@inproceedings{2869291f3a76423a8fd5e07043ab1ced,
title = "A framework of the software quality comprehensive prediction based on defect knowledge base and risk module",
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.",
keywords = "Airborne equipment software, Knowledge base, Software metrics: Machine learning",
author = "Chen, \{Li Gong\} and Wang, \{Zi Li\} and Wang, \{Shi Hai\} and Yin, \{Yong Feng\} and Ji, \{Qi Zheng\}",
year = "2014",
doi = "10.4028/www.scientific.net/AMR.912-914.1077",
language = "英语",
isbn = "9783038350774",
series = "Advanced Materials Research",
publisher = "Trans Tech Publications",
pages = "1077--1082",
booktitle = "Frontiers of Advanced Materials and Engineering Technology II",
address = "德国",
note = "2014 International Conference on Frontiers of Advanced Materials and Engineering Technology, FAMET 2014 ; Conference date: 28-03-2014 Through 29-03-2014",
}