How to cite this paper
John, B & Kadadevarmath, R. (2015). A methodology for quantitatively managing the bug fixing process using Mahalanobis Taguchi system.Management Science Letters , 5(12), 1081-1090.
Refrences
Agrawal, M., & Chari, K. (2007). Software effort, quality, and cycle time: A study of CMM level 5 projects. IEEE Transactions on Software Engineering, 33(3), 145-156.
Akhyar, G., Che Haron, C. H., & Ghani, J. A. (2008). Application of Taguchi method in the optimization of turning parameters for surface roughness. International Journal of Science Engineering and Technology, l (3), 60 – 66.
Asada, M. (2001). Wafer yield prediction by the Mahalanobis-Taguchi system. IEEE International Workshop on Statistical Methodology, 6, 25-28.
Bachy, B., & Franke, J. (2015). Modeling and optimization of laser direct structuring process using artificial neural network and response surface methodology. International Journal of Industrial Engineering Computations, 6, 553 - 564.
Berkani, S., Yallese, M.A., Boulanouar, L., & Mabrouki, T. (2015). Statistical analysis of AISI304 austenitic stainless steel machining using Ti(C, N)/Al2O3/TiN CVD coated carbide tool. International Journal of Industrial Engineering Computations, 6, 539 - 552.
Box, G. E., Hunter, W. G., & Hunter, S. J. (1978.) Statistics for Experiments: An Introduction to Design, Data Analysis, and Model Building. Wiley, New York.
Chowdhury, K. K., & John, Boby. (2003). Optimization of the Induction Hardening Operation using Robust Design. Journal of Quality Engineering Forum, 11(4), 70-76.
Cudney, E. A., Hong, J., Jugulum, R., Paryani, K., Ragsdell, K.M., & Taguchi, G. (2007). An Evaluation of Mahalanobis-Taguchi Systems and Neural Network for Multivariate Pattern Recognition. Journal of Industrial and System Engineering, 1(2), 139 – 150.
Cudney, E. A., Paryani, K., & Ragsdell, K. M. (2007). Applying the Mahalanobis-Taguchi system to vehicle ride. Journal of Industrial and System Engineering, 1(3), 251 – 259.
Fenton, N. E., & Pfleeger, S. L. (1996). Software Metrics, a rigours and practical approach. 2nd Edition, International Thomson Computer Press.
Fisher, R. A. (1974) The Design f Experiments. Hafner press, New York.
Fowlkes, W.Y., & Creveling, C.M. (1998). Engineering Methods for Robust Product Design –Using Taguchi Methods in Technology and Product Development. Addison-Wesley, Reading, MA.
Harter, D. E., Krishnan, M. S., & Slaughter, S. A. (2000). Effects of process maturity on quality, cycle time, and effort in software product development. Management Science, 46(4), 451-466.
Hayashi, S., Tanaka, Y., & Kodama, E. (2001). A new manufacturing control system using Mahalonobis distance for maximizing productivity. IEEE International Semiconductor Manufacturing Symposium, 15(4), 59 – 62.
Humphrey, W. S. (1988). Characterizing the software process: a maturity framework. IEEE Software, 5(2), 73-79.
Jacob, A.L., & Pillai, S.K. (2003). Statistical process control to improve coding and code review. IEEE Software, 20(3), 50 – 55.
Jain, A. K., Robert, P. W. D., & Jianchang, M. (2000). Statistical pattern recognition: A review. IEEE Transaction on Pattern Analysis and Machine Intelligence, 22, 4-37.
Jalote, P. (2000). CMM in Practice: Process for Executing Software Projects at Infosys. Addison-Wesley.
Jiang, J. J., Klein, G., Hwang, H. G., Huang, J., & Hung, S. Y. (2004). An exploration of the relationship between software development process maturity and project performance. Information & Management, 41(3), 279-288.
John, B., & Kadadevaramath, R. S. (2013). Optimization of the yield of a code review process. Proceedings of the International Conference on Quality, Reliability and Operations Research, 161 – 168, Excel India Publishers. ISBN: 978-93-82880-27-1.
John, B.., & Kadadevaramath, R. S. (2014). A methodology for achieving the design review defect density goals in software development process. International Journal of Manufacturing, Industrial & Management Engineering, 2(1), 181-191
John, B. (2014). Application of Mahalanobis-Taguchi system and design of experiments to reduce the field failures of splined shafts. International Journal of Quality & Reliability Management, 31(6), 681-697.
John, B. (2015). A dual response surface optimization methodology for achieving uniform coating thickness in powder coating process. International Journal of Industrial Engineering Computations, 6, 469 – 480.
Jugulam, R., & Monplaisir, L. (2002). Comparison between Mahalanabis-Taguchi system and artificial neural networks. Journal of Quality Engineering Society, 10(1), 60-73.
Kacker, R.N. (1985). Off-line Quality Control, Parameter Design and Taguchi Method, Journal of Quality Technology. 17, 176 – 209.
Mahalanobis, P.C. (1936). On Generalised distance in statistics. Proceedings of the National Institute of Sciences of India, 2(1), 49–55.
Montgomery, D.C. (2001). Design and Analysis of Experiments. John Wiley & Sons, New York.
Mishra, P.C., Das, D.K., Ukamanal, M., Routara, B.C., & Sahoo, A.K. (2015). Multi-response optimization of process parameters using Taguchi method and grey relational analysis during turning AA 7075/SiC composite in dry and spray cooling environments. International Journal of Industrial Engineering Computations, 6, 445 – 456.
Myatt, G. (2007). Making Sense of data: A Practical Guide to Exploratory Data Analysis and Data Mining, Vol. 1. John Wiley & Sons, Inc.
Naik, K., & Tripathy, P (2008). Software Testing and Quality Assurance. John Wiley and Sons.
Perry, W. (1995). Effective Methods for Software Testing. John Wiley & Sons.
Phadke, M.S. (1989). Quality engineering using robust design. Prentice Hall, USA.
Raczynski, B., & Curtis, B. (2008). Software data violate SPC’s underlying assumptions. IEEE Software, 25(3), 49.
Rama Rao, S., & Padmanabhan., G (2012). Application of Taguchi methods and ANOVA in optimization of process parameters for metal removal rate in electrochemical machining of Al/5%SiC composites. International Journal of Engineering Research and Applications, 2(3), 192 – 197.
Rao, U. S., Kestur, S., & Pradhan, C. (2008). Stochastic optimization modeling and quantitative project management. IEEE Software, 25(3), 29-36.
Sahoo, A. K., Rout, A. K., & Das, D. K. (2015). Response surface and artificial neural network prediction model and optimization for surface roughness in machining. International Journal of Industrial Engineering Computations, 6, 229 – 240.
Taguchi, G., Chowdhury, S., & Wu, Y. (2001). The Mahalanobis-Taguchi System. McGraw Hill, New York.
Taguchi, G., & Rajesh, J. (2000). New Trends in Multivariate Diagnosis. Sankhya: Indian Journal of Statistics, Series B, 62(2), 233-248.
Taguchi, G. (1980). Introduction to Off-line Quality Control. Central Japan Quality Control Association, Japan
Tian, J. (2005). Software Quality Engineering. John Wiley and Sons.
Weller, E., & Card, D. (2008). Applying SPC to software development: where and why. IEEE Software, 25(3), 48-50.
Woodall, W. H., Koulelik, R., Ysui, K. L., Kim, S. B., Stoumbos, Z. G., & Carvounis, C. P. (2003). A review and analysis of the Mahalanobis - Taguchi System. Technometrics, 45(1), 1- 30.
Wu, Y. (2004). Pattern Recognition Using Mahalanobis Distance. Journal of Quality Engineering Forum, 12(5), 787-795.
Akhyar, G., Che Haron, C. H., & Ghani, J. A. (2008). Application of Taguchi method in the optimization of turning parameters for surface roughness. International Journal of Science Engineering and Technology, l (3), 60 – 66.
Asada, M. (2001). Wafer yield prediction by the Mahalanobis-Taguchi system. IEEE International Workshop on Statistical Methodology, 6, 25-28.
Bachy, B., & Franke, J. (2015). Modeling and optimization of laser direct structuring process using artificial neural network and response surface methodology. International Journal of Industrial Engineering Computations, 6, 553 - 564.
Berkani, S., Yallese, M.A., Boulanouar, L., & Mabrouki, T. (2015). Statistical analysis of AISI304 austenitic stainless steel machining using Ti(C, N)/Al2O3/TiN CVD coated carbide tool. International Journal of Industrial Engineering Computations, 6, 539 - 552.
Box, G. E., Hunter, W. G., & Hunter, S. J. (1978.) Statistics for Experiments: An Introduction to Design, Data Analysis, and Model Building. Wiley, New York.
Chowdhury, K. K., & John, Boby. (2003). Optimization of the Induction Hardening Operation using Robust Design. Journal of Quality Engineering Forum, 11(4), 70-76.
Cudney, E. A., Hong, J., Jugulum, R., Paryani, K., Ragsdell, K.M., & Taguchi, G. (2007). An Evaluation of Mahalanobis-Taguchi Systems and Neural Network for Multivariate Pattern Recognition. Journal of Industrial and System Engineering, 1(2), 139 – 150.
Cudney, E. A., Paryani, K., & Ragsdell, K. M. (2007). Applying the Mahalanobis-Taguchi system to vehicle ride. Journal of Industrial and System Engineering, 1(3), 251 – 259.
Fenton, N. E., & Pfleeger, S. L. (1996). Software Metrics, a rigours and practical approach. 2nd Edition, International Thomson Computer Press.
Fisher, R. A. (1974) The Design f Experiments. Hafner press, New York.
Fowlkes, W.Y., & Creveling, C.M. (1998). Engineering Methods for Robust Product Design –Using Taguchi Methods in Technology and Product Development. Addison-Wesley, Reading, MA.
Harter, D. E., Krishnan, M. S., & Slaughter, S. A. (2000). Effects of process maturity on quality, cycle time, and effort in software product development. Management Science, 46(4), 451-466.
Hayashi, S., Tanaka, Y., & Kodama, E. (2001). A new manufacturing control system using Mahalonobis distance for maximizing productivity. IEEE International Semiconductor Manufacturing Symposium, 15(4), 59 – 62.
Humphrey, W. S. (1988). Characterizing the software process: a maturity framework. IEEE Software, 5(2), 73-79.
Jacob, A.L., & Pillai, S.K. (2003). Statistical process control to improve coding and code review. IEEE Software, 20(3), 50 – 55.
Jain, A. K., Robert, P. W. D., & Jianchang, M. (2000). Statistical pattern recognition: A review. IEEE Transaction on Pattern Analysis and Machine Intelligence, 22, 4-37.
Jalote, P. (2000). CMM in Practice: Process for Executing Software Projects at Infosys. Addison-Wesley.
Jiang, J. J., Klein, G., Hwang, H. G., Huang, J., & Hung, S. Y. (2004). An exploration of the relationship between software development process maturity and project performance. Information & Management, 41(3), 279-288.
John, B., & Kadadevaramath, R. S. (2013). Optimization of the yield of a code review process. Proceedings of the International Conference on Quality, Reliability and Operations Research, 161 – 168, Excel India Publishers. ISBN: 978-93-82880-27-1.
John, B.., & Kadadevaramath, R. S. (2014). A methodology for achieving the design review defect density goals in software development process. International Journal of Manufacturing, Industrial & Management Engineering, 2(1), 181-191
John, B. (2014). Application of Mahalanobis-Taguchi system and design of experiments to reduce the field failures of splined shafts. International Journal of Quality & Reliability Management, 31(6), 681-697.
John, B. (2015). A dual response surface optimization methodology for achieving uniform coating thickness in powder coating process. International Journal of Industrial Engineering Computations, 6, 469 – 480.
Jugulam, R., & Monplaisir, L. (2002). Comparison between Mahalanabis-Taguchi system and artificial neural networks. Journal of Quality Engineering Society, 10(1), 60-73.
Kacker, R.N. (1985). Off-line Quality Control, Parameter Design and Taguchi Method, Journal of Quality Technology. 17, 176 – 209.
Mahalanobis, P.C. (1936). On Generalised distance in statistics. Proceedings of the National Institute of Sciences of India, 2(1), 49–55.
Montgomery, D.C. (2001). Design and Analysis of Experiments. John Wiley & Sons, New York.
Mishra, P.C., Das, D.K., Ukamanal, M., Routara, B.C., & Sahoo, A.K. (2015). Multi-response optimization of process parameters using Taguchi method and grey relational analysis during turning AA 7075/SiC composite in dry and spray cooling environments. International Journal of Industrial Engineering Computations, 6, 445 – 456.
Myatt, G. (2007). Making Sense of data: A Practical Guide to Exploratory Data Analysis and Data Mining, Vol. 1. John Wiley & Sons, Inc.
Naik, K., & Tripathy, P (2008). Software Testing and Quality Assurance. John Wiley and Sons.
Perry, W. (1995). Effective Methods for Software Testing. John Wiley & Sons.
Phadke, M.S. (1989). Quality engineering using robust design. Prentice Hall, USA.
Raczynski, B., & Curtis, B. (2008). Software data violate SPC’s underlying assumptions. IEEE Software, 25(3), 49.
Rama Rao, S., & Padmanabhan., G (2012). Application of Taguchi methods and ANOVA in optimization of process parameters for metal removal rate in electrochemical machining of Al/5%SiC composites. International Journal of Engineering Research and Applications, 2(3), 192 – 197.
Rao, U. S., Kestur, S., & Pradhan, C. (2008). Stochastic optimization modeling and quantitative project management. IEEE Software, 25(3), 29-36.
Sahoo, A. K., Rout, A. K., & Das, D. K. (2015). Response surface and artificial neural network prediction model and optimization for surface roughness in machining. International Journal of Industrial Engineering Computations, 6, 229 – 240.
Taguchi, G., Chowdhury, S., & Wu, Y. (2001). The Mahalanobis-Taguchi System. McGraw Hill, New York.
Taguchi, G., & Rajesh, J. (2000). New Trends in Multivariate Diagnosis. Sankhya: Indian Journal of Statistics, Series B, 62(2), 233-248.
Taguchi, G. (1980). Introduction to Off-line Quality Control. Central Japan Quality Control Association, Japan
Tian, J. (2005). Software Quality Engineering. John Wiley and Sons.
Weller, E., & Card, D. (2008). Applying SPC to software development: where and why. IEEE Software, 25(3), 48-50.
Woodall, W. H., Koulelik, R., Ysui, K. L., Kim, S. B., Stoumbos, Z. G., & Carvounis, C. P. (2003). A review and analysis of the Mahalanobis - Taguchi System. Technometrics, 45(1), 1- 30.
Wu, Y. (2004). Pattern Recognition Using Mahalanobis Distance. Journal of Quality Engineering Forum, 12(5), 787-795.