Facebook Infer

Facebook Infer
Name	Facebook Infer
Developer	Facebook
Released	2013
Programming language	OCaml, Java, C
Platform	Linux, macOS, Windows
License	MIT License

Facebook Infer Facebook Infer is a static analysis tool originally developed by engineers at Facebook to detect bugs and verify properties in large-scale mobile application and server software codebases. It combines techniques from academic research in formal verification, program analysis, and automated reasoning to provide fast, incremental analyses suitable for continuous integration in projects maintained by organizations such as Instagram and WhatsApp. Infer has been cited in work by researchers at institutions like Carnegie Mellon University, University of Oxford, and École Polytechnique Fédérale de Lausanne.

History

Infer's development began in the early 2010s at Facebook as part of efforts to improve code quality across products including Facebook Messenger, Instagram, and Oculus VR. Key contributors came from collaborations with academic groups at Carnegie Mellon University, Stanford University, and University of Cambridge. Public announcements and releases followed a series of research publications presented at venues such as PLDI, OOPSLA, CAV, and ICSE conferences. Over time, Infer's codebase and documentation were made available to external developers, and it was adopted by companies beyond Facebook including Mozilla Foundation, Microsoft, and startups in the Silicon Valley ecosystem.

Architecture and Design

Infer's architecture is organized around a modular frontend, intermediate representation, analysis engines, and reporting components. The frontend parsers handle sources produced by toolchains for Android and iOS development, mapping to an intermediate representation influenced by techniques from LLVM and research on symbolic execution. The core uses an automated theorem-proving style of analysis based on ideas from separation logic and abstract interpretation to reason about memory safety, nullability, and resource leaks. Build-system integration is achieved through adapters for systems like Buck, Gradle, and Bazel, enabling incremental analyses in continuous integration pipelines used by organizations such as Airbnb, Dropbox, and Spotify.

Static Analysis Techniques

Infer implements several static-analysis techniques: supply-driven bi-abduction based on separation logic for compositional reasoning about heap-manipulating programs; symbolic execution for path-sensitive checks derived from work at Microsoft Research and ETH Zurich; and interprocedural analyses influenced by research published at SOSP and ECOOP. The engine performs taint propagation checks similar to techniques from OWASP-style analyses and applies race detection influenced by academic tools like ThreadSanitizer and Helgrind. Infer's design emphasizes scalability through incremental reanalysis and summarization, drawing upon algorithms presented at FSE and SAS.

Supported Languages and Platforms

Infer provides analyzers and integration for languages and ecosystems commonly used at large technology companies. Primary language support includes Java (used in Android apps), Objective-C and Objective-C++ (used in iOS apps), and C and C++ (used in native components). Experimental and community-driven support has extended to Kotlin and interoperability with Swift through toolchain bridges. Platforms targeted include Android-based mobile devices, iOS-based devices, and server deployments on Linux distributions commonly used in data centers operated by companies like Amazon and Google.

Usage and Integration

Infer is designed for integration into development workflows and continuous integration services such as Jenkins, Travis CI, and CircleCI. Developers incorporate Infer into build steps using adapters for build tools like Gradle, Bazel, and Buck to perform on-demand analysis during pull-request validation and nightly builds. Teams at enterprises including Mozilla Foundation, Microsoft, and Walmart have reported integrating Infer as part of code-review processes to catch regressions similar to techniques advocated by DevOps practitioners and platform engineers at Netflix. Output is rendered as machine-readable reports consumable by static-dashboard systems and code-review bots.

Evaluation and Adoption

Infer's effectiveness has been evaluated in academic studies and industrial case reports comparing it against static analyzers like Coverity, Clang Static Analyzer, and commercial offerings from vendors such as Synopsys. Publications in venues including ICSE, PLDI, and CAV reported that Infer finds certain classes of bugs—null dereferences, resource leaks, and concurrency issues—at scale with low false-positive rates in projects including Facebook Messenger, Instagram, and open-source repositories hosted on GitHub. Adoption beyond Facebook has been documented at organizations like Uber Technologies, Airbnb, and Dropbox, with some universities using Infer in software-engineering coursework at institutions like Massachusetts Institute of Technology, University of California, Berkeley, and Princeton University.

Category:Static program analysis tools