DC-SRGM: Deep Cross-Project Software Reliability Growth Model

2019 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW) Pub Date : 2019-10-01 DOI:10.1109/ISSREW.2019.00044

Kyawt Kyawt San, H. Washizaki, Y. Fukazawa, Kiyoshi Honda, Masahiro Taga, Akira Matsuzaki

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引用次数: 2

Abstract

Previous studies have suggested that software reliability growth models (SRGMs) for cross-project predictions are more practical for ongoing development projects. Several software reliability growth models (SRGMs) have been proposed based on various factors to measure the reliability and are helpful to indicate the number of remaining defects before release. Software industries want to predict the number of bugs and monitor the situation of projects for new or ongoing development projects. However, the available data is limited for projects in the initial development phases. In this situation, applying SRGMs may incorrectly predict the future number of bugs. This paper proposes a new SRGM method using the features of previous projects to predict the number of bugs for ongoing development projects. Through a case study, we identify similar projects for a target project by k-means clustering and form new training datasets. The Recurrent Neural Network based deep long short-term memory model is built over the obtained new dataset for prediction model. According to experiment results, the prediction by the proposed deep cross-project (DC) SRGM performs better than traditional SRGMs and deep SRGMs for ongoing projects.

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深度跨项目软件可靠性增长模型

以前的研究表明，跨项目预测的软件可靠性增长模型(SRGMs)对于正在进行的开发项目更实用。人们提出了几种基于各种因素的软件可靠性增长模型(SRGMs)来度量可靠性，并有助于在发布前指出剩余缺陷的数量。软件行业希望预测bug的数量，并监控新的或正在进行的开发项目的项目情况。然而，对于处于初始开发阶段的项目，可用的数据是有限的。在这种情况下，应用srgm可能会错误地预测未来的bug数量。本文提出了一种新的SRGM方法，利用以前项目的特征来预测正在进行的开发项目的bug数量。通过案例研究，我们通过k-means聚类识别目标项目的相似项目，并形成新的训练数据集。在得到的新数据集上，建立了基于递归神经网络的深度长短期记忆模型。实验结果表明，深度跨项目(DC) SRGM的预测效果优于传统SRGM和正在进行的项目深度SRGM。

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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2019 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)

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