  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 topic model commonly 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.
