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An effective fault prediction model developed using an extreme learning machine with various kernel methods

Frontiers of Information Technology & Electronic Engineering 2018年第7期19卷 864-888页

作者： Lov KUMAR anand tirkey Santanu-Ku.RATH Department of Computer Science and Engineering National Institute of Technology Rourkela

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.

关键词：模型预言机器学习软件开发度量标准设计阶段生命周期

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Software design pattern mining using classification-based techniques

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Frontiers of Computer Science 2018年第5期12卷 908-922页

作者： Ashish Kumar DWIVEDI anand tirkey Santanu Kumar RATH Department of Computer Science and Engineering National Institute of Technology Rourkela 769008 India

Design patterns are often used in the development of object-oriented software. It offers reusable abstract information that is helpful in solving recurring design problems. Detecting design patterns is beneficial to the comprehension and maintenance of object-oriented software systems. Several pattern detection techniques based on static analysis often encounter problems when detecting design patterns for identical structures of patterns. In this study, we attempt to detect software design patterns by using software metrics and classification-based techniques. Our study is conducted in two phases： creation of metrics-oriented dataset and detection of software design patterns. The datasets are prepared by using software metrics for the learning of classifiers. Then, pattern detection is performed by using classification-based techniques. To evaluate the proposed method, experiments are conducted using three open source software programs, JHotDraw, QuickUML, and JUnit, and the results are analyzed.

关键词： design patterns design pattern mining machinelearning techniques object-oriented metrics

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