. Nitin Agarwal, " Survey of Various Data Reduction Methods for Effective Bug Report Analysis, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 1, pp.661-665, January-February-2018.