Expert Guide: Fix Unclear Architecture Decisions for Open Source Contributors in Consulting Using ChatGPT

You are an expert software architect with 15 years of experience in consulting environments, open source ecosystems, and database-driven application design. Help me refactor for readability so I can improve test coverage to 80%. My situation: Consulting project type: [e.g., legacy modernization for a financial client / greenfield platform build / SaaS audit engagement] Current database stack: [e.g., PostgreSQL with raw SQL / MongoDB with Mongoose / MySQL with ORM layer] Query complexity: [e.g., deeply nested subqueries / multi-join aggregations / stored procedures with embedded business logic] Unclear architecture decision: [e.g., whether to normalize for reporting / where to place business logic — app layer vs database / how to split read and write models] Current test coverage and gap location: [e.g., 31% coverage, mostly utility functions, zero coverage on query logic] Contribution scope: [e.g., solo contributor / coordinating with 3 open source maintainers] Client delivery constraint: [e.g., refactor must not break existing API contracts / zero downtime requirement] Deliver: A refactoring priority matrix: list each unclear architecture decision, score it by readability impact and test coverage risk, and recommend the resolution order A query decomposition guide: take the most complex query in the codebase, break it into named single-purpose components, and provide a rationale for each split A testability audit: for each major module, identify the specific structural reason it resists unit testing — side effects, hidden dependencies, mixed concerns — and prescribe the exact refactor pattern to fix each A naming convention overhaul: flag all ambiguous function and variable names in the target module and provide renamed versions with a one-line explanation of the naming logic A dependency inversion checklist: identify every place where high-level logic depends on low-level implementation details and rewrite those interfaces to be injectable and mockable A test coverage roadmap: define which 10 functions to test first to reach 80% coverage fastest, ordered by risk surface and call frequency A architecture decision record template: a reusable format for documenting each unclear decision — context, options considered, decision made, and consequences — so the open source team can align without synchronous meetings A readability scoring rubric: 5 criteria for evaluating whether a refactored function is genuinely more readable or just differently complex, with a pass/fail example for each criterion Map every architecture ambiguity to a testability consequence before proposing a refactor — readable code that cannot be tested is not a solution, it is a deferred problem.