A Dynamical Quality Model to Continuously Monitor Software Maintenance
A Dynamical Quality Model to Continuously Monitor Software Maintenance
复制标题
持续监控软件维护的动态质量模型
DOI：
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发表时间：
2017
期刊：
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通讯作者：
Gustavs Venters
中科院分区：
文献类型：
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作者：
Valentina Lenarduzzi;C. Stan;D. Taibi;D. Tosi;Gustavs Venters
。背景：一些公司，特别是中小型企业(SME)，由于缺乏软件质量保证(SQA)，经常面临软件维护问题。SQA是一项复杂的任务，需要大量的努力和专业知识，这在中小企业中往往是无法实现的。在研究论文中定义了几种质量保证模型，包括维修预测模型。然而，这些模型通常被定义为“一刀切”，并且主要针对能够负担得起承担数据解释任务的软件质量专家的大行业。目的：在这项工作中，我们提出了一种持续监控最终用户运行的软件的方法，自动收集问题并向开发人员推荐可能的修复程序。持续异常监控系统还将作为知识库，建议一套质量实践，以避免(重新)将错误引入代码。方法：首先，我们确定了一套适用于中小企业的SQA实践，基于这些实践的主要制约因素。然后，我们确定了一组预测技术，包括回归和机器学习，跟踪发布的软件引发的错误和异常。最后，我们为每家公司提供一个定制的SQA模型，该模型从公司的错误/问题历史记录中自动获取。然后，通过一组用于集成开发环境的插件为开发人员提供质量模型。这些建议了一组应该采取的SQA行动，以保持一定的质量水平，并允许以尽可能低的努力消除最严重的问题。结论：收集的测量结果将作为公共数据集提供，以便研究人员也可以受益于该项目的结果。这项工作是与当地中小企业和现有的开放源码项目和社区合作开发的。
. Context: several companies, particularly Small and Medium Sized Enterprises (SMEs), often face software maintenance issues due to the lack of Software Quality Assurance (SQA). SQA is a complex task that requires a lot of effort and expertise, often not available in SMEs. Several SQA models, including maintenance prediction models, have been defined in research papers. However, these models are commonly defined as “one-size-fits-all” and are mainly targeted at the big industry, which can afford software quality experts who undertake the data interpretation tasks. Objective: in this work, we propose an approach to continuously monitor the software operated by end users, automatically collecting issues and recommending possible fixes to developers. The continuous exception monitoring system will also serve as knowledge base to suggest a set of quality practices to avoid (re)introducing bugs into the code. Method: first, we identify a set of SQA practices applicable to SMEs, based on the main constraints of these. Then, we identify a set of prediction techniques, including regressions and machine learning, keeping track of bugs and exceptions raised by the released software. Finally, we provide each company with a tailored SQA model, automatically obtained from companies’ bug/issue history. Developers are then provided with the quality models through a set of plug-ins for integrated development environments. These suggest a set of SQA actions that should be undertaken, in order to maintain a certain quality level and allowing to remove the most severe issues with the lowest possible effort. Conclusion: The collected measures will be made available as public dataset, so that researchers can also benefit of the project’s results. This work is developed in collaboration with local SMEs and existing Open Source projects and communities.