析取抽象状态的语义导向聚类

Huisong Li, F. Berenger, B. E. Chang, Xavier Rival
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引用次数: 20

摘要

为了推断复杂的结构不变量,形状分析依赖于逻辑属性的表达族。许多这样的分析操作抽象内存状态,这些状态由描述原子块或摘要的基本谓词的分离连词组成。此外,他们使用抽象记忆状态的有限析取来解释不同的形状。出于可伸缩性的考虑,析取应该保持较小,尽管精确通常需要保持额外的case拆分。在这种情况下,决定何时以及如何合并案例分割并将其替换为摘要对于准确性和效率都至关重要。现有的技术使用一组语法规则,设计起来很繁琐,而且容易失败。本文设计了一种基于轮廓的抽象状态聚类的语义准则,该准则不仅适用于离合词的保守并集,而且适用于将记忆谓词的连接分离为归纳总结的弱化。我们的方法允许定义联合运算符和扩展运算符,目的是保留分析成功所需的案例分割。我们在MemCAD分析器中实现了这种方法,并在现有库中的实际C代码上进行了评估,包括处理双链表、红黑树和avl树的程序。
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Semantic-directed clumping of disjunctive abstract states
To infer complex structural invariants, shape analyses rely on expressive families of logical properties. Many such analyses manipulate abstract memory states that consist of separating conjunctions of basic predicates describing atomic blocks or summaries. Moreover, they use finite disjunctions of abstract memory states in order to account for dissimilar shapes. Disjunctions should be kept small for the sake of scalability, though precision often requires to keep additional case splits. In this context, deciding when and how to merge case splits and to replace them with summaries is critical both for the precision and for the efficiency. Existing techniques use sets of syntactic rules, which are tedious to design and prone to failure. In this paper, we design a semantic criterion to clump abstract states based on their silhouette which applies not only to the conservative union of disjuncts, but also to the weakening of separating conjunction of memory predicates into inductive summaries. Our approach allows to define union and widening operators that aim at preserving the case splits that are required for the analysis to succeed. We implement this approach in the MemCAD analyzer, and evaluate it on real-world C codes from existing libraries, including programs dealing with doubly linked lists, red-black trees and AVL-trees.
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