http://swrc.ontoware.org/ontology#Article
Precise Formulation and Applicability of a Software Reliability Growth Model Based on Hyper-Geometric Distribution
en
Department of Computer Science Tokyo Institute of Technology/Currently with TOSHIBA Corporation Systems & Software Engineering Laboratory Saiwai-ku
Department of Computer Science Tokyo Institute of Technology
Raymond Jacoby
Yoshihiro Tohma
In this paper the Hyper-Geometric Distribution is used to estimate the number of faults in a program at the beginning of the test-and-debug phase. The Hyper-Geometric Distribution Growth Model (HGD Model) is well suited to estimating the growth curves of the observed accumulated number of detected faults. The advantage of the model is its applicability to all kinds of observed data. Application of a single model makes it possible to calculate exponential growth curves as well as S-shaped growth curves. First the HGD Model is precisely formulated. Next the exact relationship of the model to the NHPP Goel-Okumoto Growth Model and the Delayed S-shaped Growth Model is shown. Assumption of an appropriate value of w(i) the sensitivity factor of the proposed model will establish the S-shaped HGD Growth Model. The introduction of a variable fault detection rate significantly increases the goodness of fit of the estimated growth curve to the growth curve of actually observed faults. Various examples of the applicability of our model to actually observed data demonstrate the characteristics of the HGD Model.
In this paper, the Hyper-Geometric Distribution is used to estimate the number of faults in a program at the beginning of the test-and-debug phase. The Hyper-Geometric Distribution Growth Model (HGD Model) is well suited to estimating the growth curves of the observed accumulated number of detected faults. The advantage of the model is its applicability to all kinds of observed data. Application of a single model makes it possible to calculate exponential growth curves, as well as S-shaped growth curves. First, the HGD Model is precisely formulated. Next, the exact relationship of the model to the NHPP Goel-Okumoto Growth Model and the Delayed S-shaped Growth Model is shown. Assumption of an appropriate value of w(i), the sensitivity factor of the proposed model, will establish the S-shaped HGD Growth Model. The introduction of a variable fault detection rate significantly increases the goodness of fit of the estimated growth curve to the growth curve of actually observed faults. Various examples of the applicability of our model to actually observed data demonstrate the characteristics of the HGD Model.
AA00700121
Journal of Information Processing
14
2
192-203
1991-07-31
1882-6652