Affiliation:
1. Beihang University
2. Beijing Oriental Institute of Measurement and Test
Abstract
Due to the importance of the Airborne Equipment Software (AES), much more attentions have been drawn into here. Building a unified, standardized and effective management AES defect knowledge base with these data is a definitely valuable work. In this paper a framework of software quality integrate prediction has been established, which is highly essential to make accurate evaluations on the quality, predictions on the defects, identifications on the fault-prone modules. A framework on how to build an AES knowledge base is proposed, a combination mechanism is proposed by involving machine learning technology and production system, in which, in order to provide the instructions for defect prediction and quality assessment of AES.
Publisher
Trans Tech Publications, Ltd.
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