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An effective fault prediction model developed using an extreme learning machine with various kernel methods
- 一种有效的基于不同核函数的极限学习机故障预测模型
- Frontiers of Information Technology & Electronic Engineering Vol. 19, Issue 7, Pages: 864-888(2018)
- Affiliations：
Department of Computer Science and Engineering, National Institute of Technology Rourkela, Rourkela 769008, India
- Author bio：
Lov KUMAR, lovkumar505@gmail.com
[ "Anand TIRKEY, andy9c@gmail.com" ]
[ "Santanu-Ku. RATH, skrath@nitrkl.ac.in" ]
- Funds：
DOI：10.1631/FITEE.1601501
CLC： TP311Received：27 August 2016，
Revised：2018-;7-;8，
Published：2018-07
- Accepted：
Scan QR Code
Lov KUMAR, Anand TIRKEY, Santanu-Ku. RATH. An effective fault prediction model developed using an extreme learning machine with various kernel methods[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(7): 864-888.
Lov KUMAR, Anand TIRKEY, Santanu-Ku. RATH. An effective fault prediction model developed using an extreme learning machine with various kernel methods[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(7): 864-888. DOI： 10.1631/FITEE.1601501.
摘要
在软件开发生命周期的设计阶段，系统分析员常利用软件故障预测模型识别易产生故障的模块。故障预测模型通过软件度量指标预测缺陷模块。基于不同核函数的极限学习机，结合20类度量指标，建立一种故障预测模型。使用软件测试成本与效率的建议框架评估模型的效率，并对30个面向对象软件系统案例进行研究。实验结果表明，根据故障识别效率（低：47.28%；中：39.24%；高：25.72%），提出的故障预测模型适用于故障占比低于特定阈值的项目。为剔除不相关指标，并筛选适用于故障预测的最佳源代码指标集，考虑了9种不同的特征选择方法。
Abstract
System analysts often use software fault prediction models to identify fault-prone modules during the design phase of the software development life cycle. The models help predict faulty modules based on the software metrics that are input to the models. In this study
we consider 20 types of metrics to develop a model using an extreme learning machine associated with various kernel methods. We evaluate the effectiveness of the mode using a proposed framework based on the cost and efficiency in the testing phases. The evaluation process is carried out by considering case studies for 30 object-oriented software systems. Experimental results demonstrate that the application of a fault prediction model is suitable for projects with the percentage of faulty classes below a certain threshold
which depends on the efficiency of fault identification (low: 47.28%; median: 39.24%; high: 25.72%). We consider nine feature selection techniques to remove the irrelevant metrics and to select the best set of source code metrics for fault prediction.
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references
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