Bug Prediction for an ATM Monitoring Software - Use of Logistic Regression Analysis for Bug Prediction

Ozkan Sari, Oya Kalipsiz

2015

Abstract

Software testing which is carried out for the elimination of the software defects is one of the significant activities to achieve software quality. However, testing each fragment of the software is impossible and defects still occur even after several detailed test activities. Therefore, there is a need for effective methods to detect bugs in software. It is possible to detect faulty portions of the code earlier by examining the characteristics of the code. Serving this purpose, bug prediction activities help to detect the presence of defects as early as possible in an automated fashion. As a part of the ongoing thesis study, an effective model is aimed to be developed in order to predict software entities having bugs. A public bug database and ATM monitoring software source code are used for the creation of the model and to find the performance of the study.

References

  1. Weyuker, E. J.; Bell, R. M.; Ostrand, T. J., "We're Finding Most of the Bugs, but What are We Missing?," Software Testing, Verification and Validation (ICST), 2010 Third International Conference on , vol., no., pp.313,322, 6-10 April 2010.
  2. Khannur, A., Structured Software Testing: The Discipline of Discovering, Partridge Publishing, 2014, pages 22- 23.
  3. Boehm, B., Basili V. R., “Software Defect Reduction Top 10 List”, IEEE Computer, Vol. 34, No. 1, Jan 2001, pages 135-137.
  4. Shyam R. Chidamber and Chris F. Kemerer. A metrics suite for object oriented design. IEEE Trans. Software Eng., 20(6):476-493, 1994.
  5. Raimund Moser, Witold Pedrycz, and Giancarlo Succi. A comparative analysis of the efficiency of change metrics and static code attributes for defect prediction. In Proceedings of ICSE 2008, pages 181-190, 2008.
  6. Thomas Zimmermann, Rahul Premraj, and Andreas Zeller. Predicting defects for eclipse. In Proceedings of PROMISE 2007, page 76. IEEE CS, 2007.
  7. H. Okamura, Y. Etani and T. Dohi, "A multi-factor software reliability model based on logistic regression," Proceedings of 21st IEEE International Symposium on Software Reliability Engineering (ISSRE'10), pp. 31-40, IEEE CPS (2010).
  8. Kuwa, D.; Dohi, T., "Generalized Logit Regression-Based Software Reliability Modeling with Metrics Data," Computer Software and Applications Conference (COMPSAC), 2013 IEEE 37th Annual , vol., no., pp.246,255, 22-26 July 2013.
  9. Johnson, R. A., Wichern D. W., Applied Multivariate Statistical Analysis, Pearson, 6th Edition, April 2, 2007.
  10. Marco D'Ambros, Michele Lanza, and Romain Robbes. An extensive comparison of bug prediction approaches. In MSR 7810: Proceedings of the 7th International Working Conference on Mining Software Repositories, s. 31-41, 2010.
  11. Michael Fischer, Martin Pinzger, and Harald Gall. Populating a release history database from version control and bug tracking systems. In Proceedings of ICSM 2003, pages 23-32. IEEE CS, 2003.
  12. Christian Bird, Adrian Bachmann, Eirik Aune, John Duffy, Abraham Bernstein, Vladimir Filkov, and Premkumar Devanbu. Fair and balanced?: bias in bugfix datasets. In Proceedings of ESEC/FSE 2009, pages 121-130, New York, NY, USA, 2009. ACM.
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Paper Citation


in Harvard Style

Sari O. and Kalipsiz O. (2015). Bug Prediction for an ATM Monitoring Software - Use of Logistic Regression Analysis for Bug Prediction . In Proceedings of the 17th International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-758-097-0, pages 382-387. DOI: 10.5220/0005382803820387


in Bibtex Style

@conference{iceis15,
author={Ozkan Sari and Oya Kalipsiz},
title={Bug Prediction for an ATM Monitoring Software - Use of Logistic Regression Analysis for Bug Prediction},
booktitle={Proceedings of the 17th International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2015},
pages={382-387},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005382803820387},
isbn={978-989-758-097-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 17th International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - Bug Prediction for an ATM Monitoring Software - Use of Logistic Regression Analysis for Bug Prediction
SN - 978-989-758-097-0
AU - Sari O.
AU - Kalipsiz O.
PY - 2015
SP - 382
EP - 387
DO - 10.5220/0005382803820387