| Autor: |
Chen, Li Gong, Wang, Zi Li, Wang, Shi Hai, Yin, Yong Feng, Ji, Qi Zheng |
| Zdroj: |
Advanced Materials Research; April 2014, Vol. 912 Issue: 1 p1077-1082, 6p |
| Abstrakt: |
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. |
| Databáze: |
Supplemental Index |
| Externí odkaz: |
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