Software Bug Prediction Model Based on Mathematical Graph Features Metrics

2022 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW) Pub Date : 2022-04-01 DOI:10.1109/ICSTW55395.2022.00047

Tomohiro Takeda, Satoshi Masuda, K. Tsuda

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Abstract

Quality assurance is one of the most important activities in software development and maintenance. Software source codes are modified via change requests, functional improvement, and refactoring. When software changes, it is difficult to define the scope of test cases, and software testing costs tend to increase to maintain software quality. Therefore, change analysis is a challenge, and static testing is a key solution to this challenge. In this study, we propose new static testing metrics using mathematical graph analysis techniques for the control flow graph generated from the three-address code of the implementation codes based on our hypothesis of the existing correlation between the graph features and any software bugs. Five graph features are strongly correlated with the software bugs. Hence, our bug prediction model exhibits a better performance of 0.25 FN, 0.04 TN ratio, and 0.08 precision than a model based on the traditional bug prediction metrics, which are complexity, line of code (steps), and CRUD.

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基于数学图特征度量的软件Bug预测模型

质量保证是软件开发和维护中最重要的活动之一。通过变更请求、功能改进和重构来修改软件源代码。当软件变更时，很难定义测试用例的范围，并且软件测试成本倾向于增加以保持软件质量。因此，变更分析是一个挑战，而静态测试是这个挑战的关键解决方案。在本研究中，我们基于图形特征与任何软件缺陷之间存在相关性的假设，对由实现代码的三地址码生成的控制流图，使用数学图分析技术提出了新的静态测试度量。五个图形特征与软件缺陷密切相关。因此，我们的bug预测模型比基于传统的bug预测指标(复杂度、代码行数(步骤)和CRUD)的模型表现出更好的性能，分别为0.25 FN、0.04 TN比率和0.08精度。

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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2022 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW)

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