A conceptual Bayesian net model for integrated software quality prediction

• Autorzy: Radliński Ł.
• Języki publikacji: EN

Abstrakty

EN

Software quality can be described by a set of features, such as functionality, reliability, usability, efficiency, maintainability, portability and others. There are various models for software quality prediction developed in the past. Unfortunately, they typically focus on a single quality feature. The main goal of this study is to develop a predictive model that integrates several features of software quality, including relationships between them. This model is an expert-driven Bayesian net, which can be used in diverse analyses and simulations. The paper discusses model structure, behaviour, calibration and enhancement options as well as possible use in fields other than software engineering.

Słowa kluczowe

EN

Bayesian network; software quality; predictive model

• Wydawca: Wydawnictwo UMCS
• Czasopismo: Annales Universitatis Mariae Curie-Skłodowska. Sectio AI, Informatica
• Rocznik: 2011
• Tom: Vol. 11, no. 4
• Strony: 49--60
• Opis fizyczny: Bibliogr. 32 poz., rys., tab.

Twórcy

autor

Radliński Ł.

• Institute of Information Technology in Management, University of Szczecin, Mickiewicza 64, 71-101 Szczecin, Poland

Bibliografia

• [1] ISO/IEC 25000:2005, Software Engineering - Software product Quality Requirements and Evaluation (SQuaRE) - Guide to SQuaRE (2005).
• [2] ISO/IEC FDIS 9126-1:2001, Software Engineering - Product quality - Part 1: Quality model (2001).
• [3] Kan S.H., Metrics and Models in Software Quality Engineering, Addison-Wesley, Boston (2003).
• [4] Lyu M., Handbook of software reliability engineering, McGraw-Hill, Hightstown, NJ (1996).
• [5] Musa J.D. Software Reliability Engineering: More Reliable Software Faster and Cheaper, Second Edition, Authorhouse (2004).
• [6] Jensen F.V., Nielsen T.D., Bayesian Networks and Decision Graphs, Second Edition, Springer (2007).
• [7] Kjærulff U.B., Madsen A.L., Bayesian Networks and Influence Diagrams, A Guide to Construction and Analysis, Springer, New York (2008).
• [8] Abouelela M., Benedicenti L., Bayesian Network Based XP Process Modelling. International Journal of Software Engineering and Applications 1 (2010): 1.
• [9] Beaver J.M., A life cycle software quality model using bayesian belief networks, Doctoral Dissertation, University of Central Florida, Orlando, FL (2006).
• [10] 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 (2008): 1.
• [11] Fenton N., Marsh W., Neil M., Cates P., Forey S., Tailor M., Making Resource Decisions for Software Projects, In: 26th Int. Conference on Software Engineering, Washington DC (2004): 397.
• [12] 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 13 (2008): 499.
• [13] Radliński Ł., Fenton N., Neil M., Marquez D., Improved Decision-Making for Software Managers Using Bayesian Networks, In: 11th IASTED Int. Conf. Software Engineering and Applications, IASTED, Cambridge, MA (2007): 13.
• [14] Radlinski L., A Survey of Bayesian Net Models for Software Development Effort Prediction, International Journal of Software Engineering and Computing 2 (2010): 95 (in press).
• [15] Van Koten C., Gray A.R., An application of Bayesian network for predicting object-oriented software maintainability, Information and Software Technology 48 (2006): 59.
• [16] Wagner S., A Bayesian network approach to assess and predict software quality using activitybased quality models, In: 5th Int. Conf. on Predictor Models in Software Engineering, ACM Press, New York (2009).
• [17] Jones C., Applied Software Measurement: Global Analysis of Productivity and Quality, Third Edition, McGraw-Hill, New York (2008).
• [18] ISO/IEC FDIS 25010:2011, Systems and software engineering - Systems and software Quality Requirements and Evaluation (SQuaRE) - System and software quality models (2011).
• [19] ISO/IEC TR 9126-2:2003, Software engineering - Product quality - Part 2: External metrics (2003a).
• [20] ISO/IEC TR 9126-3:2003, Software engineering - Product quality - Part 3: Internal metrics (2003).
• [21] ISO/IEC TR 9126-4:2004, Software Engineering - Product quality - Part 4: Quality in use metrics (2004).
• [22] Alvaro A., Santana De Almeida E., Romero De Lemos Meira S., A software component quality framework, ACM SIGSOFT Software Engineering Notes 35 (2010): 1.
• [23] Côté M. A., Suryn W., Georgiadou E., In search for a widely applicable and accepted software quality model for software quality engineering, Software Quality Journal 15 (2007): 401.
• [24] Jarvis A., Crandall V., Inroads to Software Quality: "How to" Guide and Toolkit, Prentice Hall PTR, Upper Saddle River, NJ (1997).
• [25] O’Regan G., A Practical Approach to Software Quality, Springer-Verlag, New York (2002).
• [26] Ortega M., Perez M., Rojas T., Construction of a Systemic Quality Model for Evaluating a Software Product, Software Quality Journal 11 (2003): 219.
• [27] Rosqvist T., Koskela M., Harju H., Software Quality Evaluation Based on Expert Judgement, Software Quality Journal 11 (2003): 39.
• [28] Schulmeyer G.G., McManus J.I. (eds.), Handbook of Software Quality Assurance, Prentice Hall PTR, Upper Saddle River, NJ (1999).
• [29] Villalba M.T., Fernández-Sanz L., Martínez J.J., Empirical support for the generation of domainoriented quality models, IET Software 4 (2010): 1.
• [30] Fenton N.E., Neil M., Caballero J.G., Using Ranked Nodes to Model Qualitative Judgments in Bayesian Networks, IEEE Transactions on Knowledge and Data Engineering 19 (2007): 1420.
• [31] Radliński Ł., BaNISoQ: Bayesian Net Model for Integrated Software Quality Prediction, http://lukrad.univ.szczecin.pl/projects/banisoq/ (2011).
• [32] AgenaRisk BN Tool, Agena; http://www.agenarisk.com (2009).