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提高软件开发中的错误分配:使用模糊逻辑和进化算法的多标准方法

Chetna Gupta¹, Varun Gupta^2,3

¹Jaypee Institute of Information Technology, Noida, India.

PeerJ. Computer science

|July 10, 2024

概括

本研究介绍了一种新的算法,通过评估错误报告和开发人员能力来自动化错误管理. 该方法显著提高了错误选准确度和开发人员工作负载管理.

科学领域:

软件工程软件工程软件工程
人工智能的人工智能
数据挖掘数据挖掘

背景情况:

错误追踪系统 (BTS) 对于软件开发至关重要,但通常会受到主观和杂的错误报告的影响.
传统的错误管理依赖于直觉,导致错误的优先级和分配效率低下.
缺少针对bug属性的正式框架,如严重程度和优先级,使数据驱动的决策更加复杂.

研究的目的:

为自动化错误管理提出一种混合,多标准模糊的,多目标的进化算法.
解决关于错误报告和开发人员工作负担的多标准决策中的权衡问题.
为了提高错误选的准确性,区分开发人员的活动,并评估开发人员的可用性.

主要方法:

开发了一种混合方法,结合了模糊逻辑和多目标进化算法.
根据专业知识,性能和可用性创建了开发人员能力评分的指标.
建立了相对错误重要性得分的指标.
收集了关于错误报告,开发人员工作量和错误优先级的明确知识.

主要成果:

在五个开源项目的实验中,与现有方法相比,实现了大约20%的改进.
获得了准确度 (92.05%),回忆 (89.04%),f测量 (90.05%),准确度 (91.25%) 的和平均值.
在不同的开发人员和错误数量下,以最低的成本有效地最大化了错误吞吐量.

结论:

拟议的自动化错误管理方法显著提高了分拣准确性.
该系统有效地区分了活跃和不活跃的开发人员.
开发人员的可用性根据当前的工作负载准确地确定,优化资源配置.

关键词:

错误追踪系统错误追踪系统进化算法是一种进化算法.软件开发软件开发

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