High-level languages are growing in popularity. However, decades of C software development have produced large libraries of fast, timetested, meritorious code that are impractical to recreate from scratch. Cross-language bindings can expose low-level C code to high-level languages. Unfortunately, writing bindings by hand is tedious and error-prone, while mainstream binding generators require extensive manual annotation or fail to offer the language features that users of modern languages have come to expect.We present an improved binding-generation strategy based on static analysis of unannotated library source code. We characterize three high-level idioms that are not uniquely expressible in C's lowlevel type system: array parameters, resource managers, and multiple return values. We describe a suite of interprocedural analyses that recover this high-level information, and we show how the results can be used in a binding generator for the Python programming language. In experiments with four large C libraries, we find that our approach avoids the mistakes characteristic of hand-written bindings while offering a level of Python integration unmatched by prior automated approaches. Among the thousands of functions in the public interfaces of these libraries, roughly 40% exhibit the behaviors detected by our static analyses.

Android's popularity has given rise to myriad application analysis techniques to improve the security and robustness of mobile applications, motivated by the evolving adversarial landscape. These techniques have focused on identifying undesirable behaviors in individual applications, either due to malicious intent or programmer error. We present a collection of tools that provide a static information flow analysis across a set of applications, showing a holistic view of all the applications destined for a particular device. The techniques we present include a static binary single-app analysis, a security lint tool to mitigate the limits of static binary analysis, a multi-app information flow analysis, and an evaluation engine to detect information flows that violate specified security policies.We show that our single-app analysis is comparable with the leading approaches on the DroidBench benchmark suite; we present a brief listing of lint-like heuristics used to show the limits of the single-app analysis in the context of an application; we present a multi-app analysis, and demonstrate information flows that cannot be detected by single-app analyses; and we present a policy evaluation engine to automatically detect violations in collections of Android apps. General TermsSecurity

High-level languages are growing in popularity. However, decades of C software development have produced large libraries of fast, timetested, meritorious code that are impractical to recreate from scratch. Cross-language bindings can expose low-level C code to high-level languages. Unfortunately, writing bindings by hand is tedious and error-prone, while mainstream binding generators require extensive manual annotation or fail to offer the language features that users of modern languages have come to expect.We present an improved binding-generation strategy based on static analysis of unannotated library source code. We characterize three high-level idioms that are not uniquely expressible in C's lowlevel type system: array parameters, resource managers, and multiple return values. We describe a suite of interprocedural analyses that recover this high-level information, and we show how the results can be used in a binding generator for the Python programming language. In experiments with four large C libraries, we find that our approach avoids the mistakes characteristic of hand-written bindings while offering a level of Python integration unmatched by prior automated approaches. Among the thousands of functions in the public interfaces of these libraries, roughly 40% exhibit the behaviors detected by our static analyses.

Programs written in multiple languages are known as polyglot programs. In part due to the proliferation of new and productive high-level programming languages, these programs are becoming more common in environments that must interoperate with existing systems. Polyglot programs must manage resource lifetimes across language boundaries. Resource lifetime management bugs can lead to leaks and crashes, which are more difficult to debug in polyglot programs than monoglot programs.We present analyses to automatically infer the ownership semantics of C libraries. The results of these analyses can be used to generate bindings to C libraries that intelligently manage resources, to check the correctness of polyglot programs, and to document the interfaces of C libraries. While these analyses are unsound and incomplete, we demonstrate that they significantly reduce the manual annotation burden for a suite of fifteen open source libraries.

Programs written in multiple languages are known as polyglot programs. In part due to the proliferation of new and productive high-level programming languages, these programs are becoming more common in environments that must interoperate with existing systems. Polyglot programs must manage resource lifetimes across language boundaries. Resource lifetime management bugs can lead to leaks and crashes, which are more difficult to debug in polyglot programs than monoglot programs.We present analyses to automatically infer the ownership semantics of C libraries. The results of these analyses can be used to generate bindings to C libraries that intelligently manage resources, to check the correctness of polyglot programs, and to document the interfaces of C libraries. While these analyses are unsound and incomplete, we demonstrate that they significantly reduce the manual annotation burden for a suite of fifteen open source libraries.