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Prediction of Software Reliability Using Connectionist Models
July 1992 (vol. 18 no. 7)
pp. 563-574

The usefulness of connectionist models for software reliability growth prediction is illustrated. The applicability of the connectionist approach is explored using various network models, training regimes, and data representation methods. An empirical comparison is made between this approach and five well-known software reliability growth models using actual data sets from several different software projects. The results presented suggest that connectionist models may adapt well across different data sets and exhibit a better predictive accuracy. The analysis shows that the connectionist approach is capable of developing models of varying complexity.

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Index Terms:
software reliability; connectionist models; network models; training regimes; data representation methods; complexity; neural nets; software reliability
N. Karunanithi, D. Whitley, Y.K. Malaiya, "Prediction of Software Reliability Using Connectionist Models," IEEE Transactions on Software Engineering, vol. 18, no. 7, pp. 563-574, July 1992, doi:10.1109/32.148475
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