Probabilistic Study of Software Defects Underlying Relation between Pre-Release and Post-Release Defects

Components that have defects after release, but not during testing, are very undesirable as they point to ‘holes’ in the testing process. In this work, the main objective is to provide a relation between pre-release & post-release defects. This work describes the initial effort of building analysis for defects in system testing carried out by an independent testing team. The motivation to have such correlation analysis in software defects is to serve as an early quality indicator of the software entering system testing and assist the testing team to manage and control test execution activities. Dataset is analysed with curve fitting methods & then sets are validated using correlation methods. After this, different performance parameters are calculated using probabilistic analysis tools.

[1]  M. Shepperd,et al.  A critique of cyclomatic complexity as a software metric , 1988, Softw. Eng. J..

[2]  B. Freimut,et al.  The importance of life cycle modeling to defect detection and prevention , 2002, 10th International Workshop on Software Technology and Engineering Practice.

[3]  Sanjay Kumar Dubey,et al.  Software Defect Prediction Models for Quality Improvement : A Literature Study , 2012 .

[4]  Shafay Shamail,et al.  Ineffectiveness of Use of Software Science Metrics as Predictors of Defects in Object Oriented Software , 2009, 2009 WRI World Congress on Software Engineering.

[5]  Tze-Jie Yu,et al.  Identifying Error-Prone Software—An Empirical Study , 1985, IEEE Transactions on Software Engineering.

[6]  Taghi M. Khoshgoftaar,et al.  Application of neural networks to software quality modeling of a very large telecommunications system , 1997, IEEE Trans. Neural Networks.

[7]  Chris F. Kemerer,et al.  A Metrics Suite for Object Oriented Design , 2015, IEEE Trans. Software Eng..

[8]  Pranayanath Reddy Anantula,et al.  Defect Prediction and Analysis Using ODC Approach in a Web Application , 2011 .

[9]  Ajit Ashok.  Defect Analysis and Prevention for Software Process Quality Improvement , 2010 .

[10]  Vijay Kumar,et al.  Applying Soft Computing Approaches to Predict Defect Density in Software Product Releases: An Empirical Study , 2013, Comput. Informatics.

[11]  Claes Wohlin,et al.  Evaluation of Usage-Based Reading—Conclusions after Three Experiments , 2004, Empirical Software Engineering.

[12]  Harald C. Gall,et al.  Cross-project defect prediction: a large scale experiment on data vs. domain vs. process , 2009, ESEC/SIGSOFT FSE.

[13]  Norman Fenton,et al.  A Probabilistic Model for Software Defect Prediction , 2001 .

[14]  Seija Komi-Sirviö,et al.  Development and evaluation of software process improvement methods , 2004 .

[15]  Nachiappan Nagappan,et al.  Predicting defects using network analysis on dependency graphs , 2008, 2008 ACM/IEEE 30th International Conference on Software Engineering.