Synthesizing data structure refinements from integrity constraints

Shankara Pailoor, Yuepeng Wang, Xinyu Wang, Işıl Dillig
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引用次数: 7

Abstract

Implementations of many data structures use several correlated fields to improve their performance; however, inconsistencies between these fields can be a source of serious program errors. To address this problem, we propose a new technique for automatically refining data structures from integrity constraints. In particular, consider a data structure D with fields F and methods M, as well as a new set of auxiliary fields F′ that should be added to D. Given this input and an integrity constraint Φ relating F and F′, our method automatically generates a refinement of D that satisfies the provided integrity constraint. Our method is based on a modular instantiation of the CEGIS paradigm and uses a novel inductive synthesizer that augments top-down search with three key ideas. First, it computes necessary preconditions of partial programs to dramatically prune its search space. Second, it augments the grammar with promising new productions by leveraging the computed preconditions. Third, it guides top-down search using a probabilistic context-free grammar obtained by statically analyzing the integrity checking function and the original code base. We evaluated our method on 25 data structures from popular Java projects and show that our method can successfully refine 23 of them. We also compare our method against two state-of-the-art synthesis tools and perform an ablation study to justify our design choices. Our evaluation shows that (1) our method is successful at refining many data structure implementations in the wild, (2) it advances the state-of-the-art in synthesis, and (3) our proposed ideas are crucial for making this technique practical.
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从完整性约束中综合数据结构改进
许多数据结构的实现使用多个相关字段来提高性能;但是,这些字段之间的不一致可能会导致严重的程序错误。为了解决这个问题,我们提出了一种从完整性约束中自动提炼数据结构的新技术。具体来说,考虑一个具有字段F和方法M的数据结构D,以及应该添加到D的一组新的辅助字段F '。给定此输入和与F和F '相关的完整性约束Φ,我们的方法会自动生成满足所提供的完整性约束的D的细化。我们的方法基于CEGIS范例的模块化实例化,并使用一种新的归纳合成器,该合成器通过三个关键思想增强了自上而下的搜索。首先,它计算部分程序的必要前提条件,以大幅减少其搜索空间。其次,它通过利用计算的先决条件,用有希望的新结果来增强语法。第三,它通过静态分析完整性检查函数和原始代码库获得的概率上下文无关语法来指导自顶向下搜索。我们在流行的Java项目中的25个数据结构上评估了我们的方法,并表明我们的方法可以成功地改进其中的23个。我们还将我们的方法与两种最先进的合成工具进行了比较,并进行了消融研究,以证明我们的设计选择是正确的。我们的评估表明:(1)我们的方法在精炼许多野外数据结构实现方面是成功的,(2)它在综合方面推进了最先进的技术,(3)我们提出的想法对于使该技术实用至关重要。
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