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Software quality improvement: a model based on managing factors impacting software quality

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Abstract

Software quality is recognized as being very significant for achieving competitiveness in the software industry, so improvements in this area are gaining increasing importance. Software quality improvements can only be achieved by managing all of the factors that influence it. However, in a real business system, there are a great number of factors impacting software quality, while the processes are stochastic and resources are limited, so economic data should also be taken into consideration. This paper uses a Markov chain and proposes a systematic framework for modelling the stochastic processes of a quality management system and selection of the optimum set of factors impacting software quality. A methodology is presented for managing the factors that affect software quality with an illustrative hypothetical example for convenience of application of the proposed methodology.

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References

  • Anderson, D. R., Sweeney, D. J., & Williams, T. A. (1994). An introduction to management science, quantitative approach to decision making. Eagan: West Publishing Company.

    Google Scholar 

  • Arthur, L. J. (1993). Improving software quality: An insider’s guide to TQM. New York: Wiley.

    MATH  Google Scholar 

  • Baum, L. E., & Petrie, T. (1966). Statistical inference for probabilistic functions of finite state Markov chains. The Annals of Mathematical Statistics, 37(6), 1554–1563.

  • Bayona-Oré, S., Calvo-Manzano, J. A., Cuevas, G., & San-Feliu, T. (2014). Critical success factors taxonomy for software process deployment. Software Quality Journal, 22, 21–24.

    Article  Google Scholar 

  • Berthiaux, H., & Mizonov, V. (2004). Applications of markov chains in particulate process engineering: A review. The Canadian Journal of Chemical Engineering, 82(6), 1143–1168.

    Article  Google Scholar 

  • Boehm, B. (1998). WinWin spiral model: A case study. In IEEE.

  • Boehm, B. W., Brown, J. R., Kaspar, H., Lipow, M., MacLeod, G., & Merritt, M. J. (1978). Characteristics of software quality. Amsterdam: North Holland.

    MATH  Google Scholar 

  • Bowling, S. R., Khasawneh, M. T., Kaewkuekool, S., & Cho, B. R. (2004). A Markovian approach to determining optimum process target levels for a multi-stage serial production system. European Journal of Operational Research, 159(3), 636–650.

    Article  MathSciNet  MATH  Google Scholar 

  • Budgen, D. (1994). Sof tware design. Wokingham: Addison-Wesley.

    Google Scholar 

  • Cheikhrouhou, N., Hachen, C., & Glardon, R. (2009). A Markovian model for the hybrid manufacturing planning and control method ‘Double Speed Single Production Line’. Computers & Industrial Engineering, 57(3), 1022–1032.

    Article  Google Scholar 

  • Dash, Y., & Dubey, S. K. (2012). application of principal component analysis in software quality improvement. International Journal of Advanced Research in Computer Science and Software Engineering, 2(4), 202–205.

    Google Scholar 

  • Deming, W. E. (1986). Out of the crisis. Cambridge, MA: MIT Centre for Advanced Engineering Study.

    Google Scholar 

  • Dunn, R. (1990). SQA: A management perspective, American Programmer, November.

  • Durrett, R. (1996). Probability theory and examples (2nd ed.). Ithaca: Cornel University.

    MATH  Google Scholar 

  • Endall, K. E., & Kendall, J. E. (1995). Systems analysis and design. Prentice Hall, NJ: Upper Saddle River.

    Google Scholar 

  • Erikkson, I., & McFadden, F. (1993). Quality function deployment: A tool to improve software quality. Information and Software Technology, 35(9), 491–498.

    Article  Google Scholar 

  • Frei, F. X., & Kalakota, R. (1999). Process variation as a determinant of service quality and bank performance: Evidence from the retail banking study. Management Science, 45(9), 1210–1220.

    Article  Google Scholar 

  • Gautreau, N., Yacout, S., & Hall, R. (1997). Simulation of partially observed Markov decision process and dynamic quality improvement. Computers & Industrial Engineering, 32(4), 691–700.

    Article  Google Scholar 

  • Ghosh, A., & Majumdar, S. K. (2010). Modeling failure types and failure times of turning and boring machine systems. International Journal of Quality & Reliability Management, 27(7), 815–831.

    Article  Google Scholar 

  • Glock, C. H., & Jaber, M. Y. (2013). A multi-stage production-inventory model with learning and forgetting effects, rework and scrap. Computers & Industrial Engineering, 64(2), 708–720.

    Article  Google Scholar 

  • Goyal, A., Sharma, S. K., & Gupta, P. (2009). Availability analysis of a part of rubber tube production system under preemptive resume priority repair. International Journal of Industrial Engineering, 16(4), 260–269.

    Google Scholar 

  • Griffin, A., & Hauser, J. (1993). Voice of the customer. Marketing Science, 12(1), 1–27.

    Article  Google Scholar 

  • He, Z., Staples, G., Ross, M., & Court, I. (1996). Fourteen Japanese quality tools in software process improvement. The TQM Magazine, 8(4), 40–44.

    Article  Google Scholar 

  • Hevner, A. R. (1997). Phase containment metrics for software quality improvement. Information and Software Technology, 39(13), 867–877.

    Article  Google Scholar 

  • Hsin-Hung, W., & Jiunn-I, S. (2008). Applying a Markov chain model in quality function deployment. Quality & Quantity, 42(5), 665–678.

    Article  Google Scholar 

  • Humphrey, W. S. (1989). Managing the software process. Reading, MA: Addison-Wesley.

    Google Scholar 

  • ISO 9000:2005. (2005) Quality management systems: Fundamentals and vocabulary. International Organization for Standardization.

  • Jørgensen, M. (1999). Software quality measurement. Advances in Engineering Software, 30(12), 907–912.

    Article  Google Scholar 

  • Karlsson, J. (1997). Managing software requirements using quality function deployment. Software Quality Journal, 6, 311–325.

    Article  MathSciNet  Google Scholar 

  • Kemp, S. (2005). Quality management demystified. Blacklick, OH: McGraw-Hill Professional Publishing.

    Google Scholar 

  • Khan, H. A. (2013). Establishing a defect management process model for software quality improvement. International Journal of Future Computer and Communication, 2(6), 585–589.

    Article  Google Scholar 

  • Khoo, M. B. C. (2003–2004). Design of runs rules schemes. Quality Engineering, 16(1), 27–43.

  • Khoshgoftaar, T. M., Allen, E. B., Wendell, D. J., & Hudepohl, J. P. (2001). Cost–benefit analysis of software quality models. Software Quality Journal, 9(1), 9–30.

    Article  Google Scholar 

  • Kondo, Y. (2001). Customer satisfaction: How can I measure it? Total Quality Management & Business Excellence, 12(7–8), 867–872.

    Article  Google Scholar 

  • Korkmaz, O., Akman, I., & Ostrovska, S. (2011). Assessing software quality using the markov decision processes. Human Factors and Ergonomics in Manufacturing & Service Industries, 21(1), 1–19.

    Article  Google Scholar 

  • Kwong, C. K., & Bai, H. (2002). A fuzzy AHP approach to the determination of importance weights of customer requirements in quality function deployment. Journal of Intelligent Manufacturing, 13, 367–377.

    Article  Google Scholar 

  • Lai, C. D., Xie, M., & Govindaraju, K. (2000). Study of a Markov model for a high-quality dependent process. Journal of Applied Statistics, 27(4), 461–473.

    Article  MATH  Google Scholar 

  • Latif, M. S., Sarwar, M. U., Talib, M. R., Khan, M. I., & Sarwar, M. B. (2013). Improvement in software quality with prioritization requirements. International Journal of Computer Science and Management Research, 2(4), 2171–2183.

    Google Scholar 

  • Lethbridge, T. C., & Laganière, R. (2005). Object-oriented software engineering: Practical software development using UML and Java (2nd ed.). London: McGraw-Hill Education.

    Google Scholar 

  • Li, J., & Huang, N. (2007). Quality evaluation in flexible manufacturing systems: a markovian approach. Mathematical Problems in Engineering, 2007, 24.

  • Merlyn, V., & Parkinson, J. (1994). Development effectiveness: Strategies for IS organizational transition. New York: Wiley.

    Google Scholar 

  • Modgil, V., Sharma, S. K., & Singh, J. (2012). Performance modeling and availability analysis of sole lasting unit in shoe making industry. Nature and Science, 10(2), 45–49.

    Google Scholar 

  • O’Kane, J., Papadoukakis, A., & Hunter, D. (2007). Simulation usage in SMEs. UK Journal of Small Business and Enterprise Development, 14(3), 514–527.

    Article  Google Scholar 

  • Oliva, R., & Bean, M. (2008). Developing operational understanding of service quality through a simulation environment. International Journal of Service Industry Management, 19(2), 160–175.

    Article  Google Scholar 

  • Oriogun, P. K. (1999). A survey of Boehm’s work on the spiral models and COCOMO II towards software development process quality improvement. Software Quality Journal, 8(1), 53–62.

    Article  Google Scholar 

  • Pakdil, F., Özkök, O., Dengiz, B., Kara, I., Selvi, N., & Kargi, A. (2009). A systematic approach to reduce human and system-related errors causing customer dissatisfaction in a production environment. Total Quality Management & Business Excellence, 20(1), 129–137.

    Article  Google Scholar 

  • Parzinger, M. J., & Nath, R. (2000). A study of the relationships between total quality management implementation factors and software quality. Total Quality Management, 11(3), 353–371.

    Article  Google Scholar 

  • Pfleeger, S. L., & Atlee, J. M. (2006). Software engineering theory and practice. New Jersey: Pearson Education, Prentice Hall.

    Google Scholar 

  • Pillai, V. M., & Chandrasekharan, M. P. (2008). An absorbing Markov chain model for production systems with rework and scrapping. Computers & Industrial Engineering, 55(3), 695–706.

    Article  Google Scholar 

  • Pukite, J., & Pukite, P. (1998). Modeling for reliability analysis: Markov modeling for reliability, maintainability, safety and supportability analyses of complex systems. New York: IEEE Press Inc.

    Book  Google Scholar 

  • Sanz, L. F., Diez, T., Pages, C., & Martinez, J. J. (2013). Managing factors which influence software quality results. Covenant Journal of Informatics and Communication Technology (CJICT), 1(1), 10–14.

    Google Scholar 

  • Saunders, J., & Curran, E. (1994). Software quality. Wokingham: Addison-Wesley.

    Google Scholar 

  • Schiffauerova, A., & Thomson, V. (2006). Managing cost of quality: Insight into industry practice. Total Quality Management, 18(5), 542–550.

    Google Scholar 

  • Sharma, A. K., Kalia, A., & Singh, H. (2012). An analysis of optimum software quality factors. IOSR Journal of Engineering, 2(4), 663–669.

    Article  Google Scholar 

  • Sokolowski, J. A., & Banks, C. M. (2009). Principles of modeling and simulation: A multidisciplinary approach. New York: Wiley.

    Book  MATH  Google Scholar 

  • Stanley, J., & Malhoit, G. (2001). Spreadsheet Markov analysis for plant power reliability. Quality Engineering, 13(3), 457–464.

    Article  Google Scholar 

  • Stasiowski, F. A., & Burnstein, P. E. (1994). Total quality management for the design firm. New York: Wiley.

    Google Scholar 

  • Sullivan, L. P. (1986). Quality function deployment. Quality Progress, 19(6), 39–50.

    Google Scholar 

  • Tai, A. H., Ching, W. K., & Chan, L. Y. (2009). Detection of machine failure: Hidden Markov model approach. Computers & Industrial Engineering, 57(2), 608–619.

    Article  Google Scholar 

  • Tavaré, S. (1979). A note on finite homogeneous continuous-time Markov chains. Biometrics, 35(4), 831–834.

    Article  MathSciNet  MATH  Google Scholar 

  • Wang, P.-C., Wu, H.-H., & Liao, A. Y. H. (2005). Combining grey theory and quality function deployment in analyzing customer dynamic requirements. International Journal of Advanced Manufacturing Technology, 25(11–12), 1241–1247.

    Google Scholar 

  • Will, J., Bertrand, M., & Fransoo, J. C. (2002). Modelling and simulation: Operations management research methodologies using quantitative modeling. International Journal of Operations & Production Management, 22(2), 241–264.

    Article  Google Scholar 

  • Wu, H.-H., Wu, C.-H., & Lin, J. T. (2011). Dynamic selling of quality-graded products under demand uncertainties. Computers & Industrial Engineering, 61(1), 141–149.

    Article  MathSciNet  Google Scholar 

  • Yacout, S., & Gautreau, N. (2000). A partially observable simulation model for quality assurance policies. International Journal of Production Research, 38(2), 253–267.

    Article  MATH  Google Scholar 

  • Yao, Y.-H., & Lee, H.-K. (2004). Applying ISO 9001 and CMMI in quality-oriented knowledge management for software process improvement. International Journal of Electronic Business Management, 2(2), 140–151.

    Google Scholar 

  • Yourdon, E. (1992). Decline and fall of the american programmer. Englewood Cliffs, NJ: Yourdon Press.

    Google Scholar 

  • Zahedi, F. (1995). Quality information systems. Danvers, MA: Boyd & Fraser.

    Google Scholar 

  • Zhang, Z. (2001). Implementation of total quality management: An empirical study of Chinese manufacturing firms, Unpublished doctoral dissertation, University of Groningen, Netherlands.

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Authors and Affiliations

  1. Quality Management Department, Faculty of Organizational Sciences, University of Belgrade, Belgrade, Serbia

    Ivan Janicijevic, Maja Krsmanovic & Nedeljko Zivkovic

  2. Software Engineering Department, Faculty of Organizational Sciences, University of Belgrade, Belgrade, Serbia

    Sasa Lazarevic

Authors
  1. Ivan Janicijevic
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  2. Maja Krsmanovic
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  3. Nedeljko Zivkovic
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  4. Sasa Lazarevic
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Corresponding author

Correspondence to Maja Krsmanovic.

Appendix: Selection of optimum strategy algorithm

Appendix: Selection of optimum strategy algorithm

Represents a state of output for each activity (correctly–incorrectly; in accordance–not in accordance; adequate–inadequate, …). In the manuscript shown in Table 6

A state transition diagram is used to describe the behavior of systems. In the manuscript shown in Fig. 3

On the basis of historical data from monitoring the process performances. In the manuscript shown in Table 7: Option 1

Calculation based on the initial probability of transition from one state to another (shown in Table 7: Option 1) and impact of factors on the quality using Formulas (15) and (16). The results of calculations are shown in Table 7: Option 2, 3 and 4

Using Markov chain simulation based on transition probability matrices. The probabilities of delivery of the conformant software are shown in Fig. 4

Based on the costs of improvements of the factors impacting software quality and the probability of delivery of the conformant software. Comparisons of the options are shown in Fig. 4

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Janicijevic, I., Krsmanovic, M., Zivkovic, N. et al. Software quality improvement: a model based on managing factors impacting software quality. Software Qual J 24, 247–270 (2016). https://doi.org/10.1007/s11219-014-9257-z

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