Amazon Web Services (AWS) is a comprehensive and broadly adopted cloud provider. AWS SDKs provide access to AWS services through API endpoints. However, incorrect use of these APIs can lead to code defects, crashes, performance issues, and other problems. AWS best practices are a set of guidelines for correct and secure use of these APIs to access cloud services, allowing conformant clients to fully reap the benefits of cloud computing.
We present static analyses, developed in the context of a commercial service for detection of code defects and security vulnerabilities, to identify deviations from AWS best practices. We focus on applications that use the AWS SDK for Python, called Boto3. Precise static analysis of Python cloud applications requires robust type inference for inferring the types of cloud service clients. However, Boto3’s “Pythonic” APIs pose unique challenges for type resolution, as does the inter-procedural style in which service clients are used. We offer a layered approach that combines multiple type-resolution and tracking strategies in a staged manner: (i) general-purpose type inference augmented by type annotations, (ii) inter-procedural dataflow analysis expressed in a domain-specific language, and (iii) name-based resolution as a low-confidence fallback. Across >3,000 popular Python GitHub repos that make use of the AWS SDK, our layered type inference system achieves 85% precision and 100% recall in inferring Boto3 clients in Python client code.
Additionally, we use real-world developer feedback to assess a representative sample of eight AWS best-practice rules. These rules detect a wide range of issues including pagination, polling, and batch operations. Developers have accepted more than 85% of the recommendations made by five out of eight Python rules, and almost 83% of all recommendations.
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