Optimization of software development life cycle process to minimize the delivered defect density

Many organizations utilize information technology to gain competitive advantage. As the need for software increased, the number of software companies and the competition among them also increased. The software organizations in countries like India can no longer survive based on cost advantage alone. The companies need to deliver defect-free software on time within the budgeted cost. This paper is a case study on minimizing the delivered defect density by optimally executing the various phases in software development life cycle process. The implementation of the study on four projects has shown that the delivered defect density can be minimized by executing the software development process with optimum settings suggested by the methodology. The project managers can also utilize the approach to achieve the goals set on other important output characteristics like productivity, schedule, etc.

[1]  László Monostori,et al.  Robust production planning and control for multi-stage systems with flexible final assembly lines , 2017, Int. J. Prod. Res..

[2]  Thong Ngee Goh,et al.  Adaptive ridge regression system for software cost estimating on multi-collinear datasets , 2010, J. Syst. Softw..

[3]  Everett E. Adam,et al.  Quality improvement practices and the effect on manufacturing firm performance: Evidence from Mexico and the USA , 2001 .

[4]  Yan Hao,et al.  Statistical prediction modeling for software development process performance , 2011, 2011 IEEE 3rd International Conference on Communication Software and Networks.

[5]  Erol Egrioglu,et al.  Type 1 fuzzy function approach based on ridge regression for forecasting , 2018, Granular Computing.

[6]  Fugee Tsung,et al.  Statistical process control for multistage manufacturing and service operations: A review and some extensions , 2008 .

[7]  Boby John.  Modeling the Defect Density of Embedded System Software Using Bayesian Belief Networks: A Case Study , 2012 .

[8]  Leon S. Lasdon,et al.  Design and Use of the Microsoft Excel Solver , 1998, Interfaces.

[9]  Uma Sudhakar Rao,et al.  Stochastic Optimization Modeling and Quantitative Project Management , 2008, IEEE Software.

[10]  M. Terziovski,et al.  The relationship between total quality management practices and operational performance , 1999 .

[11]  Thomas Fehlmann,et al.  A new approach for continuously monitoring project deadlines in software development , 2017, IWSM-Mensura.

[12]  Douglas C. Montgomery,et al.  Introduction to Statistical Quality Control , 1986 .

[13]  Mayuram S. Krishnan,et al.  Effects of Process Maturity on Quality, Cycle Time, and Effort in Software Product Development , 2000 .

[14]  Hamdy A. Taha,et al.  Operations Research an Introduction , 2007 .

[15]  Elizabeth A. Peck,et al.  Introduction to Linear Regression Analysis , 2001 .

[16]  N. Draper,et al.  Applied Regression Analysis , 1966 .

[17]  Gebregziabher Hailesilasie.  Determinants of public employees' performance: evidence from Ethiopian public organizations , 2009 .

[18]  Boby John,et al.  A methodology for quantitatively managing the bug fixing process using Mahalanobis Taguchi system , 2015 .

[19]  M. Sverke,et al.  Measuring quantitative and qualitative aspects of the job insecurity climate , 2015 .

[20]  Eugene L. Grant,et al.  Statistical Quality Control , 1946 .

[21]  Vijay Gurbaxani,et al.  A process oriented framework for assessing the business value of information technology , 1995, DATB.

[22]  Jiju Antony,et al.  Six Sigma in the software industry: results from a pilot study , 2004 .

[23]  Daniela M. Witten,et al.  An Introduction to Statistical Learning: with Applications in R , 2013 .

[24]  Boby John,et al.  A regression spline control chart for monitoring characteristics exhibiting nonlinear profile over time , 2019, The TQM Journal.

[25]  Kwok-Leung Tsui,et al.  Effects of estimation errors on cause-selecting charts , 2005 .

[26]  Pradipta Kishore Dash,et al.  Short-term wind speed and wind power prediction using hybrid empirical mode decomposition and kernel ridge regression , 2017, Appl. Soft Comput..

[27]  Boby John,et al.  Application of multistage process control methodology for software quality management , 2016 .

[28]  Xiaoming Zhang,et al.  Multi-modal kernel ridge regression for social image classification , 2018, Appl. Soft Comput..

[29]  Amir Ahmadi-Javid,et al.  Control charts for monitoring multi-stage service processes with optimal queue performance , 2020, Commun. Stat. Simul. Comput..

[30]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[31]  Shari Lawrence Pfleeger,et al.  Software Metrics : A Rigorous and Practical Approach , 1998 .

[32]  R. Mahesh,et al.  Monitoring Software Failure Process using Half Logistic Distribution , 2016 .

[33]  Douglas C. Montgomery,et al.  Introduction to Linear Regression Analysis, Solutions Manual (Wiley Series in Probability and Statistics) , 2007 .

[34]  Trevor Hastie,et al.  An Introduction to Statistical Learning , 2013, Springer Texts in Statistics.

[35]  Shigeru Yamada,et al.  A Method of Statistical Process Control for Successful Open Source Software Projects and Its Application to Determining the Development Period , 2016 .

[36]  Luc Muyldermans,et al.  Price determinants for remanufactured electronic products: a case study on eBay UK , 2015 .

[37]  Changsoon Park,et al.  A profile monitoring of the multi-stage process , 2019 .

[38]  Douglas R. Vogel,et al.  Managing software quality in a very large development project , 1995, Inf. Manag..