CPA-SEL: Automated Quality Prediction: Exploiting Knowledge of the Business Case

提案编号：NSF提案0810879标题：自动质量评估：利用业务案例的知识i: Tim Menzies， Bojan cukii故障预测模型非常有用，因为它们允许软件项目经理指导将质量保证资源分配给最需要它们的工件。最近的研究结果表明，这一研究领域已经达到了极限。当所有的数据挖掘者不能做到这一点时，他们都会遇到性能天花板效应。以及更好地将软件质量度量与故障发生联系起来的附加信息。为了构建突破天花板效应的高质量预测器，必须在搜索空间中引入更多的拓扑。标准的机器学习算法缺乏软件项目特有的业务知识。要添加业务知识，建议调查哪些业务级别的项目关注点最有希望，以便它们可以为自动故障预测模型提供指导。人在环规则引出和维护环境将与自动规则推理相结合。预期这种方法将允许软件经理安全地关注于所选择的质量保证技术的应用，相信自动化的质量预测器将对实际存在质量问题的工件发出警报。

Proposal Number: NSF Proposal 0810879Title: Automatic Quality Assessment: Exploiting Knowledge of the Business CasePIs: Tim Menzies, Bojan CukicFault prediction models are very useful, as they allow software project managers to guide the allocation of quality assurance resources to artifacts which need them the most. Recent results now indicate that this research area has reached its limits. All data miners hit a performance ceiling effect when they cannot find additional information that better relates software quality measures with fault occurrence. To build better quality predictors that break through ceiling effects, more topology must be introduced into the search space. Standard machine learning algorithms lack the business knowledge which characterizes software projects. To add that business knowledge, it is proposed to investigate which business level project concerns will be the most promising so they can provide guidance for automated fault prediction models. Human-in-the-loop rule elicitation and maintenance environments will be combined with automated rule inference.It is expected that this approach will allow software managers to safely focus on the application of quality assurance techniques of choice, confident that automated quality predictors will raise alerts about the artifacts in which quality issues actually exist.