基于分类预测的启发式错误定位方法
Heuristic Wrong Positioning Method Based on Classification Forecast
DOI: 10.12677/SEA.2016.51001, PDF, HTML, XML, 下载: 1,956  浏览: 5,954 
作者: 王 瑾, 罗 杰:北京航空航天大学计算机学院,北京
关键词: 机器学习方法分类预测启发式错误定位Machine Learn Method Classification Forecast Heuristic Wrong Positioning
摘要: 本文利用机器学习的方法通过训练学习,对通过形式化的错误定位算法进行错误定位的程序在二次定位或者多次定位前进行结果预测,引导用户给出高效的反馈信息(期望值或者正确值的确认信息),从而减少错误定位的次数,提高错误定位的效率,实现启发式的错位定位。
Abstract: This paper mainly forecasts the wrong positioning results for the program of the formal error localization algorithm before using it proceed two wrong positioning or multiple wrong positioning by training learn based on machine learn methods. It leads the user to give the high effective feedback information (expected value or the right value information). Thus, it decreases the wrong positioning number, raises the efficiency of the wrong positioning and realizes the heuristic wrong positioning.
文章引用:王瑾, 罗杰. 基于分类预测的启发式错误定位方法[J]. 软件工程与应用, 2016, 5(1): 1-9. http://dx.doi.org/10.12677/SEA.2016.51001

参考文献

[1] Silva, P., Moreno, A.M. and Peters, L. (2015) Software Project Management: Learning from Our Mistakes. IEEE Software, 32, 40-43.
http://dx.doi.org/10.1109/MS.2015.71
[2] Perscheid, M. and Hirschfeld, R. (2014) Follow the Path: Debugging Tools for Test-driven Fault Navigation. Software Maintenance, Reengineering and Reverse Engineering (CSMR-WCRE). IEEE Conference on Software Evolution Week. Victoria, City of Gardens, British Columbia, Canada, 2014, 446-449.
[3] Liang, X., Mao, L.Q. and Huang, M. (2014) Research on Improved the Tarantula spectrum Fault Localization Algorithm. 2nd International Conference on Information Technology and Electronic Commerce (ICITEC), Dalian, China, 2014, 631-654.
[4] Digiuseppe, N. and Jones, J.A. (2011) On the Influence of Multiple Faults on Coverage-Based Fault Localization. Proceedings of the 2011 International Symposium on Software Testing and Analysis, Toronto, Canada, 2011, 210-220.
http://dx.doi.org/10.1145/2001420.2001446
[5] Mathur, A. and Foody, G.M. (2008) Multiclass and Binary SVM Classification: Implications for Training and Classification Users. IEEE Geoscience & Remote Sensing Letters, 5, 241-245.
http://dx.doi.org/10.1109/LGRS.2008.915597
[6] Adeniyi, D.A., Wei, Z. and Yongquan, Y. (2014) Automated Web Usage Data Mining and Recommendation System Using K-Nearest Neighbor (KNN) Classification Method. Applied Computing & Informatics, 2, 36-38.
[7] Digiuseppe, N. and Jones, J.A. (2015) Fault Density, Fault Types, and Spectra-Based Fault Localization. Empirical Software Engineering, 20, 928-967.
http://dx.doi.org/10.1007/s10664-014-9304-1
[8] Xu, Q., Pei, Y. and Wang, L. (2013) An Evaluation Framework of Coverage-Based Fault Localization for Object- Oriented Programs. Trustworthy Computing and Services, Springer Berlin Heidelberg, Berlin, 591-597.
[9] Wong, W.E., Debroy, V. and Choi, B. (2010) A Family of Code Coverage-Based Heuristics for Effective Fault Localization. Journal of Systems & Software, 83, 188-208.
http://dx.doi.org/10.1016/j.jss.2009.09.037