Investigating code fault proneness using program slicing

使用程序切片调查代码错误倾向

• 批准号: EP/E063039/1
• 负责人: Tracy Hall
• 金额: $ 9.52万
• 依托单位: Brunel University London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2008
• 资助国家: 英国
• 起止时间: 2008 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FE063039%2F1
• 关键词: Investigating; code; fault; proneness; using

This feasibility study explores the relationship between program slices and faults. The aim is to investigate whether the characteristics of program slices can be used to identify fault-prone code hotspots. Slicing metrics and dependence clusters are used to characterise the slices in a software component. The relationship between the characteristics of those slices in the component and the faults in that component are then analysed. Identifying fault-prone code is difficult and reliable predictors of fault-proneness are not widely reported in the literature. Program slicing is an established software engineering technique to support the detection and correction of known faults. Once a problem has emerged, slicing enables all statements that could have caused that problem to be identified and extracted. This extracted code makes the identification and removal of the fault much easier. We propose to investigate whether slicing could also be a good predictor of latent faults that have not yet caused a problem. The results of this study will show whether the use of program slicing can be extended as a reliable tool to predict fault-prone code. Our previous proof of concept study suggests that this investigation is viable and that slicing may offer valuable insights into fault-proneness.

本可行性研究探讨了程序切片和故障之间的关系。其目的是调查程序切片的特性是否可以用来识别容易出错的代码热点。切片度量和依赖性聚类被用于对软件组件中的切片进行分类。然后分析元件中这些切片的特征与该元件中的故障之间的关系。识别容易出错的代码是困难的，可靠的预测错误倾向的文献中没有广泛报道。程序切片是一种成熟的软件工程技术，用于支持已知故障的检测和纠正。一旦出现问题，切片就可以识别和提取可能导致该问题的所有语句。这个提取的代码使得故障的识别和排除更加容易。我们建议调查切片是否也可以是一个很好的预测潜在的故障，尚未造成的问题。本研究的结果将显示程序切片的使用是否可以扩展为预测易出错代码的可靠工具。我们以前的概念研究证明，这项调查是可行的，切片可能提供有价值的见解错误倾向。

项目成果

A Theoretical and Empirical Analysis of Three Slice-Based Metrics for Cohesion

• DOI: 10.1142/s0218194010004888
• 发表时间: 2010-08
• 期刊: Int. J. Softw. Eng. Knowl. Eng.
• 作者: S. Counsell;T. Hall;David Bowes

Researcher Bias: The Use of Machine Learning in Software Defect Prediction

• DOI: 10.1109/tse.2014.2322358
• 发表时间: 2014-06
• 期刊: IEEE Transactions on Software Engineering
• 影响因子: 7.4
• 作者: M. Shepperd;David Bowes;T. Hall

SLuRp

鲁尔普

• DOI: 10.1145/2372233.2372243
• 发表时间: 2012
• 作者: Bowes D

Some Code Smells Have a Significant but Small Effect on Faults

• DOI: 10.1145/2629648
• 发表时间: 2014-08-01
• 期刊: ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY
• 影响因子: 4.4
• 作者: Hall, Tracy;Zhang, Min;Sun, Yi
• 通讯作者: Sun, Yi