PREDICTION OF DEFECT DENSITY FOR OPEN SOURCE SOFTWARE USING REPOSITORY METRICS
Keywords:
Defect Density, Repository Metrics, Simple and Multiple Linear RegressionsAbstract
Open source software refers to software with unrestricted access for use or modification. Many software development organizations are using this open source methodology in their development process. Many software developers can work in parallel with the open source project using the web as a shared resource. The defect density of such projects is often required to be predicted for the purpose to ensure quality standards. Static metrics for defect density prediction require extraction of abstract information from the code. Repository metrics, on the other hand, are easy to extract from the repository data sets. In this paper, an analysis has been performed over repository metrics of open source software. Further, defect density is being predicted using these metrics individually and jointly. Sixty two open source software are considered for analysis using Simple and Multiple Linear Regression methods as statistical procedures. The results reveal a statistically significant level of acceptance for prediction of defect density using few repository metrics individually and jointly.
Downloads
References
Trung T. D. & James M. B.(2005). The FreeBSD Project: A Replication Case Study of Open
Source Development. IEEE transaction on software engineering, 31, 6, 481-494.
Sourceforge, http://sourceforge.net/. [Accessed: on April 19, 2015].
Chidamber S. & Kemerer C. (1994). A metrics suite for object oriented design. IEEE
Transaction of software engineering, 20, 6, 476-493.
Weyuker E.J., Ostrand T.J., & Bell R. M. (2008). Do too many cooks spoil the broth? Using
the number of developers to enhance defect predication models. Empirical software
engineering, 13, 5, 539-559.
Gyimothy T., Ferenc R., & Siket I. (2005). Empirical validation of object oriented metrics on
open source software for fault prediction. IEEE Transaction on software engineering, 31, 10,
-910.
Knab P., Pinzger M., & Bernstein A. (2006). Predicting Defect Densities in source code files
with Decision Tree Learners. Proceedings of the 2006 international workshop on Mining
software Repositories, 119-125.
Subramanyam R. and Krishnan M.S. (2003). Empirical Analysis of CK Metrics for Object-
Oriented Design Complexity: Implications for Software Defects. IEEE Transactions on
Software Engineering, 29, 4, 297-310.
Mockus A., Fielding R.T., & Herbsleb J.D. (2002). Two case studies of open source software
development: Apache and Monzilla. ACM Transaction on software engineering methodology,
, 3, 309-346.
Sherriff M., Williams L., & Vouk M. (2004). Using In-Process Metrics to predict Defect
Density in Haskell Programs. The 15th International Symposium on Software Reliability
Engineering.
Rahmani C. and Khazanchi D. (2010). A Study on Defect Density of Open source software.
The 9th International conference on Computer and Information Science, 679-683.
Caglyan B., Bener A., & Koch S. (2009). Merits of Using Repository Metrics in Defect
Prediction for Open Source Projects. International Conference on Software Engineering, 31-
Park, R. (1992). Software Size Measurement: A Framework for Counting Source Statements.
CMU/SEI-92-TR-20, Software Engineering Institute, Pittsburgh, PA.
Institute of Electrical and Electronics Engineers 1044-2009 (2010), “IEEE Standards
Classification for Software Anamolies,” Available at
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5399061.
Alali A., Kagdi H., & Maletic J.I. (2008). What’s Typical Commit? A Characterization of
Open Source Software Repository. Proceedings of 16th IEEE International Conference on
Program Comprehension, 182-191.
Basili V. R. & Perricone B. T. (1982). Software Errors and Complexity: An Empirical
Investigation. Communication of ACM, 27, 1, 42-54.
Statistical Package for Social Sciences (n.d.). Available at http://www.spss.com/statistics.
IBM SPSS Statistics Version 20 64-bit.
Poole M.A. & O’Ferrell P.N. (1971). The assumptions of the linear regression model,” Inst.
Brit. Geogr., Trans., 52, 145–158.
Elliott A.C. & Woodward W.A. (2007). Statistical analysis quick reference guidebook with
SPSS examples. 1st ed. London, Sage Publications.
Verma D. & Kumar S. (2014). An Improved Approach for Reduction of Defect Density Using
Optimal Module Sizes. Hindwai Publishing Corporation, Advances in Software Engineering,
Volume 2014, Article ID 803530.
Verma D. & Kumar S. (2015). Exponential Relationship Based Approach For Predictions Of
Defect Density Using Optimal Module Sizes. Proceedings Of National Academy Of Sciences
Section A: Physical Sciences, DOI 10.1007/s40010-015-0261-x.
Raza A., Capretz L.F., & Ahmed F. (2012). Users’ perception of open source usability: an
empirical study. Journal of Engineering and Computers, 28, 2, 109-121.
Keng S. & Yuhong T. (2013). Open Source Software Development Process Model: A
Grounded Theory Approach. Journal of Global Information Management, 21, 4, 103-120.
Jiang Y., Li M., & Zhou Z. (2011). Software Defect Detection with Rocus. Journal of
Computer Science and Technology, 26, 2, 328-342.
Verma D., Mandhan N., & Kumar S. (2015). Analysis of Approach for Predicting Software
Defect Density using Static Metrics. International Conference on Computing, Communication
and Automation, 880-886.
Deepak N. & Kumar S. (2015). Flexible Self-Managing Pipe-line Framework Reducing
Development Risk to Improve Software Quality. International Journal of Information
Technology and Computer Science, 7, 7, 35-47.
