The Effect of Programming Language in Software Bug Prediction

2022 3rd International Conference on Communication, Computing and Industry 4.0 (C2I4) Pub Date : 2022-12-15 DOI:10.1109/C2I456876.2022.10051493

Gopika G. Jayadev, Kaparthi Gayathri, T. Babu, Harika Gandiboina, Kavya Sudha, Bondu Venkteswalru

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Abstract

Software maintenance is a very important phase in the life cycle of software development. As part of maintenance, we need to identify bugs within the code and fix them for every release. Software bug prediction (SBP) allows us to identify modules in the software that may have the tendency to be buggy. This enables us to perform targeted testing and properly plan maintenance cycles. In most research performed, we observed that dataset of software programs written in C language were used. Each programming language are inherently different and has it's own constructs, practices and nuances. In this research we focus on identifying if there exists a specific classifier that works well for each programming language which in turn is used to analyze the effect of programming language on Software Bug Prediction. Datasets of software written in C, C++ and Java has been collected and the most accurate classifier for each dataset of a programming language has been identified. The ML models used in this paper include Naive Bayes, Decision Tree and Random Forest.

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编程语言在软件Bug预测中的作用

软件维护是软件开发生命周期中非常重要的一个阶段。作为维护的一部分，我们需要识别代码中的错误，并在每个版本中修复它们。软件bug预测(SBP)允许我们识别软件中可能有bug倾向的模块。这使我们能够执行有针对性的测试并适当地计划维护周期。在进行的大多数研究中，我们观察到使用C语言编写的软件程序数据集。每种编程语言本质上都是不同的，有自己的结构、实践和细微差别。在这项研究中，我们着重于确定是否存在一个特定的分类器，该分类器适用于每种编程语言，进而用于分析编程语言对软件Bug预测的影响。收集了用C、c++和Java编写的软件数据集，并确定了每种编程语言数据集的最准确分类器。本文使用的机器学习模型包括朴素贝叶斯、决策树和随机森林。

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 3rd International Conference on Communication, Computing and Industry 4.0 (C2I4)

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