SENSitivE data: Sensitivity Analysis for Software Engineering Data

这笔短暂的旅行补助金是为了让谢泼德教授能够参观麦克唐纳教授在奥克兰的研究实验室。近年来，将软件工程重新定位为循证实践的举措很受欢迎。然而，基础的实证研究依赖于他们的数据集的质量，然而这是一个令人惊讶的被忽视的话题。我们在访问期间的目的是找到一种方法来证明数据驱动的软件工程研究结果的可靠性或不受噪声影响。我们提出了一种新的方法来评估经验研究结果的噪声脆弱性，基于敏感性分析和阶梯技术来确定已发表的经验研究的临界点。如果成功，这将为软件工程研究社区提供一个通用的方法来评估数据质量对他们的研究结论的潜在影响。这项研究非常重要，因为它提出了一个解决问题的问题的解决方案，我们的研究是如何不知道我们的研究是多么容易受到潜在的问题数据集。鉴于这项研究宣称的目的是影响实践，这将产生相当大的社会影响。

This short travel grant is to enable Prof Shepperd to visit Prof MacDonell's research labs in Auckland.In recent years there has been a welcome move to realign software engineering as an evidence-based practice. However, the underlying empirical studies are dependent upon the quality of their data sets, yet this is a surprisingly neglected topic. Our aim during the visit is to find a means to demonstrate the reliability, or freedom from noise-effects, of data-driven software engineering research results.We propose a new method for assessing the vulnerability to noise of an empirical research result, based on sensitivity analysis and a laddering technique, to determine the tipping point of published empirical studies.If successful, this will provide the software engineering research community with a general purpose method of assessing the potential impact of data quality upon their study conclusions.This research is extremely important as it proposes a solution to the problem of not knowing how vulnerable our research is to potentially problematic data sets. Given the stated aim of this research is to influence practice, this will have considerable societal impact.