JavaScript performance is often bound by its dynamically typed nature. Compilers do not have access to static type information, making generation of efficient, type-specialized machine code difficult. We seek to solve this problem by inferring types. In this paper we present a hybrid type inference algorithm for JavaScript based on points-to analysis. Our algorithm is fast, in that it pays for itself in the optimizations it enables. Our algorithm is also precise, generating information that closely reflects the program's actual behavior even when analyzing polymorphic code, by augmenting static analysis with run-time type barriers.

We describe techniques for analyzing data polymorphism in C, and show that understanding data polymorphism is important for statically verifying type casts in the Linux kernel, where our techniques prove the safety of 75% of downcasts to structure types, out of a population of 28767. We also discuss prevalent patterns of data polymorphism in Linux, including code patterns we can handle and those we cannot.

We present a unified approach to type checking and property checking for low-level code. Type checking for low-level code is challenging because type safety often depends on complex, program-specific invariants that are difficult for traditional type checkers to express. Conversely, property checking for low-level code is challenging because it is difficult to write concise specifications that distinguish between locations in an untyped program's heap. We address both problems simultaneously by implementing a type checker for low-level code as part of our property checker.

We present an overview of the Saturn program analysis system, including a rationale for three major design decisions: the use of function-at-a-time, or summary-based, analysis, the use of constraints, and the use of a logic programming language to express program analysis algorithms. We argue that the combination of summaries and constraints allows Saturn to achieve both great scalability and great precision, while the use of a logic programming language with constraints allows for succinct, high-level expression of program analyses.

We describe an ongoing project, the deployment of a modular checker to statically find and prevent every buffer overflow in future versions of a Microsoft product. Lightweight annotations specify requirements for safely using each buffer, and functions are checked individually to ensure they obey these requirements and do not overflow. Our focus is on the incremental deployment of this technology: by layering the annotation language, using aggressive inference techniques, and slicing warnings by checker confidence, teams must pay only part of the cost of annotating a program to achieve part of the benefit, which provides incentive for further annotation. To date over 400,000 annotations have been added to specify buffer usage in the source code for this product, of which over 150,000 were automatically inferred, and over 3,000 potential buffer overflows have been found and fixed.

We present a study of all sources of aliasing in over one million lines of C code, identifying in the process the common patterns of aliasing that arise in practice. We find that aliasing has a great deal of structure in real programs and that just nine programming idioms account for nearly all aliasing in our study. Our study requires an automatic alias analysis that both scales to large systems and has a low false positive rate. To this end, we also present a new context-, flow-, and partially path-sensitive alias analysis that, together with a new technique for object naming, achieves a false aliasing rate of 26.2%on our benchmarks.

This paper proposes a novel approach to shape analysis: using local reasoning about individual heap locations instead of global reasoning about entire heap abstractions. We present an inter-procedural shape analysis algorithm for languages with destructive updates. The key feature is a novel memory abstraction that differs from traditional abstractions in two ways. First, we build the shape abstraction and analysis on top of a pointer analysis. Second, we decompose the shape abstraction into a set of independent configurations, each of which characterizes one single heap location. Our approach: 1) leads to simpler algorithm specifications, because of local reasoning about the single location; 2) leads to efficient algorithms, because of the smaller granularity of the abstraction; and 3) makes it easier to develop context-sensitive, demand-driven, and incremental shape analyses.We also show that the analysis can be used to enable the static detection of memory errors in programs with explicit deallocation. We have built a prototype tool that detects memory leaks and accesses through dangling pointers in C programs. The experiments indicate that the analysis is sufficiently precise to detect errors with low false positive rates; and is sufficiently lightweight to scale to larger programs. For a set of three popular C programs, the tool has analyzed about 70K lines of code in less than 2 minutes and has produced 97 warnings, 38 of which were actual errors.