Predictive evaluation for software testing progress via GMDH networks
Yasuhide Shinohara, Tadashi Dohi, Shunji Osaki · Electronics and Communications in Japan (Part III Fundamental Electronic Science) · 1999
The GMDH network is a learning machine based on the principle of heuristic self-organization. In this paper, use of the GMDH network for predicting the testing progress of software products is discussed. The fundamental GMDH and the improved GMDH using the AIC as the evaluation criterion are introduced for estimating the fault-occurrence times observed in the testing of software. Finally, in a numerical example, the GMDH network, an existing software reliability growth model, and a multilayered neural network are compared from the viewpoint of predicting performance. As a result, it is shown that the GMDH network overcomes the problem of determining an adequate network size in using a multilayered neural network and, in addition, provides a more accurate measure in evaluating software reliability than other prediction models. © 1999 Scripta Technica, Electron Comm Jpn Pt 3, 82(5): 22–28, 1999