Research on Efficient Software Defect Prediction Using Deep Learning Approaches

Razauddin, Sindhu Madhuri G, Ashish Oberoi, Aman Vats, A. Sivasangari, Kuldeep Siwach
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Abstract

Software Defect prediction results provide a list of source code artifacts that are prone to defects. Quality assurance teams can effectively devote more energy and allocate limited resources to defect-prone source code verification software products. A module that identifies defect prediction methods for frequent defects before the start of the testing phase. Measurement-based defect-prone modules improve software quality and reduce costs, leading to effective resource allocation. The previous method doesn't analyze the defect pattern, and it has less performance during software development. This work introduces a deep learning-based Pattern-based Modified Hidden Markova Fault Tree (PMHMFT) framework to extract the hidden fault analysis during cross-project validation. The proposed Modified Hidden Markova Fault Tree algorithm constructs the defect fault tree to analyze the cross-project code defect. To prevent defect based on software metrics software prediction model are used. Hidden Markova Fault tree-based classification categorize component as defective and non-defective. Using a Levy flight, optimize the method to search the fault classes efficiently compared to another method. The Markova Fault Tree model construct fault tree based given data; it is easy to identify the fault in software platform. The proposed PMHMFT to implement evaluate the performance using k-fold validation. Thus, the proposed work on software defect prediction achieves higher accuracy in true classification and prediction with less error rate. The software defects are predicted, and these predicted defects are optimized by using Levy flight optimization. Our proposed PMHMFT technique is very useful technique for predicting software defect and gives the better prediction rates in effective manner.
基于深度学习方法的高效软件缺陷预测研究
软件缺陷预测结果提供了一个容易出现缺陷的源代码工件列表。质量保证团队可以有效地将更多的精力和有限的资源分配给容易出现缺陷的源代码验证软件产品。一个模块,用于在测试阶段开始之前识别常见缺陷的缺陷预测方法。基于度量的容易出现缺陷的模块提高了软件质量并降低了成本,从而导致了有效的资源分配。以前的方法没有对缺陷模式进行分析,在软件开发过程中性能较差。本文引入了一种基于深度学习的基于模式的修正隐马尔可夫故障树(PMHMFT)框架来提取跨项目验证过程中的隐故障分析。提出的改进隐马尔可娃故障树算法构建缺陷故障树来分析跨项目代码缺陷。为了防止基于软件度量的缺陷,采用了软件预测模型。基于隐马尔可夫故障树的分类将部件分为缺陷和非缺陷。利用Levy飞行,对该方法进行了优化,与其他方法相比,可以有效地搜索故障类别。马尔可娃故障树模型基于给定数据构造故障树;在软件平台上容易识别故障。提出的PMHMFT实现使用k-fold验证来评估性能。因此,本文提出的软件缺陷预测方法在真实分类和预测方面具有较高的准确性,错误率较低。对软件缺陷进行预测,并利用Levy飞行优化对预测缺陷进行优化。本文提出的PMHMFT技术是一种非常有用的软件缺陷预测技术,能够有效地提高软件缺陷的预测率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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