This study examines temporal patterns of software systems defects using the Autoregressive Integrated Moving Average (ARIMA) approach. Defect reports from ten software application projects are analyzed;five of these p...This study examines temporal patterns of software systems defects using the Autoregressive Integrated Moving Average (ARIMA) approach. Defect reports from ten software application projects are analyzed;five of these projects are open source and five are closed source from two software vendors. Across all sampled projects, the ARIMA time series modeling technique provides accurate estimates of reported defects during software maintenance, with organizationally dependent parameterization. In contrast to causal models that require extraction of source-code level metrics, this approach is based on readily available defect report data and is less computation intensive. This approach can be used to improve software maintenance and evolution resource allocation decisions and to identify outlier projects—that is, to provide evidence of unexpected defect reporting patterns that may indicate troubled projects.展开更多
The importance of software residual defects and some prediction residual defects models are introduced. The problem that is not easy adapted to a general model is discussed. The model of prediction residual defects ba...The importance of software residual defects and some prediction residual defects models are introduced. The problem that is not easy adapted to a general model is discussed. The model of prediction residual defects based on BBNs is proposed and the detailed processes of the approach are given.展开更多
为克服软件可信性评估过程中可信证据正面度量收集数据困难的问题,提出基于软件缺陷的可信证据度量模型(trustworthy evidence measurement model based on software defects,TEMMSD),利用软件系统中存在的缺陷类型、数目以及缺陷严重...为克服软件可信性评估过程中可信证据正面度量收集数据困难的问题,提出基于软件缺陷的可信证据度量模型(trustworthy evidence measurement model based on software defects,TEMMSD),利用软件系统中存在的缺陷类型、数目以及缺陷严重程度等因素,从侧面实现可信证据度量。通过软件开发者、第三方测评和用户反馈3个方面采集、获取软件生命周期中不同阶段存在的缺陷数据并对初始数据进行预处理,运用正交缺陷分类法对缺陷数据进行缺陷分析并可信归类,从主客观的角度确定缺陷类型的权重,实现软件系统的缺陷证据度量。对石油企业自然递减跟踪系统进行实例分析,分析结果表明,TEMMSD模型在软件可信证据度量中具有可行性和有效性。展开更多
文摘This study examines temporal patterns of software systems defects using the Autoregressive Integrated Moving Average (ARIMA) approach. Defect reports from ten software application projects are analyzed;five of these projects are open source and five are closed source from two software vendors. Across all sampled projects, the ARIMA time series modeling technique provides accurate estimates of reported defects during software maintenance, with organizationally dependent parameterization. In contrast to causal models that require extraction of source-code level metrics, this approach is based on readily available defect report data and is less computation intensive. This approach can be used to improve software maintenance and evolution resource allocation decisions and to identify outlier projects—that is, to provide evidence of unexpected defect reporting patterns that may indicate troubled projects.
基金The sustentation fund come fron China Academy of Engineering Physics 2003-421050504-12-02
文摘The importance of software residual defects and some prediction residual defects models are introduced. The problem that is not easy adapted to a general model is discussed. The model of prediction residual defects based on BBNs is proposed and the detailed processes of the approach are given.
文摘为克服软件可信性评估过程中可信证据正面度量收集数据困难的问题,提出基于软件缺陷的可信证据度量模型(trustworthy evidence measurement model based on software defects,TEMMSD),利用软件系统中存在的缺陷类型、数目以及缺陷严重程度等因素,从侧面实现可信证据度量。通过软件开发者、第三方测评和用户反馈3个方面采集、获取软件生命周期中不同阶段存在的缺陷数据并对初始数据进行预处理,运用正交缺陷分类法对缺陷数据进行缺陷分析并可信归类,从主客观的角度确定缺陷类型的权重,实现软件系统的缺陷证据度量。对石油企业自然递减跟踪系统进行实例分析,分析结果表明,TEMMSD模型在软件可信证据度量中具有可行性和有效性。
