Abstract
Although a number of approaches have been taken to quality prediction for software. none have achieved widespread applicability. Our aim here is to produce a single model to combine the diverse forms of often causal, evidence available in software development in a more natural and efficient way than done previously. We use Bayesian Belief Networks as the appropriate formalism for representing this evidence. We can use the subjective judgements of experienced project managers to build the probability model and use this model to produce forecasts about the software quality throughout the development life cycle. Moreover, the causal or influence structure of the model more naturally mirrors the real world sequence of events and relations than can be achieved with other formalisms. The paper focuses on the particular model that has been developed for Philips Consumer Electronics, using expert knowledge from Philips Research Labs. The model is used especially to predict defect rates at various testing and operational phases. To make the model usable by software quality managers we have developed a tool (AID) and have used it to validate the model on 28 diverse projects from within Philips. In each of these projects, extensive historical records were available. The results of the validation are encouraging. In most cases the model provides accurate predictions of defect rates even on projects whose size was outside the original scope of the model.
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References
Adams E. (1984), “Optimizing preventive service of software products”, IBM Research Journal, 28(1), 2–14.
Agena (2002) Agena Ltd, “Bayesian Belief Nets”, http://www.agena.co.uk
Basili V., Briand L. and Melo W.L. (1996), “A validation of object oriented design metrics as quality indicators”, IEEE Trans. Software Eng.
Cartwright M. and Shepperd M. (1997), “Building predictive models from object-oriented metrics,” presented at 8th European Software Control and Metrics Conf., Berlin.
Elliot J. (2001). ESPRIT Project MODIST: Models of Uncertainty and Risk for Distributed Software Development. http://www.modist.org.uk/
Fenton N. (1999) SERENE consortium, “SERENE (SafEty and Risk Evaluation using Bayesian Nets): Method Manual”, ESPRIT Project 22187, http://www.dcs.qmw.ac.uk/~norman/serene.htm/~norman/serene.htm.
Fenton N. and M. Neil (1999) “A Critique of Software Defect Prediction Research”, IEEE Trans. Software Eng., 25, No.5, 675–689.
Fenton N. and N. Ohlsson (2000) “Quantitative analysis of faults and failures in a complex software system”, IEEE Trans. Software Eng., 26, 797–814.
Fenton N.E. and S.L. Pfleeger (1997), Software Metrics: A Rigorous and Practical Approach, (2nd Edition), PWS Publishing Company.
Hugin (1998). Hugin Expert Brochure, Hugin Expert A/S, P.O. Box 8201 DK-9220 Aalborg, Denmark.
Khoshgoftaar T.M. and Munson J. C. (1990), “Predicting software development errors using complexity metrics”. IEEE J of Selected Areas in Communications, Vol.8, No.2, pp.253–261, 1990.
Krause P.J. (1998) “Learning Probabilistic Networks”, Knowledge Engineering Review, 13, 321–351.
Lauritzen S.L. and D.J. Spiegelhalter, (1988) “Local computations with probabilities on graphical structures and their application to expert systems (with discussion)” J. Roy. Stat. Soc. Ser B 50, pp. 157–224.
McCall, P.K. Richards and G.F. Walters (1977), Factors in software quality. Volumes 1, 2 and 3. Springfield Va., NTIS, AD/A-049-014/015/055.
Musa J. (1999). Software Reliability Engineering, McGraw Hill.
Neil M. (1999) IMPRESS (IMproving the software PRocESS using bayesian nets) EPSRC Project GR/L06683, http://www.csr.city.ac.uk/csr_city/projects/impress.html
Neil M., B. Littlewood and N. Fenton. (1996). “Applying Bayesian Belief Networks to Systems Dependability Assessment”. Proceedings of Safety Critical Systems Club Symposium, Leeds, Published by Springer-Verlag.
Neil M., N. Fenton and L. Nielson (2000), “Building large-scale Bayesian Networks”, Knowledge Engineering Review, 15(3), 257–284.
Pearl J. (1997) Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference Morgan Kauffman.
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Neil, M., Krause, P., Fenton, N. (2003). Software Quality Prediction Using Bayesian Networks. In: Khoshgoftaar, T.M. (eds) Software Engineering with Computational Intelligence. The Springer International Series in Engineering and Computer Science, vol 731. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-0429-0_6
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DOI: https://doi.org/10.1007/978-1-4615-0429-0_6
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