论文标题
上下文感知特征模型中的异常检测
Anomaly detection in Context-aware Feature Models
论文作者
论文摘要
功能模型是一种组织配置空间的机制，并通过使用功能（即代表功能的名称）描述配置选项来促进软件变体的构建。功能模型的开发是一个易犯错活动，检测其异常是促进其用法所需的具有挑战性且重要的任务。 最近，已扩展了功能模型，以捕获配置选项与上下文影响和用户自定义的相关性。不幸的是，此扩展使检测异常的任务更加困难。在本文中，我们在上下文感知功能模型中对异常分析进行形式化，并展示了如何使用量化的布尔公式（QBF）求解器来检测异常情况，而无需依赖对SAT求解器的迭代调用。通过扩展重新配置机Hyvarrec，我们提出了发现的结果，表明QBF求解器可以优于异常分析的通用技术。
Feature Models are a mechanism to organize the configuration space and facilitate the construction of software variants by describing configuration options using features, i.e., a name representing a functionality. The development of Feature Models is an error prone activity and detecting their anomalies is a challenging and important task needed to promote their usage. Recently, Feature Models have been extended with context to capture the correlation of configuration options with contextual influences and user customizations. Unfortunately, this extension makes the task of detecting anomalies harder. In this paper, we formalize the anomaly analysis in Context-aware Feature Models and we show how Quantified Boolean Formula (QBF) solvers can be used to detect anomalies without relying on iterative calls to a SAT solver. By extending the reconfigurator engine HyVarRec, we present findings evidencing that QBF solvers can outperform the common techniques for anomaly analysis.
