Traces of user interactions with a software system, captured in production, are commonly used as an input source for user experience testing. In this paper, we present an alternative use, introducing a novel approach of modeling user interaction traces enriched with another type of data gathered in production—software fault reports consisting of software exceptions and stack traces. The model described in this paper aims to improve developers’ comprehension of the circumstances surrounding a specific software exception and can highlight specific user behaviors that lead to a high frequency of software faults. Modeling the combination of interaction traces and software crash reports to form an interpretable and useful model is challenging due to the complexity and variance in the combined data source. Therefore, we propose a probabilistic unsupervised learning approach, adapting the nested hierarchical Dirichlet process, which is a Bayesian non-parametric hierarchical topic model originally applied to natural language data. This model infers a tree of topics, each of whom describes a set of commonly co-occurring commands and exceptions. The topic tree can be interpreted hierarchically to aid in categorizing the numerous types of exceptions and interactions. We apply the proposed approach to large scale datasets collected from the ABB RobotStudio software application, and evaluate it both numerically and with a small survey of the RobotStudio developers.

中文翻译：

---

基于交互数据的软件异常分层使用上下文建模

在生产中捕获的用户与软件系统交互的痕迹通常用作用户体验测试的输入源。在本文中，我们提出了一种替代用途，介绍了一种对用户交互跟踪进行建模的新方法，该方法富含生产中收集的另一种类型的数据——由软件异常和堆栈跟踪组成的软件故障报告。本文中描述的模型旨在提高开发人员对特定软件异常周围情况的理解，并可以突出导致高频率软件故障的特定用户行为。由于组合数据源的复杂性和差异，对交互跟踪和软件崩溃报告的组合进行建模以形成可解释且有用的模型具有挑战性。所以，我们提出了一种概率无监督学习方法，采用嵌套分层狄利克雷过程，这是一种贝叶斯非参数分层主题模型，最初应用于自然语言数据。该模型推断出一棵主题树，每个主题树描述了一组常见的同时出现的命令和异常。可以分层解释主题树以帮助对众多类型的异常和交互进行分类。我们将建议的方法应用于从 ABB RobotStudio 软件应用程序收集的大规模数据集，并通过数值和对 RobotStudio 开发人员的小型调查对其进行评估。该模型推断出一棵主题树，每个主题树描述了一组常见的同时出现的命令和异常。可以分层解释主题树以帮助对众多类型的异常和交互进行分类。我们将提议的方法应用于从 ABB RobotStudio 软件应用程序收集的大规模数据集，并通过数值和对 RobotStudio 开发人员的小型调查对其进行评估。该模型推断出一棵主题树，每个主题树描述了一组常见的同时出现的命令和异常。可以分层解释主题树以帮助对众多类型的异常和交互进行分类。我们将提议的方法应用于从 ABB RobotStudio 软件应用程序收集的大规模数据集，并通过数值和对 RobotStudio 开发人员的小型调查对其进行评估。