An extension of fault-prone filtering using precise training and a dynamic threshold

Subscribe (Full Service)


	Search: The ACM Digital Library The Guide

	Feedback

An extension of fault-prone filtering using precise training and a dynamic threshold

Full text

Pdf (1.42 MB)

Source

International Conference on Software Engineering archive
Proceedings of the 2008 international working conference on Mining software repositories table of contents

Leipzig, Germany

SESSION: Mining 2 table of contents

Pages 89-98

Year of Publication: 2008

ISBN:978-1-60558-024-1

Authors

Hideaki Hata	Osaka University, Suita, Japan
Osamu Mizuno	Osaka University, Suita, Japan
Tohru Kikuno	Osaka University, Suita, Japan

Sponsors

SIGSOFT: ACM Special Interest Group on Software Engineering
ACM: Association for Computing Machinery

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 6, Downloads (12 Months): 40, Citation Count: 0

Additional Information:

abstract references index terms collaborative colleagues

Tools and Actions:	Review this Article Save this Article to a Binder Display Formats: BibTex EndNote ACM Ref

DOI Bookmark:	Use this link to bookmark this Article: http://doi.acm.org/10.1145/1370750.1370772 What is a DOI?

ABSTRACT

Fault-prone module detection in source code is important for assurance of software quality. Most previous fault-prone detection approaches have been based on software metrics. Such approaches, however, have difficulties in collecting the metrics and in constructing mathematical models based on the metrics. To mitigate such difficulties, we have proposed a novel approach for detecting fault-prone modules using a spam-filtering technique, named Fault-Prone Filtering. In our approach, fault-prone modules are detected in such a way that the source code modules are considered as text files and are applied to the spam filter directly. In practice, we use the training only errors procedure and apply this procedure to fault-prone. Since no pre-training is required, this procedure can be applied to an actual development field immediately. This paper describes an extension of the training only errors procedures. We introduce a precise unit of training, "modified lines of code," instead of methods. In addition, we introduce the dynamic threshold for classification. The result of the experiment shows that our extension leads to twice the precision with about the same recall, and improves 15% on the best F₁ measurement.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	E. Arisholm, L. C. Briand, and M. J. Fuglerud. Data mining techniques for building fault-proneness models in telecom java software. In Proc. of 18th International Symposium on Software Reliability Engineering (ISSRE2007), pages 215--224, 2007.
	2	L. Aversano, L. Cerulo, and C. D. Grosso. Learning from bug-introducing changes to prevent fault prone code. In IWPSE '07: Ninth international workshop on Principles of software evolution, pages 19--26, New York, NY, USA, 2007. ACM.
	3	P. Bellini, I. Bruno, P. Nesi, and D. Rogai. Comparing fault-proneness estimation models. In Proc. of 10th IEEE International Conference on Engineering of Complex Computer Systems (ICECCS'05), pages 205--214, 2005.
	4	L. C. Briand, W. L. Melo, and J. Wust. Assessing the applicability of fault-proneness models across object-oriented software projects. IEEE Trans. on Software Engineering, 28(7):706--720, 2002.
	5	CRM114 -- the Controllable Regex Mutilator. http://crm114.sourceforge.net/.
	6	G. Denaro and M. Pezze. An empirical evaluation of fault-proneness models. In Proc. of 24th International Conference on Software Engineering (ICSE '02), pages 241--251, 2002.
	7	Eclipse Project. http://www.eclipse.org/.
	8	L. Guo, B. Cukic, and H. Singh. Predicting fault prone modules by the Dempster-Shafer belief networks. In Proc. of 18th IEEE International Conference on Automated Software Engineering (ASE'03), pages 249--252, 2003.
	9	Y. Kamei, A. Monden, S. Matsumoto, T. Kakimoto, and K. ichi Matsumoto. The effects of over and under sampling on fault-prone module detection. In Proceedings of the 1st International Symposium on Empirical Software Engineering and Measurement (ESEM2007), pages 196--204, September 2007.
	10	T. M. Khoshgoftaar and N. Seliya. Comparative assessment of software quality classification techniques: An empirical study. Empirical Software Engineering, 9:229--257, 2004.
	11	T. Menzies, J. Greenwald, and A. Frank. Data mining static code attributes to learn defect predictors. IEEE Trans. on Software Engineering, 33(1):2--13, January 2007.
	12	O. Mizuno, S. Ikami, S. Nakaichi, and T. Kikuno. Spam filter based approach for finding fault-prone software modules. In Proc. of 2007 International Workshop on Mining Software Repositories (MSR2007), 2007.
	13	O. Mizuno and T. Kikuno. Training on errors experiment to detect fault-prone software modules by spam filter. In The 6th joint meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering (ESEC/FSE2007), pages 405--414, 2007.
	14	N. Seliya, T. M. Khoshgoftaar, and S. Zhong. Analyzing software quality with limited fault-proneness defect data. In Proc. of Ninth IEEE International Symposium on High-Assurance Systems Engineering (HASE'05), pages 89--98, 2005.
	15	J. Sliwerski, T. Zimmermann, and A. Zeller. When do changes induce fixes? (on Fridays.). In Proc. of Mining Software Repository 2005, pages 24--28, 2005.
	16	C. Wohlin, P. Runeson, M. Host, M. C. Ohlsson, B. Regnell, and A. Wesslen. Experimentation in software engineering: An introduction. Kluwer Academic Publishers, 2000.