Data-driven modelling and probabilistic analysis of interactive software usage
Data-driven modelling and probabilistic analysis of interactive software usage
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DOI：
10.1016/j.jlamp.2018.07.003
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发表时间：
2018-11
期刊：
影响因子：
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通讯作者：
Oana Andrei;Muffy Calder
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文献类型：
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作者：
Oana Andrei;Muffy Calder
本文回答了这个研究问题：鉴于用户的使用方式不同，甚至单个用户的使用方式不同，我们如何建模和理解用户与软件的必然交互方式。我们的第一个贡献是引入了两个新的概率混合模型，这两个模型是从记录的用户跟踪集推断出来的，其中包括观察状态和潜在状态。这些模型概括了使用的时间和随机方面、用户的异质和动态性质以及收集数据的时间间隔的时间方面(例如，一天、一个月等)。一个关键概念是活动模式，它封装了在一组记录的用户跟踪中共享的常见观察到的时间行为。每个活动模式是一个离散时间马尔可夫链，其中观测变量标记状态，潜在状态指定活动模式。第二个贡献是我们如何使用参数化的、概率的、时态的逻辑属性来推理活动模式中以及活动模式之间的假设行为。推断模型和假设属性的不同组合为理解软件用法提供了丰富的技术集。第三个贡献是通过从全世界数万用户使用的软件应用程序到用户踪迹的应用程序来演示模型和时态逻辑属性。
This paper answers the research question: how can we model and understand the ways in which usersactuallyinteract with software, given that usage styles vary from user to user, and even from use to use for an individual user. Our first contribution is to introduce two new probabilistic, admixture models, inferred from sets of logged user traces, which include observed and latent states. The models encapsulate the temporal and stochastic aspects of usage, the heterogeneous and dynamic nature of users, and the temporal aspects of the time interval over which the data was collected (e.g. one day, one month, etc.). A key concept isactivity patterns, which encapsulate common observed temporal behaviours shared across a set of logged user traces. Each activity pattern is a discrete-time Markov chain in which observed variables label the states; latent states specify the activity patterns. The second contribution is how we use parametrised, probabilistic, temporal logic properties to reason about hypothesised behaviours within an activity pattern, and between activity patterns. Different combinations of inferred model and hypothesised property afford a rich set of techniques for understanding software usage. The third contribution is a demonstration of the models and temporal logic properties by application to user traces from a software application that has been used by tens of thousands of users worldwide.