NDLI: Extract rules from software quality prediction model based on neural network

Content Provider	IEEE Xplore Digital Library
Author	Qi Wang Bo Yu Jie Zhu
Copyright Year	2004
Description	Author affiliation: Dept. of Electron. Eng., Shanghai Jiao Tong Univ., China (Qi Wang)
Abstract	To get a highly reliable software product to the market on schedule, software engineers must allocate resources on the fault-prone software modules across the development effort. Software quality models based upon data mining from past projects can identify fault-prone modules in current similar development efforts. So that resources can be focused on fault-prone modules to improve quality prior to release. Many researchers have applied the neural networks approach to predict software quality. Although neural networks have shown their strengths in solving complex problems, their shortcoming of being 'black boxes' models has prevented them from being accepted as a common practice for fault-prone software modules prediction. That is a significant weakness, for without the ability to produce comprehensible decisions; it is hard to trust the reliability of neural networks that address real-world problems. We introduce an interpretable neural network model for software quality prediction. First, a three-layer feed-forward neural network with the sigmoid function in hidden units and the identity function in output unit was trained. The data used to train the neural network is collected from an earlier release of a telecommunications software system. Then use clustering genetic algorithm (CCA) to extract comprehensible rules from the trained neural network. We use the rule set extracted from the trained neural network to detect the fault-prone software modules of the later release and compare the predicting results with the neural network predicting results. The comparison shows that although the rule set's predicting accuracy is a little less than the trained neural network, it is more comprehensible.
Sponsorship	IEEE Comput. Soc.
Starting Page	191
Ending Page	195
File Size	72013
Page Count	5
File Format	PDF
ISBN	076952236X
ISSN	10823409
DOI	10.1109/ICTAI.2004.62
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-11-15
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Software quality Predictive models Neural networks Data mining Reliability engineering Resource management Fault diagnosis Feedforward systems Feedforward neural networks Software systems
Content Type	Text
Resource Type	Article

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