MaterialX

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These are the

level criteria. You can also view the

level criteria.

Analysis 2/2 ●

Dynamic code analysis

Criterion [dynamic_analysis]
Met

Unmet

N/A

?

The project MUST apply at least one dynamic analysis tool to any proposed major production release of the software produced by the project before its release. ^{[dynamic_analysis]}
A dynamic analysis tool examines the software by executing it with specific inputs. For example, the project MAY use a fuzzing tool (e.g., American Fuzzy Lop) or a web application scanner (e.g., OWASP ZAP or w3af). In some cases the OSS-Fuzz project may be willing to apply fuzz testing to your project. For purposes of this criterion the dynamic analysis tool needs to vary the inputs in some way to look for various kinds of problems or be an automated test suite with at least 80% branch coverage. The Wikipedia page on dynamic analysis and the OWASP page on fuzzing identify some dynamic analysis tools. The analysis tool(s) MAY be focused on looking for security vulnerabilities, but this is not required.

The GitHub Actions workflow for MaterialX builds with Clang sanitizers and runs the unit test suite for each commit to the repository:

https://github.com/AcademySoftwareFoundation/MaterialX/blob/main/.github/workflows/main.yml

Criterion [dynamic_analysis_enable_assertions]
Met

Unmet

N/A

?

The project SHOULD include many run-time assertions in the software it produces and check those assertions during dynamic analysis. ^{[dynamic_analysis_enable_assertions]}
This criterion does not suggest enabling assertions during production; that is entirely up to the project and its users to decide. This criterion's focus is instead to improve fault detection during dynamic analysis before deployment. Enabling assertions in production use is completely different from enabling assertions during dynamic analysis (such as testing). In some cases enabling assertions in production use is extremely unwise (especially in high-integrity components). There are many arguments against enabling assertions in production, e.g., libraries should not crash callers, their presence may cause rejection by app stores, and/or activating an assertion in production may expose private data such as private keys. Beware that in many Linux distributions NDEBUG is not defined, so C/C++ assert() will by default be enabled for production in those environments. It may be important to use a different assertion mechanism or defining NDEBUG for production in those environments.

The GitHub Actions workflow for MaterialX builds with Clang dynamic analysis, and runs the full MaterialX test suite with these compilation settings:

https://github.com/AcademySoftwareFoundation/MaterialX/blob/main/.github/workflows/main.yml

This data is available under the Community Data License Agreement – Permissive, Version 2.0 (CDLA-Permissive-2.0). This means that a Data Recipient may share the Data, with or without modifications, so long as the Data Recipient makes available the text of this agreement with the shared Data. Please credit Jonathan Stone and the OpenSSF Best Practices badge contributors.

MaterialX

Basics 4/5 ●

Identification

Prerequisites

Project oversight

Other

Change Control 4/4 ●

Public version-controlled source repository

Quality 4/7 ●

Coding standards

Working build system

Automated test suite

Security 3/5 ●

Use basic good cryptographic practices

Secured delivery against man-in-the-middle (MITM) attacks

Other security issues

Analysis 2/2 ●

Dynamic code analysis