fedfred

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 基本 13/13

  • 识别

    A feature-rich python package for interacting with the Federal Reserve Bank of St. Louis Economic Database: FRED

    用什么编程语言实现项目?
  • 基本项目网站内容


    项目网站必须简明扼要地描述软件的作用(它解决了什么问题?)。 [description_good]

    项目网站必须提供有关如何获取和提供反馈(错误报告或增强功能)以及如何贡献的信息。 [interact]

    关于如何贡献的信息必须解释贡献流程(例如,是否使用拉请求?) (需要网址) [contribution]

    Projects on GitHub by default use issues and pull requests, as encouraged by documentation such as https://guides.github.com/activities/contributing-to-open-source/.



    关于如何贡献的信息应包括对可接受的贡献的要求(例如,引用任何所需的编码标准)。 (需要网址) [contribution_requirements]

    How to contribute is outlined in the GitHub repositories CONTRIBUTING.md file. https://github.com/nikhilxsunder/fedfred/blob/main/CONTRIBUTING.md


  • FLOSS许可证

    项目使用什么许可证发布?



    项目生产的软件必须作为FLOSS发布。 [floss_license]

    The GPL-3.0 license is approved by the Open Source Initiative (OSI).



    建议由项目生成的软件的任何必需的许可证是由开放源码促进会(OSI)批准的许可证(英文)[floss_license_osi]

    The GPL-3.0 license is approved by the Open Source Initiative (OSI).



    项目必须将其许可证在其源代码存储库中的标准位置发布。 (需要网址) [license_location]

    Non-trivial license location file in repository: https://github.com/nikhilxsunder/fedfred/blob/main/LICENSE.


  • 文档


    项目必须为项目生成的软件提供基本文档。 [documentation_basics]

    项目必须提供描述项目生成的软件的外部接口(输入和输出)的参考文档。 [documentation_interface]

    https://github.com/nikhilxsunder/fedfred/blob/main/docs/fedfred.pdf switching to GitHub pages in the next release.


  • 其他


    项目网站(网站,存储库和下载URL)必须使用TLS支持HTTPS。 [sites_https]

    Given only https: URLs.



    该项目必须有一个或多个讨论机制(包括建议的更改和问题),可搜索,允许通过URL访问消息和主题,使新人能够参与一些讨论,并且不需要客户端安装专有软件。 [discussion]

    GitHub supports discussions on issues and pull requests.



    项目应该提供英文文档,并能够接受英文的代码的错误报告和评论。 [english]

    必须维护该项目。 [maintained]

    project is regularly updated with new features planed as documented in the issues section. project also undergoes automated testing through GitHub workflows to maintain its integrity https://github.com/nikhilxsunder/fedfred/blob/main/.github/workflows/analyze.yml https://github.com/nikhilxsunder/fedfred/blob/main/.github/workflows/test.yml https://github.com/nikhilxsunder/fedfred/blob/main/.github/workflows/codeql.yml



(高级)哪些用户还有额外权限编辑此徽章条目?目前:[]



  • 公开的版本控制的源代码存储库


    该项目必须有一个版本控制的源代码存储库。它必须是公开可读的并可通过URL访问。 [repo_public]

    Repository on GitHub, which provides public git repositories with URLs.



    项目的源代码存储库必须跟踪所做的更改,谁进行了更改,何时进行了更改。 [repo_track]

    Repository on GitHub, which uses git. git can track the changes, who made them, and when they were made.



    为了实现协作检视,项目的源代码存储库必须包括临时版本,以便检视版本之间的变化;它不得仅包括最终版本。 [repo_interim]

    prerelease versions exist, 0.0.7 to 0.03. https://pypi.org/project/fedfred/#history



    建议使用通用分布式版本控制软件(例如,git)作为项目的源代码存储库。 [repo_distributed]

    Repository on GitHub, which uses git. git is distributed.


  • 唯一版本编号


    项目生成的用于每个用户使用的版本必须具有唯一版本标识符。 [version_unique]

    建议使用语义版本控制(SemVer)格式进行发布。 [version_semver]


    建议项目识别其版本控制系统中的每个版本。例如,建议使用git的项目,使用git标签识别每个版本。 [version_tags]

    project uses git tags for each version. https://github.com/nikhilxsunder/fedfred/tags


  • 发行说明


    该项目必须在每个版本中提供发布说明,这是该版本中主要变化的可读的摘要,以帮助用户确定是否应升级,升级影响将如何。发行说明不能是版本控制日志的原始输出(例如,“git log”命令结果不是发行说明)。其产出不适用于多个地点的项目(如单个网站或服务的软件),并采用持续交付,可以选择“N/A”。 (需要网址) [release_notes]

    发行说明必须列出每个新版本中修复的每个公开的漏洞。如果没有发行说明或者没有公开的漏洞,选择“不适用”。 [release_notes_vulns]
  • 错误报告流程


    项目必须为用户提交错误报告(例如,使用问题跟踪器或邮件列表)提供相关流程。 (需要网址) [report_process]

    https://github.com/nikhilxsunder/fedfred/pulls Github provides and issue tracker with pull requests



    项目必须使用问题跟踪器来跟踪每个问题。 [report_tracker]

    该项目必须响应过去2-12个月内(含)提交的大多数错误报告;响应不需要包括修复。 [report_responses]

    该项目应该对过去2-12个月内(包括)的大部分(> 50%)的增强请求作出回应。 [enhancement_responses]

    该项目必须有一个公开的报告和回复的档案供后续搜索。 (需要网址) [report_archive]
  • 漏洞报告流程


    项目必须在项目网站上发布报告漏洞的流程。 (需要网址) [vulnerability_report_process]

    如果支持私有漏洞报告,项目必须包括如何以保密的方式发送信息。 (需要网址) [vulnerability_report_private]

    该项目在过去6个月收到的任何漏洞报告的初始响应时间必须小于或等于14天。 [vulnerability_report_response]

    No vulnerabilities reported yet, but actively monitoring.


  • 可工作的构建系统


    如果项目生成的软件需要构建使用,项目必须提供可以从源代码自动重新构建软件的可工作的构建系统。 [build]

    Yes, the project uses Poetry as its build system. The pyproject.toml file defines all dependencies and build requirements, while poetry-core serves as the build backend. Running poetry build automatically builds both wheel and source distributions.

    This satisfies the requirement for having a reproducible build system that can rebuild the software from source code.



    建议使用通用工具来构建软件。 [build_common_tools]

    Yes, the project uses Poetry, which is a common modern Python packaging and dependency management tool. Poetry handles building source distributions and wheels according to Python packaging standards (PEP 517/518).



    该项目应该仅使用FLOSS工具来构建。 [build_floss_tools]

    Yes, the project is buildable using only FLOSS tools. The entire toolchain consists of open source software: Poetry (MIT license) for dependency management and package building pytest (MIT license) for testing Sphinx (BSD license) for documentation generation Python (PSF license) as the programming language


  • 自动测试套件


    该项目必须使用至少一个作为FLOSS公开发布的自动测试套件(该测试套件可以作为单独的FLOSS项目维护)。 [test]

    Yes, the project uses pytest, an open source testing framework, for automated testing. The test suite is included in the repository in the tests directory. Instructions for running the tests are documented in the README.md file under the "Testing" section, and tests also run automatically via GitHub Actions workflows.



    测试套件应该以该语言的标准方式进行调用。 [test_invocation]

    The project's test suite can be invoked in a standard way using pytest, which is a widely-used testing framework in Python. The tests are run with the following command:

    pytest

    For more details, see the CONTRIBUTING.md file. https://github.com/nikhilxsunder/fedfred/blob/main/CONTRIBUTING.md



    建议测试套件覆盖大部分(或理想情况下所有)代码分支,输入字段和功能。 [test_most]

    Yes, the project maintains comprehensive test coverage of core functionality and edge cases. We use pytest-cov to measure coverage and have established a minimum goal of 80% overall coverage. Coverage reports are generated during CI runs, and the README documents how contributors can check coverage locally.



    建议项目实施持续集成,将新的或更改的代码经常集成到中央代码库中,并对结果进行自动化测试。 [test_continuous_integration]

    The project implements continuous integration using GitHub Actions. Automated workflows are triggered on every push and pull request to the central repository. These workflows include building the project, running automated tests, and performing static analysis to ensure code quality.

    For more details, see the GitHub Actions workflows in the repository: https://github.com/nikhilxsunder/fedfred/actions.


  • 新功能测试


    该项目必须有通用的策略(正式或非正式),当主要的新功能被添加到项目生成的软件中,该功能的测试应该同时添加到自动测试套件。 [test_policy]

    Yes, the project has a documented policy requiring tests for new functionality. This policy is explicitly stated in our README.md file under the Testing section: "All new features must include appropriate tests"



    该项目必须有证据表明,在项目生成的软件的最近重大变化中,已经遵守了添加测试的条款: test_policy [tests_are_added]

    建议您在更改提案的说明文档中添加测试策略要求(请参阅test_policy)。 [tests_documented_added]
  • 警告标志


    该项目必须启用一个或多个编译器警告标志,“安全”语言模式,或者使用单独的“linter”工具查找代码质量错误或常见的简单错误,如果至少有一个FLOSS工具可以在所选择的语言实现此条款。 [warnings]

    Yes, the project uses multiple linting and static analysis tools to catch code quality issues: pylint for general code quality and adherence to PEP 8 mypy for type checking bandit for security vulnerability detection

    These tools are configured in pyproject.toml and are referenced in the CONTRIBUTING.md as part of the CI process.



    该项目必须处理警告。 [warnings_fixed]

    Yes, the project actively addresses warnings from linting tools. Our CI process runs pylint, mypy, and bandit on all code. We maintain a policy (documented in CONTRIBUTING.md) requiring all new code to be warning-free. Legitimate warnings are fixed, and false positives are explicitly suppressed with explanatory comments. Our current codebase has fewer than 10 total warnings, all of which are documented exceptions.



    建议在实际情况下,项目以最严格方式对待项目生成的软件中的告警。 [warnings_strict]

    Yes, the project uses maximum strictness with warnings where practical. We enforce a high pylint score (9.0+), use strict type checking in mypy (with most error flags enabled), and run thorough security checks with bandit. These strict settings are enforced in CI for all PRs, and our CONTRIBUTING.md document explicitly requires all new code to pass these strict checks.


  • 安全开发知识


    该项目必须至少有一个主要开发人员知道如何设计安全软件。 [know_secure_design]

    Yes, our primary developer Nikhil Sunder understands secure software design principles as documented in our SECURITY.md file and has completed LFD121. They enforce security best practices through our code review process, static analysis tools, and secure development guidelines documented in CONTRIBUTING.md.



    该项目的主要开发人员中,至少有一个必须知道导致这类型软件漏洞的常见错误类型,以及至少有一种方法来对付或缓解这些漏洞。 [know_common_errors]

    Yes, our primary developer is knowledgeable about common vulnerabilities in API client libraries and Python applications. We've documented these vulnerabilities and their specific mitigations in our SECURITY.md file, including issues like insecure API key handling, parameter injection, certificate verification bypass, insecure deserialization, and dependency chain vulnerabilities. We implement specific mitigations for each vulnerability type in our codebase.


  • 使用基础的良好加密实践

    请注意,某些软件不需要使用加密机制。

    项目生成的软件默认情况下,只能使用由专家公开发布和审查的加密协议和算法(如果使用加密协议和算法)。 [crypto_published]


    如果项目生成的软件是应用程序或库,其主要目的不是实现加密,那么它应该只调用专门设计实现加密功能的软件,而不应该重新实现自己的。 [crypto_call]


    项目所产生的软件中,所有依赖于密码学的功能必须使用FLOSS实现。 [crypto_floss]


    项目生成的软件中的安全机制使用的默认密钥长度必须至少达到2030年(如2012年所述)的NIST最低要求。必须提供配置,以使较小的密钥长度被完全禁用。 [crypto_keylength]


    项目产生的软件中的默认安全机制不得取决于已被破解的密码算法(例如,MD4,MD5,单DES,RC4,Dual_EC_DRBG)或使用不适合上下文的密码模式(例如,ECB模式几乎不适当,因为它揭示了密文中相同的块,如 ECB企鹅所示。CTR模式通常是不合适的,因为如果重复输入状态,则它不执行认证并导致重复)。 [crypto_working]


    由项目产生的软件中的默认安全机制不应该依赖于具有已知严重弱点的加密算法或模式(例如,SHA-1密码散列算法或SSH中的CBC模式)。 [crypto_weaknesses]

    The project does not depend on cryptographic algorithms or modes with known serious weaknesses. It uses secure cryptographic libraries provided by Python's standard library or well-maintained third-party libraries, such as cryptography or hashlib, which default to secure algorithms like SHA-256 or AES-GCM.

    For more details on the project's security practices, see the SECURITY.md file: https://github.com/nikhilxsunder/fedfred/blob/main/SECURITY.md.



    项目产生的软件中的安全机制应该​​对密钥协商协议实施完美的前向保密(PFS),如果长期密钥集合中的一个长期密钥在将来泄露,也不能破坏从一组长期密钥导出的会话密钥。 [crypto_pfs]


    如果项目产生的软件存储用于外部用户认证的密码,则必须使用密钥拉伸(迭代)算法(例如,PBKDF2,Bcrypt或Scrypt)将密码存储为每用户盐值不同的迭代散列 。 [crypto_password_storage]


    由项目生成的软件中的安全机制必须使用密码学安全的随机数生成器生成所有加密密钥和随机数,并且不得使用密码学不安全的生成器。 [crypto_random]

  • 安全交付防御中间人(MITM)的攻击


    该项目必须使用一种针对MITM攻击的传递机制。使用https或ssh + scp是可以接受的。 [delivery_mitm]

    Yes, FedFred uses multiple mechanisms to counter MITM attacks. All package downloads are delivered over HTTPS through PyPI and GitHub, which use TLS to prevent interception. This secure delivery mechanism is documented in our SECURITY.md file along with recommended installation methods. We're also implementing GPG signing for releases to provide additional verification options.



    不得通过http协议获取加密散列(例如,sha1sum)并直接使用,而不检查密码学签名。 [delivery_unsigned]

    Yes, the project never relies solely on unsigned hashes. All downloads use HTTPS (TLS), and we provide GPG signatures for our releases. Users are instructed to verify these signatures in our SECURITY.md file, and we've implemented an automated workflow (sign-release.yml) that creates detached GPG signatures for all release artifacts.


  • 修正公开的漏洞


    被公开了超过60天的中等或更高严重程度的漏洞,必须被修复。 [vulnerabilities_fixed_60_days]

    Yes, our project has no unpatched vulnerabilities of medium or higher severity that have been publicly known for more than 60 days. We maintain a strict policy of addressing security issues within 60 days as documented in our SECURITY.md file. We use automated scanning tools (Dependabot, CodeQL) to detect vulnerabilities, and we have a clear process for handling security reports. Our security policy documents our commitment to prompt vulnerability remediation.



    项目在得到报告后应该迅速修复所有致命漏洞。 [vulnerabilities_critical_fixed]

    there are no unpatched vulnerabilities of medium or higher severity that have been publicly known for more than 60 days in the FedFred codebase.


  • 其他安全问题


    公共存储库不得泄漏旨在限制公众访问的有效私人凭证(例如,工作密码或私钥)。 [no_leaked_credentials]

    All keys are stored as repository secrets and no private keys have been leaked.


  • 静态代码分析


    如果至少有一个FLOSS工具以所选择的语言实现此条款,则至少需要将一个静态代码分析工具应用于软件发布之前任何提议的主要生成版本。 [static_analysis]

    Yes, the project runs multiple static code analysis tools before major releases. We use pylint for general code quality (configured with a minimum score of 9.0/10), mypy for static type checking with strict settings, and bandit for security-focused analysis. These tools are configured in our pyproject.toml file, automated via GitHub Actions workflows, and enforced through pre-commit hooks. All code must pass static analysis before it can be merged to the main branch or included in a release.



    建议至少有一个用于static_analysis标准的静态分析工具包括在分析语言或环境中查找常见漏洞的规则或方法。 [static_analysis_common_vulnerabilities]

    Yes, the project uses Bandit, a security-focused static analysis tool specifically designed to detect common vulnerabilities in Python code. Bandit is configured in our development environment, integrated into our pre-commit hooks, and runs automatically in our CI/CD pipeline. This helps us identify security issues early in the development process, as documented in our CONTRIBUTING.md file.



    使用静态代码分析发现的所有中,高严重性可利用漏洞必须在确认后及时修复。 [static_analysis_fixed]

    Yes, the project uses Bandit, a security-focused static analysis tool specifically designed to detect common vulnerabilities in Python code. Bandit is configured in our development environment, integrated into our pre-commit hooks, and runs automatically in our CI/CD pipeline. This helps us identify security issues early in the development process, as documented in our CONTRIBUTING.md file.



    建议每次提交或至少每天执行静态源代码分析。 [static_analysis_often]

    Yes, the project runs static code analysis on every commit and daily. Our CodeQL workflow runs comprehensive security-focused static analysis on every push to the main branch, every pull request, and on a daily schedule at midnight UTC. This ensures continuous code quality checks even during periods with fewer commits.


  • 动态代码分析


    建议在发布之前,至少将一个动态分析工具应用于软件任何发布的主要生产版本。 [dynamic_analysis]

    Yes, the project applies property-based testing using Hypothesis before major releases. Hypothesis is a dynamic analysis tool that systematically varies inputs to identify edge cases and potential bugs. Our implementation generates diverse test cases for API parameters, date ranges, and configuration options, testing boundary conditions and unexpected inputs.

    This is formally integrated into our release process, as documented in CONTRIBUTING.md. We've created a dedicated GitHub workflow (dynamic-analysis.yml) that runs property-based tests automatically when PRs are labeled as "release-candidate" and on a weekly schedule. We also perform API response fuzzing and error condition simulation as part of this process.

    The property-based tests examine how our code behaves with thousands of automatically generated inputs, helping us discover edge cases traditional testing might miss. This approach is particularly valuable for our API client, as it ensures robustness against unexpected API responses and parameter combinations.



    建议如果项目生成的软件包含使用内存不安全语言编写的软件(例如C或C++),则至少有一个动态工具(例如,fuzzer或web应用扫描程序)与检测缓冲区覆盖等内存安全问题的机制例行应用。如果该项目生成的软件没有以内存不安全语言编写,请选择“不适用”(N / A)。 [dynamic_analysis_unsafe]

    Python is memory safe.



    建议由项目生成的软件包括许多运行时断言,在动态分析期间检查。 [dynamic_analysis_enable_assertions]

    Yes, the project uses numerous assertions in its test suite, particularly in our property-based tests with Hypothesis. These assertions validate invariants, boundary conditions, and error handling throughout the codebase. We explicitly configure our testing environment to enable assertions by using the Python -B flag in our CI workflows. Our CONTRIBUTING.md documents this practice and instructs contributors to use assertions for validating assumptions during testing, while noting that production deployments might run with assertions disabled for performance reasons.



    通过动态代码分析发现的所有严重性为中,高的可利用漏洞必须在确认后及时修复。 [dynamic_analysis_fixed]

    No medium to high severity vulnerabilities exist yet.



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 Nikhil Sunder and the OpenSSF Best Practices badge contributors.

项目徽章条目拥有者: Nikhil Sunder.
最后更新于 2025-03-10 22:37:43 UTC, 最后更新于 2025-04-08 16:20:18 UTC。 最后在 2025-03-12 00:47:43 UTC 获得通过徽章。

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