SHF: Small: Causal Foundations of Statistical Fault Localization

这项研究的目标是提高自动化技术的有效性，这些技术寻求定位软件中导致测试或操作使用期间发生的观察到的故障(故障)的故障，以便可以修复这些故障。这一目标很重要，因为正常运行的软件在商业、通信、国家安全、交通、科学和许多其他活动中至关重要。要实现预期的改进，需要使用最近开发的跨多个学科的方法，以便能够准确地估计各种治疗、暴露或干预(例如，医疗)对感兴趣的结果(例如，疾病)的因果影响，而不存在偏见。如果成功，拟议的研究将有可能帮助软件开发人员有效地定位和修复他们产品中的故障，从而防止经济损失、伤害甚至死亡等损害。这项研究还将有助于在软件工程界传播合理的因果推理方法。更具体地说，本研究将研究和改进因果统计故障定位(CSFL)的基础，包括因果模型的形式、因果状态的抽象和迭代的处理。将开发一种基于值的CSFL方法，该方法包括分析和分析程序变量的值，并将其与基于谓词的CSFL相结合，以便更准确地估计程序元素的失败影响。将探索一种新的CSFL方法，它使用多级统计模型来集成不同类型和粒度级别的执行数据，对于给定的程序版本和跨版本都是如此。Meta分析技术将被应用于CSFL获得的一组可疑性分数，以考虑分数分布的特点和预测单个分数可信度的因素。此外，还需要研究选择偏差问题在不同背景下如何影响SFL技术，以及如何减轻它。最后，这项研究将探索病例对照方法在软件故障很少的情况下提高SFL的成本效益的潜在价值。

The goal of this research is to improve the effectiveness of automated techniques that seek to locate the faults in software that caused observed failures (malfunctions) to occur during testing or operational use, so the faults can be repaired. This goal is important because properly functioning software is critical in business, communications, national security, transportation, science, and many other activities. The desired improvements are to be achieved by employing methodology that has been developed recently, across several disciplines, to enable the causal effects of various kinds of treatments, exposures, or interventions (e.g., medical treatments) upon outcomes of interest (e.g., diseases) to be estimated accurately and without bias. If successful, the proposed research has the potential to help software developers to efficiently localize and repair faults in their products, thereby preventing harms such as economic loss, injury, and even death. The research will also help to disseminate sound causal inference methodology in the software engineering community. More specifically, the research will investigate and improve the foundations of causal statistical fault localization (CSFL), including the form of causal models, the abstraction of causal states, and the handling of iteration. A value-based approach to CSFL will be developed, which involves profiling and analyzing the values of program variables, and this will be integrated with predicate-based CSFL, in order to more accurately estimate the failure-causing effects of program elements. A new approach to CSFL will be explored that employs multilevel statistical models to integrate execution data of different types and granularity levels, both for a given program version and across versions. Meta-analysis techniques will be applied to the set of suspiciousness scores obtained with CSFL, in order to take account of features of the score distribution and of factors that predict the credibility if individual scores.Also to be investigated is how the problem of selection bias affects SFL techniques in different settings and how it can be mitigated. Finally, the research will explore the potential value of case-control methodology for improving the cost-effectiveness of SFL in scenarios where software failures are infrequent.