Data analytics for "cost of quality" of code artefacts.

随着软件系统规模和复杂性的不断增大， 提出了适应高要求，不仅正确性，而且性能，安全性，可靠性 和其他质量标准。随着人才的短缺，投资开发人员的技能，使他们能够 识别和修复他们自己的编码缺陷被认为是有价值的。这一过程越容易， 部分自动化，CodeExellence的投资回报率越高。预期的成本节约基于 (i)缩短上市时间，同时提高客户满意度，（ii）修复代码中的关键缺陷，同时 仍在开发中，在最具成本和时间效益的情况下修复它们，（iii）确保良好的开发 这些实践产生了可以重用的组件和框架，从而减少了返工， 交付时间，以及（iv）通过自动化和持续的 结构质量改善。 ENGAGE项目旨在利用人工智能，数据分析，发布 工程和软件工程决策支持，以便实现控制和诊断的自动化 软件中的过程。实现这一合作项目目标的途径是分析、设计和 评估现有存储库中可用的大数据。作为诊断分析的一种形式，其目的是 提取数据中固有的趋势和模式。这些趋势旨在指导和自动化 关于“应该修复哪些错误？“更确切地说，研究将集中在（一）方法 提取模式，（ii）表征模式，（iii）评估这些模式的适用性和有用性 模式.

With the increasing size and complexity of software systems, more and more adaptive ways have been proposed to accommodate the high demands not only for correctness, but also performance, security, reliability and other quality criteria. With the shortage of personal, investing into developers' skills by enabling them to identify and fix their own coding flaws is considered valuable. The more this process can be facilitated and partially automated, the higher its return of investment for CodeExellence. Expected cost savings are based on (i) improved time to market, while improving customer satisfaction, (ii) fixing critical defects in code while it is still in development, when it is most cost and time effective to fix them, (iii) ensures good development practices that result in components and frameworks that can be reused, thereby reducing rework and improving delivery times, and (iv) drives application maintenance costs down through automated and continuous structural quality improvement. The ENGAGE project is intended to leverage expertise in artificial intelligence, data analytics, release engineering and software engineering decision support for the sake of automating the control and diagnostic process in software. The pathway to achieve this collaboration project objective is to analyze, design and evaluate big data available from existing repositories. As a form of diagnostic analytics, the intention is to extract trends and patterns inherent in the data. These trends are planned to guide and automate the decision-making process on "Which bugs should be fixed?" More precisely, the research will be on (i) Methods to extract patterns, (ii) Characterize patterns, and (iii) Evaluate the applicability and usefulness of these patterns.