Weighted Hybrid Defect Content and Effectiveness Model

Abstract:

Requirements analysis phase is an important step which is at the earliest stage of a software lifecycle. If defects are found out at early stage, the cost of a project can be considerably minimized. A weighted method with higher accuracy is developed, based on a Hybrid Defect Content and Effectiveness Model (HDCE) used at requirements analysis phase. In this model, the defects of requirements are classified and every level has different weight value. Moreover, comparison of actual case data clearly indicates that prediction made from this model is more accurate than the prediction made from just using historical data model.

• Periodical: Advanced Materials Research (Volumes 846-847)
• Pages: 1762-1767
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.846-847.1762
• Online since: November 2013
• Authors: Hua Yin Zhang*, Jian Long Ding
• Keywords: Defect Estimation, Hybrid Prediction Model, Quality Assurance Effectiveness, Quality Management, Software Quality Assurance, Software Requirement Analysis

References

[1] State Standard of the People's Republic of China - Specification for computer software quality assurance plan: GB／T  12504 – 90.

[2] Michael Klas, Haruka Naruka, Frank Elberzhager and Jurgen Munch, Support planning and controlling of early quality assurance by combining expert judgment and defect data, Empir Software Eng, 2010, vol. 15, pp.423-454.

DOI: 10.1007/s10664-009-9112-1

[3] Fishman, George S. Monte Carlo, Concepts, algorithms, and applications, Springer Verlag, New York, 1999(Corrected third printing).

[4] Jef Jacobsa, Jan van Molla, Rob Kustersc, Jobs Trienekensc and Aarnout Brombacherc, Identification of factors that influence defect injection and detection in development of software intensive products, Information and Software Technology 2007, vol. 49, pp.774-789.

DOI: 10.1016/j.infsof.2006.09.002

[5] Lenandlar Singh, Where are we? A review of effort and cost estimation techniques used for web applications, Computer Sciences and Telecommunications, 2009, vol. 20.

[6] Brijendra Singh and Suresh Prasad Kannojia, A model for software product quality prediction, Journal of Software Engineering and Applications, 2012, vol. 5, pp.395-401.

DOI: 10.4236/jsea.2012.56046

[7] M Sangeeha, C Arumugam, P G Sapna and Senthil Kumar K M, A cooperative approach to ensure software product quality, International Journal of Computer Science Issues, 2011, vol. 8, pp.598-603.