Amazon Technologies, Inc.

Michael William Whalen

Neha Rungta

John Byron Cook

Carsten Varming

Andrew Jude Gacek

Sean McLaughlin

Vaibhav Sharma

Serdar Tasiran

Matthias Schlaipfer

Samuel Bayless

Tongtong Xiang

Andrew Apicelli

Fernando Dias Pauer

Rajat Gupta

Murphy Berzish

Dhivashini Jaganathan

Ankush Das

Saswat Padhi

Raveesh Yadav

Roman Pruzhanskiy

Andrew Judge Gacek

Harshil Rajeshkumar Shah

System and methods for IoT event detector correctness verification. Detector models (e.g., state-based models including variables, states, transitions and actions) take IoT device data as input and detect, based on the data, events that triggers actions. To verify a correctness of the models prior to deploying the models at scale, an event detector model correctness checker obtains a representation of a definition of the model, verifies, based on analysis of the model definition, whether the model complies with correctness properties, and generates a report indicating whether the model complies. Example correctness properties include a reachability correctness property that indicates that respective states or actions are reachable according to the definition of the event detector model. The analysis may be accessed via an interface element and may result in generation of a report that identifies a location of non-compliance within the model definition.

IoT event detector correctness verification

A software development process may support a transition from unverifiable, legacy code to verifiable code that is provably correct by construction. A behavioral model may be developed for legacy software that includes various behavioral criteria. Then, source code implemented in a verifiable language may be verified using the behavioral model to perform verification. Once the source code is complete and verified, a new verified implementation may be compiled. The verified implementation may then be executed, along with the legacy software, to identify differences in behavior which are fed back into the behavioral model and subsequently into the new source code. This process may then be iterated with the verifiable code being deployable once behavioral differences are resolved.

Transitioning legacy software to be provably correct

A document anonymization system transforms structured documents, such as security policies, that contain user-specific and other sensitive data, producing encoded logic problems in the format or language of one or more constraint solvers; the logic problems do not contain any of the sensitive data. The system may perform a one- or two-stage anonymization process: in a first stage, the electronic document is analyzed according to its document type to identify parameters likely to contain sensitive data, and the associated values are replaced with arbitrary values; in a second stage, after the anonymized electronic document is converted into logic formulae representing the data, the system performs replacements of string constants in the logic formulae with arbitrary strings to further anonymize the sensitive data. The system may confirm that anonymization preserves the document structure, difficulty level, and satisfiability of the original document by executing the constraint solver against the anonymized logic problem.

Anonymizing sensitive data in logic problems for input to a constraint solver

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Michael William Whalen