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Mining quantified temporal rules: Formalism, algorithms, and evaluation

机译：挖掘量化的时间规则：形式主义，算法和评估

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摘要

Libraries usually impose constraints on how clients should use them. Often these constraints are not well-documented. In this paper, we address the problem of recovering such constraints automatically, a problem referred to as specification mining. Given some client programs that use a given library, we identify constraints on the library usage that are (almost) satisfied by the given set of clients. The class of rules we target for mining combines simple binary temporal operators with state predicates (composed of equality constraints) and quantification. This is a simple yet expressive subclass of temporal properties (LTL formulae) that allows us to capture many common API usage rules. We focus on recovering rules from execution traces and apply classical data mining concepts to be robust against bugs (API usage rule violations) in clients. We present new algorithms for mining rules from execution traces. We show how a propositional rule mining algorithm can be generalized to treat quantification and state predicates in a unified way. Our approach enables the miner to be complete (i.e., mine all rules within the targeted class that are satisfied by the given traces) while avoiding an exponential blowup. We have implemented these algorithms and used them to mine API usage rules for several Windows APIs. Our experiments show the efficiency and effectiveness of our approach.
机译：图书馆通常会限制客户应如何使用它们。通常，这些约束条件没有得到充分证明。在本文中，我们解决了自动恢复此类约束的问题，即称为规范挖掘的问题。给定一些使用给定库的客户端程序，我们确定（几乎）给定客户端集满足的库使用约束。我们要挖掘的规则类别将简单的二进制时间运算符与状态谓词（由等式约束组成）和量化结合在一起。这是时间属性（LTL公式）的一个简单而富有表现力的子类，它使我们能够捕获许多常见的API使用规则。我们专注于从执行跟踪中恢复规则，并应用经典的数据挖掘概念来对付客户端中的错误（违反API使用规则）。我们提出了从执行跟踪中挖掘规则的新算法。我们展示了命题规则挖掘算法如何可以通用化以统一的方式处理量化和状态谓词。我们的方法使矿工得以完整（即挖掘目标类别中给定跟踪满足的所有规则），同时避免了指数爆炸。我们已经实现了这些算法，并使用它们来挖掘多个Windows API的API使用规则。我们的实验表明了我们方法的有效性和有效性。

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