Abstract
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.
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References
Mooney, J.G., Gurbaxani, V., Kraemer, K.L.: A process oriented framework for assessing the business value of information technology. ACM SIGMIS Database 27(2), 68–81 (1996). https://doi.org/10.1145/243350.243363
Adam Jr., E.E., Flores, B.E., Macias, A.: Quality improvement practices and the effect on manufacturing firm performance: evidence from Mexico and the USA. Int. J. Prod. Res. 39(1), 43–63 (2001). https://doi.org/10.1080/00207540150208853
Samson, D., Terziovski, M.: The relationship between total quality management practices and operational performance. J. Oper Manag. 17(4), 393–409 (1999). https://doi.org/10.1016/S0272-6963(98)00046-1
Phan, D.D., George, J.F., Vogel, D.R.: Managing software quality in a very large development project. Inf. Manag. 29(5), 277–283 (1995). https://doi.org/10.1016/0378-7206(95)00032-2
Harter, D.E., Krishnan, M.S., Slaughter, S.A.: Effects of process maturity on quality, cycle time, and effort in software product development. Manage. Sci. 46(4), 451–466 (2000). https://doi.org/10.1287/mnsc.46.4.451.12056
Jalote, P.: CMM in Practice: Process for Executing Software Projects at Infosys. Addison-Wesley Longman Inc, NJ (2000)
Fenton, N.E., Pfleeger, S.L.: Software Metrics. A Rigorous and Practical Approach, 3rd edn. CRC Press, Boca Raton, FL (2015)
Tsung, F., Li, Y., Jin, M.: Statistical process control for multistage manufacturing and service operations: a review and some extensions. Int. J. Serv. Oper. Inf. 3(2), 191–204 (2008). https://doi.org/10.1504/IJSOI.2008.019333
Shu, L., Tsung, F., Tsui, K.L.: Effects of estimation errors on cause-selecting charts. IIE Trans. 37(6), 559–567 (2005). https://doi.org/10.1080/07408170590929027
John, B., Kadadevaramath, R.S., Edinbarough, I.A.: Application of multistage process control methodology for software quality management. J. Project Manag. 1(2), 55–66 (2016). https://doi.org/10.5267/j.jpm.2017.2.001
Ebadi, M., Ahmadi-Javid, A.: Control charts for monitoring multi-stage service processes with optimal queue performance. Commun. Stat. Simul. Comput. (2019). https://doi.org/10.1080/03610918.2018.1520872
Park, C., Ttsui, K.-L.: A profile monitoring of multi-stage process. Qual. Technol. Quant. Manag. 16(4), 407–423 (2018). https://doi.org/10.1080/16843703.2018.1447282
Gyulai, D., Pfeiffer, A., Monostori, L.: Robust production planning and control for multi-stage systems with flexible final assembly lines. Int. J. Prod. Res. 55(13), 3657–3673 (2017). https://doi.org/10.1080/00207543.2016.1198506
John, B.: Modeling the defect density of embedded system software using Bayesian belief networks: a case study. Softw. Qual. Prof. 14(3), 39–45 (2012)
John, B., Kadadevarmath, R.: A methodology for quantitatively managing the bug fixing process using Mahalanobis Taguchi system. Manag. Sci. Lett. 5(12), 1081–1090 (2015). https://doi.org/10.5267/j.msl.2015.10.006
Tamura, S.: CMMI and TSP/PSP: using TSP data to create process performance models. http://repository.cmu.edu/cgi/viewcontent.cgi?article=1284&context=sei. Last accessed 07 Dec 2016
Hao, Y., Zhang, Y.F.: Statistical prediction modeling for software development process performance. In: IEEE 3rd International Conference on Communication Software and Networks (ICCSN), pp. 703–706. https://doi.org/10.1109/ICCSN.2011.6014187 (2001)
Rao, U.S., Kestur, S., Pradhan, C.: Stochastic optimization modeling and quantitative project management. IEEE Softw. 25(3), 29–36 (2008). https://doi.org/10.1109/MS.2008.77
Antony, J., Fergusson, C.: Six Sigma in the software industry: results from a pilot study. Manag. Audit. J. 19(8), 1025–1032 (2004). https://doi.org/10.1108/02686900410557926
Draper, N.R., Smith, H.: Applied Regression Analysis, 3rd edn. Wiley, Singapore (2003)
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning Data Mining, Inference and Prediction, 2nd edn. Springer, New York (2009)
Lastad, L., Berntson, E., Näswall, K., Lindfors, P., Sverke, M.: Measuring quantitative and qualitative aspects of the job insecurity climate: scale validation. Career Dev. Int. 20(3), 202–217 (2015). https://doi.org/10.1108/CDI-03-2014-0047
Pang, G., Casalin, F., Papagiannidis, S., Muyldermans, L., Tse, Y.K.: Price determinants for remanufactured electronic products: a case study on eBay UK. Int. J. Prod. Res. 53(2), 572–589 (2005). https://doi.org/10.1080/00207543.2014.958594
Li, Y.F., Xie, M., Goh, T.N.: Adaptive ridge regression system for software cost estimating on multi-collinear datasets. J. Syst. Softw. 83(11), 2332–2343 (2010). https://doi.org/10.1016/j.jss.2010.07.032
Naik, J., Satapathy, P., Dash, P.K.: Short-term wind speed and wind power prediction using hybrid empirical mode decomposition and kernel ridge regression. Appl. Soft Comput. 70, 1167–1188 (2018). https://doi.org/10.1016/j.asoc.2017.12.010
Zhang, X., Chao, W., Li, Z., Liu, C., Li, R.: Multi-modal kernel ridge regression for social image classification. Appl. Soft Comput. 67, 117–125 (2018). https://doi.org/10.1016/j.asoc.2018.02.030
Bas, E., Egrioglu, E., Yolcu, U., Grosan, C.: Type 1 fuzzy function approach based on ridge regression for forecasting. Granul. Comput. (2018). https://doi.org/10.1007/s41066-018-0115-4
James, G., Witten, D., Hastie, T., Tibshirani, R.: An Introduction to Statistical Learning with Applications in R. Springer, New York (2013)
Fylstra, D., Lasdon, L., Watson, J., Waren, A.: Design and use of the Microsoft excel solver. Interfaces 28(5), 29–55 (1999)
Montgomery, D.C., Peck, E.A., Vining, G.G.: Introduction to Linear Regression Analysis, 3rd edn. Wiley, Singapore (2003)
Hailesilasie, G.: Determinants of public employees’ performance: evidence from Ethiopian public organizations. Int. J. Prod. Perform. Manag. 58(3), 238–253 (2009). https://doi.org/10.1108/17410400910938841
John, B., Vaibhav, A.: A regression spline control chart for monitoring characteristics exhibiting nonlinear profile over time. TQM J. 31(3), 507–522 (2019). https://doi.org/10.1108/TQM-08-2018-0105
Montgomery, D.C.: Introduction to Statistical Quality Control, 4th edn. Wiley, New Delhi (2002)
Leavenworth, R.S., Grant, E.L.: Statistical Quality Control, 7th edn. Tata McGraw-Hill Education, New Delhi (2000)
Taha, H.A.: Operations Research—An Introduction, 9th edn. Pearson, New Delhi (2014)
Hillier, F.S., Lieberman, G.J.: Operations Research—Concepts and Cases, 8th edn. Tata McGraw-Hill Publishing Company Ltd, New Delhi (2008)
John, B.: SLA Baselining of non-normal metrics: a profit optimization approach. Softw. Qual. Prof. 12(2), 42–44 (2010)
Prasad, R.S., Sowmya, K., Mahesh, R.: Monitoring software failure process using half logistic distribution. Int. J. Comput. Appl. 145(4), 1–8 (2016)
Yamada, S., Yamaguchi, M.: A method of statistical process control for successful open source software projects and its application to determining the development period. Int. J. Reliab. Quality Saf. Eng. 23(05), 1650018-1:17 (2016). https://doi.org/10.1142/S0218539316500182
Kim, H.C.: The comparative study for statistical process control of software reliability model based on finite and infinite NHPP using Rayleigh distribution. Int. J. Soft Comput. 11(3), 165–171 (2016)
Fehlmann, T.M., Kranich, E.: October. A new approach for continuously monitoring project deadlines in software development. In: 27th International Workshop on Software Measurement and 12th International Conference on Software Process and Product Measurement, ACM, pp. 161–169, Gothenburg, Sweden (2017)
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The authors are grateful to the reviewers and organizing committee of AFOR—2017 for providing the opportunity to present the paper at AFOR—2017 held at Kolkata, India.
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John, B., Kadadevaramath, R.S. Optimization of software development life cycle process to minimize the delivered defect density. OPSEARCH 56, 1199–1212 (2019). https://doi.org/10.1007/s12597-019-00414-y
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DOI: https://doi.org/10.1007/s12597-019-00414-y