Separately Modeling Fault Detection and Removing Testing Effort for Software Reliability Evaluation
Authors: 
Caihua Wu, Jianchao Ma, Li ChenAuthors Info & Claims
Pages 119 - 125
Abstract
Software reliability growth model (SRGM) describes the process of software failure, which is used to evaluate software reliability. Current SRGMs typically assume that software faults are removed once a software fault is detected and no new faults will be brought during this process. In fact, the assumption may be unrealistic. It is possible that new faults may be brought during debugging, which is called imperfect debugging. Thus, in this paper, we propose a new SRGM that incorporates with testing effort and imperfect debugging. We model software fault removal process and detection process separately. Furthermore, testing effort is divided into two efforts, namely detection effort and removal effort. The proposed model is verified by two real data sets and contrasted with other well-known SRGMs. The experimental results show that the proposed model outperforms the comparison models in terms of fitting capability and prediction capability.
References
[1]
Lyu, M.R. Handbook of Software Reliability Engineering. 1996, McGraw-Hill.
[2]
Schneidewind, N.F. Analysis of error processes in computer software{C}. Proceedings of International Conference on Reliable Software, IEEE Computer Society, 1975, 337--346.
[3]
Xie, M. Zhao, M. The Schneidewind software reliability model revisited{C}. Proceedings of the 3rd International Symposium on Software Reliability Engineering, 1992,184--192.
[4]
Y.P.Wu, Q.P.Hu, S.H.Ng. A study of software fault detection and correction process models{C}. IEEE International Conference on Management of Innovation and Technology, 2006, 812--816.
[5]
Jung-Hua Lo, Chin-Yu Huang. An integration of faut detection and correction process in software reliability analysis{J}. The Journal of Systems and Software, 2006, 79(9),1312--1323.
[6]
Huang CY, Kuo SY, Lyu M R. An assessment of testing-effort dependent software reliability growth models{J}.IEEE Trans. on Reliability, 2007, 56(2):198--211.
[7]
Xuemei zhang and Hoang Pham, An analysis of factors affecting software reliability{J}, The Journal Systems and Software, 2000, 50(1):43--56.
[8]
Chin-Yu Huang, Sy-Yen Kuo. Analysis of Incorporating Logistic Testing-EffortFunction Into Software Reliability Modeling. IEEE transactions on reliability, 2002, 51(3):261--270
[9]
Li Haifeng, Li Qiuying, Lu Minyan. A software reliability growth model considering an S-shaped testing effort function under imperfect debugging{J}. Journal of Harbin Engineering University, 2011, 32(11), 1460--1467.
[10]
Sy-Yen Kuo, Chin-Yu Huang, Michael R. Lyu. Framework for Modeling Software Reliability, Using Various Testing-Efforts and Fault-Detection Rates{J}. IEEE Transactions on Reliability, 2001,50(3):310--320.
[11]
Wu Caihua, Ma Jianchao, Wang Xiaowei, Liu Juntao. Software reliability growth model with fault detection effort and removing effort modeling separately. 2015 6th IEEE International Conference on Software Engineering and Service Science (ICSESS), 2015.
[12]
Zhangnan. Research on software reliability growth models based on queuing theory {D}. Dissertation for the Doctoral Degree of Harbin Institute of Technology, 2013.06.
[13]
P.K. Kapur, D.N. Goswami, Amit Bardhan, Ompal Singh. Flexible software reliability growth model with testing effort dependent learning process{J}. Applied Mathematical Modeling, 2008,32(7):1298--1307.
[14]
Tohma, M. Structural approach to the estimation of the number of residual software faults based on the hyper-geometric distribution{J}. IEEE transactions on software engineering. 1989,15(3),345--355.
[15]
Musa, John D, Iannino, Anthony, Okumoto, Kazuhira. Software Reliability,Measurement,Prediction and Application. McGraw-Hill. 1987.
[16]
www.7d-soft.com
[17]
R.Peng, Y.F.Li, W.J. Zhang, Q.P. Hu. Testing effort dependent software reliability model for imperfect debugging process considering detection and correction {J}. Reliability Engineering and System Safety, 2014, 126(complete)
Index Terms
- Separately Modeling Fault Detection and Removing Testing Effort for Software Reliability Evaluation
Recommendations
Generalised multi release framework for fault determination with fault reduction factor
The huge dependability on software systems has led to the need of reliable software in a short span of time. One of the ways to achieve this is to provide a series of versions of the software system. Thus, firms release the first software version with the ...
A unified approach of testing coverage‐based software reliability growth modelling with fault detection probability, imperfect debugging, and change point
AbstractThis paper presents a unified approach to model the reliability growth of software with imperfect debugging and coverage factor. Existing testing coverage‐based software reliability growth models considered that faults present at a particular ...
Testing Effort Dependent Software FDP and FCP Models with Consideration of Imperfect Debugging
SSIRI '10: Proceedings of the 2010 Fourth International Conference on Secure Software Integration and Reliability ImprovementSoftware reliability can be enhanced considerably during testing with faults being detected and corrected by testers. The allocation of testing resources, such as man power and CPU hours, during testing phase can largely influence fault detection speed ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
May 2019
202 pages
ISBN:9781450371728
DOI:10.1145/3339363
Copyright © 2019 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
In-Cooperation
- Research Center for Science and Technology for Learning, National Central University, Taiwan
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 24 May 2019
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
CSSE 2019
CSSE 2019: 2019 2nd International Conference on Computer Science and Software Engineering
May 24 - 26, 2019
Xi'an, China
Acceptance Rates
CSSE '19 Paper Acceptance Rate 33 of 74 submissions, 45%;
Overall Acceptance Rate 33 of 74 submissions, 45%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 77Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 14 Dec 2024
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in