CONCERNING EFFECTIVE ERROR IDENTIFIED WITH SOFTWARE RECORDS DECREASE TECHNIQUES

Ch. Srinivas, G. Lingam

Abstract


To reduce time cost in manual work, text classification techniques they can fit on conduct automatic bug triage. In this particular paper, we address the problem of understanding reduction for bug triage, i.e., the simplest way to reduce the scale and improve the grade of bug data. Software companies spend over 45 percent of cost when controlling software bugs. An inevitable step of fixing bugs is bug triage, which aims to correctly assign a developer to a new bug. To discover a purchase of applying instance selection and possess selection, we extract attributes from historic bug data sets developing a predictive model for every new bug data set. We combine instance selection with feature selection to concurrently reduce data scale inside the bug dimension coupled with word dimension. The conclusion result shows our data reduction can effectively reduce the data scale and lift a realistic look at bug triage. We empirically investigate performance of understanding reduction on totally 600,000 bug reports of two large free projects, namely Eclipse and Mozilla. Our work supplies a kinds of leveraging techniques on human sources to produce reduced and-quality bug data in software development and maintenance.


Keywords


Mining Software Repositories; Data Management In Bug Repositories; Bug Data Reduction; Bug Triage;

References


P. S. Bishnu and V. Bhattacherjee, “Software fault prediction using quad tree-based k-means clustering algorithm,” IEEE Trans. Knowl. Data Eng., vol. 24, no. 6, pp. 1146–1150, Jun. 2012.

J. Xuan, H. Jiang, Z. Ren, J. Yan, and Z. Luo, “Automatic bug triage using semi-supervised text classification,” in Proc. 22nd Int. Conf. Soft. Eng. Knowl. Eng., Jul. 2010, pp. 209–214.

C. Sun, D. Lo, S. C. Khoo, and J. Jiang, “Towards more accurate retrieval of duplicate bug reports,” in Proc. 26th IEEE/ACM Int. Conf. Automated Soft. Eng., 2011, pp. 253–262.

S. Kim, H. Zhang, R. Wu, and L. Gong, “Dealing with noise in defect prediction,” in Proc. 32nd ACM/IEEE Int. Conf. Soft. Eng., May 2010, pp. 481–490.

A. E. Hassan, “The road ahead for mining software repositories,” in Proc. Front. Soft. Maintenance, Sep. 2008, pp. 48–57.


Full Text: PDF

Refbacks

  • There are currently no refbacks.


Bookmark and Share


Copyright © 2012 - 2023, All rights reserved.| ijitr.com

Creative Commons License
International Journal of Innovative Technology and Research is licensed under a Creative Commons Attribution 3.0 Unported License.Based on a work at IJITR , Permissions beyond the scope of this license may be available at http://creativecommons.org/licenses/by/3.0/deed.en_GB.