SHF: Small: New Frontiers in Constraint-Based Program Analysis

标题：SHF：小：基于约束的程序分析的新领域基于约束的分析是一种流行的程序分析方法：它允许将分析规范从分析实现中分离出来，它通过利用现成的求解器来实现复杂的实现，并且它提供自然的程序规范作为约束。这个项目提出了多米诺骨牌，这是一个框架，通过支持自动合成常见的和新出现的程序分析用例，例如找到好的抽象、分析不完整的程序和合并用户反馈，扩展了基于约束的分析的好处。这个项目的智力价值在于从根本上推进了需求驱动的、组合的和基于学习的分析技术。通过一劳永逸地自动合成用例，domino放大了基于约束的分析的传统好处，使分析设计人员不必为他们的分析重新实现那些用例。项目更广泛的意义和重要性在于通过使程序分析更加自动化、可伸缩和灵活来增强它们的适用性和有用性。包含这些分析的工件将在可靠性、安全性、性能和能源效率方面提高软件质量。dominos还允许分析用户根据他们的反馈调整分析，从而提高分析用户的工作效率。dominos自动合成用Datalog（一种流行的声明性逻辑编程语言）表示的任何程序分析的用例实现。现有的基于约束的分析框架主要关注于解决硬约束，而多米诺骨牌也适应在程序分析的不同用例中自然产生的软约束，例如，对各种权衡、分析用户的直觉和缺失的程序规范进行建模。domino的多功能性通过将其应用于三个重要的用例来证明：客户驱动的分析、基于摘要的分析和用户引导的分析。尽管它们的多样性，所有三个用例都需要解决最大可满足性（MaxSAT）问题的实例，该问题由硬（不可违背）约束和软（不可违背）约束的组合组成。求解这种混合约束不仅计算困难，而且还提出了确定软约束的权重或置信度的问题。dominos开发了MaxSAT优化，包括需求驱动、组合和基于学习的方法，这些方法是通用的，独立于任何分析、用例或求解器，旨在扩展到远远超出现有MaxSAT求解器范围的实例。

Title: SHF:Small:New Frontiers in Constraint-Based Program AnalysisConstraint-based analysis is a popular approach to program analysis: it allows to separate analysis specification from analysis implementation, it enables sophisticated implementations by leveraging advances in off-the-shelf solvers, and it provides natural program specifications as constraints. This project proposes Dominoes, a framework that extends the benefits of constraint-based analysis by enabling automatic synthesis of common and emerging use-cases of program analyses, such as finding good abstractions, analyzing incomplete programs, and incorporating user feedback. The intellectual merit of this project is to fundamentally advance demand-driven, compositional, and learning-based analysis techniques. By automatically synthesizing use-cases once and for all, Dominoes amplifies the traditional benefits of constraint-based analysis, liberating analysis designers from having to re-implement those use-cases for their analyses. The project's broader significance and importance lies in enhancing the applicability and usefulness of program analyses by making them more automated, scalable, and flexible. Artifacts embodying these analyses will improve software quality in aspects of reliability, security, performance, and energy efficiency. Dominoes will also improve the productivity of analysis users by allowing them to adapt analyses to their feedback.Dominoes automatically synthesizes implementations of use-cases for any program analysis expressed in Datalog, a popular declarative logic programming language. Existing constraint-based analysis frameworks predominantly focus on solving hard constraints, whereas Dominoes also accommodates soft constraints that arise naturally in diverse use-cases of program analysis, e.g., to model various tradeoffs, intuitions of analysis users, and missing program specifications. The versatility of Dominoes is demonstrated by applying it to three important use-cases: client-driven analysis, summary-based analysis, and user-guided analysis. Despite their diversity, all three use-cases entail solving instances of the maximum satisfiability (MaxSAT) problem, which consists of a combination of hard (inviolable) constraints and soft (violable) constraints. Solving such mixed constraints is not only computationally hard but also poses the problem of specifying weights or confidences of soft constraints. Dominoes develops MaxSAT optimizations comprising demand-driven, compositional, and learning-based methods that are general and independent of any analysis, use-case, or solver, and aim to scale to instances well beyond the reach of existing MaxSAT solvers.