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首页> 外文会议>2019 4th International Conference on Internet of Things: Smart Innovation and Usages >Software Fault Proneness Prediction Using Genetic Based Machine Learning Techniques
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Software Fault Proneness Prediction Using Genetic Based Machine Learning Techniques

机译：基于遗传机器学习技术的软件故障倾向性预测

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摘要

This work is an attempt to propose a software replica to predict fault proneness by means of genetic based method implementing machine learning. The underlying method is collection of data from open source software, where the data will be in form of object oriented metrics. The said data would be used to create model for forecasting the faults. These techniques are known as genetic based Classifier Systems or learning classifier systems. Later in this work, there is in detail description about data collection technique and stepwise algorithm to get the results. In the end it can be concluded that these techniques can be used to make prediction model on object oriented data of software and can be useful pertaining to fault proneness prediction in the near the beginning stages in the development sequence. of any software (SDLC).
机译：这项工作是尝试提出一种软件副本，以通过实现机器学习的基于遗传的方法来预测故障倾向。底层方法是从开源软件收集数据，其中数据将采用面向对象度量的形式。所述数据将用于创建预测故障的模型。这些技术被称为基于遗传的分类器系统或学习分类器系统。在本工作的后面，将详细介绍有关数据收集技术和逐步算法以获取结果的信息。最后可以得出结论，这些技术可用于在软件的面向对象的数据上建立预测模型，并且对于开发序列开始阶段附近的故障倾向性预测可能是有用的。任何软件（SDLC）。

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