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Towards expert-based modelling of integrated software quality

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Języki publikacji
EN
Abstrakty
EN
This paper reports on a part of a project aimed at building an probabilistic model for integrated software quality simulation and prediction. This paper discusses results of the questionnaire survey focused on gathering expert knowledge about the factors influencing various features of software quality. Specifically, this analysis identifies project and process factors of software quality, investigates relationships between quality features and their sub-features as well as priorities for quality features. The survey has been performed among software engineering experts and projects managers. Obtained results will be used to calibrate that model for software quality simulation and prediction. These results also partially deliver a general overview on how software quality features are perceived by industry.
Rocznik
Strony
13--26
Opis fizyczny
Bibliogr. 17 poz., rys., tab.
Twórcy
Bibliografia
  • [1] Abouelela M., Benedicenti L., Bayesian Network Based XP Process Modelling, International Journal of Software Engineering and Applications, vol 1, no.3, pp. 1-15, 2010.
  • [2] Beaver J.M., A life cycle software quality model using Bayesian belief networks, Doctoral Dissertation, University of Central Florida, Orlando, FL, 2006.al of Software Engineering and Applications, vol 1, no.3, pp. 1-15, 2010.
  • [3] Fenton N., Hearty P., Neil M., Radliński Ł., Software Project and Quality Modelling Using Bayesian Networks. In: Meziane, F., Vadera, S. (eds.) Artificial Intelligence Applications for Improved Software Engineering Development: New Prospects, Information Science Reference, New York, pp. 1-25, 2008.
  • [4] Fenton N., Neil M., Marsh W., Hearty P., Radliński Ł., Krause P., On the effectiveness of early life cycle defect prediction with Bayesian Nets, Empirical Software Engineering, vol. 13, pp. 499-537, 2008.
  • [5] ISO/IEC 25010:2011(E), Software engineering – Software product Quality Requirements and Evaluation (SQuaRE) – System and software quality models, 2011.
  • [6] Jones C., Applied Software Measurement: Global Analysis of Productivity and Quality, Third Edition, McGraw-Hill, New York, 2008.
  • [7] Kan S. H., Metrics and Models in Software Quality Engineering, Addison-Wesley, Boston, 2003.
  • [8] Lyu M., Handbook of software reliability engineering, McGraw-Hill, Hightstown, NJ, 1996.
  • [9] Maxwell, K.D.: Applied Statistics for Software Managers. Prentice Hall PTR, Upper Saddle River, 2002.
  • [10] Radliński Ł., A conceptual Bayesian net model for integrated software quality prediction, Annales UMCS, Informatica, vol. 11, no. 4, pp. 49-60, 2011.
  • [11] Radliński Ł., A Framework for Integrated Software Quality Prediction using Bayesian Nets, in Proceedings of International Conference on Computational Science and Its Applications (ICCSA 2011), Santander: Springer, 2011.
  • [12] Radliński Ł., Empirical Analysis of the Impact of Requirements Engineering on Software Quality, Requirements Engineering: Foundation for Software Quality, Lecture Notes in Computer Science, vol. 7195, Springer, Berlin-Heidelberg, pp. 232-238, 2012.
  • [13] Radliński Ł., Enhancing Bayesian Network Model for Integrated Software Quality Prediction, in Proc. Fourth International Conference on Information, Process, and Knowledge Management, Valencia, 2012, pp. 144-149.
  • [14] Radliński Ł., Factors of Software Quality – Analysis of Extended ISBSG Dataset, Foundations of Computing and Decision Studies, vol. 36, no. 3-4, pp. 293-313, 2011.
  • [15] Van Koten C., Gray A.R., An application of Bayesian network for predicting objectoriented software maintainability, Information and Software Technology, vol. 48, pp. 59-67, 2006.
  • [16] Wagner S., A Bayesian network approach to assess and predict software quality using activity-based quality models, In: 5th Int. Conf. on Predictor Models in Software Engineering, ACM Press, New York, 2009.
  • [17] Zhang D., Tsai J. J. P., Machine Learning and Software Engineering, Software Quality Journal, vol. 11, no. 2, pp. 87-119, 2003.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPS3-0025-0118
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