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

SHF：小型：基于约束的程序分析的新领域

• 批准号: 1526270
• 负责人: Mayur Naik
• 金额: $ 45万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2017-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1526270&HistoricalAwards=false
• 关键词: SHF; Small; New; Frontiers; Constraint

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.

标题：SHF：小：基于约束程序分析的新边界基于基于计划的分析是一种流行的程序分析方法：它允许将分析规范与分析实现分开，它通过利用现成的求解器的进步来实现复杂的实现，并且它提供了自然程序规范作为约束。 该项目提出了多米诺骨牌，该框架通过启用程序分析的共同和新兴用例自动综合，从而扩展了基于约束分析的好处，例如找到良好的抽象，分析不完整的程序以及并入用户反馈。 该项目的智力优点是从根本上推进需求驱动，组成和基于学习的分析技术。通过一劳永逸的用例自动合成用例，多米诺骨牌放大了基于约束分析的传统好处，从而使分析设计师不得不重新实施这些用例进行分析。 该项目更广泛的意义和重要性在于通过使计划分析的适用性和实用性通过使它们更加自动化，可扩展性和灵活性来增强其适用性和实用性。 体现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。 Dominoes还将通过允许分析将分析调整为反馈来提高分析用户的生产率。Dominoes自动合成了在Datalog中表达的任何程序分析的用例实现，这是一种流行的声明性逻辑编程语言。 现有的基于约束的分析框架主要集中在解决硬性约束上，而多米诺骨牌还适应了在各种程序分析的不同用例中自然出现的软限制，例如，以建模各种权衡折衷，分析用户的直觉以及缺少程序规范。 通过将其应用于三个重要用例：客户端驱动分析，基于摘要的分析和用户指导分析来证明多米诺骨牌的多功能性。 尽管它们多样性，但这三个用例都需要解决最大满意度（MAXSAT）问题的解决方案，该实例包括硬（不可侵犯）约束和软（违规）约束的组合。 解决这种混合约束不仅在计算上很难，而且还提出了指定权重或软限制的信心的问题。 多米诺骨牌开发了包括需求驱动，组成和基于学习的方法的MaxSAT优化，这些方法是一般且独立于任何分析，用例或求解器，并旨在扩展到超出现有MaxSat求解器范围的实例。