Delphi Simple Code Analyzer: Quick Guide to Static AnalysisStatic code analysis is a vital part of modern software development. For Delphi (Object Pascal) developers, integrating static analysis into your workflow helps catch bugs early, improve code quality, and enforce consistent style. This guide explains what the Delphi Simple Code Analyzer (DSCA) does, how to use it, typical checks it performs, and how to integrate it into your development and CI processes.
What is Delphi Simple Code Analyzer?
Delphi Simple Code Analyzer (DSCA) is a lightweight static analysis tool designed for Delphi/Object Pascal projects. It scans source files without running them, identifies potential problems (bugs, bad patterns, performance issues, and style inconsistencies), and reports findings so you can fix issues proactively. DSCA focuses on simplicity and speed, making it suitable for both small teams and large codebases.
Why use static analysis for Delphi?
- Bug prevention: Detects mistakes like uninitialized variables, unreachable code, or incorrect use of properties before runtime.
- Maintainability: Highlights complex or unclear code, helping you refactor toward simpler, more readable implementations.
- Consistency: Enforces coding conventions and style rules across a team.
- Performance and safety: Identifies potential performance pitfalls and unsafe constructs.
- CI/CD readiness: Runs automatically in builds to prevent regressions and maintain quality gates.
Typical checks performed by DSCA
DSCA commonly includes categories of checks such as:
- Syntax and parsing issues (malformed constructs, missing semicolons)
- Unused declarations (variables, constants, types, methods)
- Uninitialized variables or fields
- Possible nil-dereference points
- Resource leaks (objects not freed, file handles left open)
- Deprecated API usage and platform-specific concerns
- Complexity metrics (cyclomatic complexity, method length)
- Naming and style inconsistencies (camelCase vs PascalCase, prefix/suffix rules)
- Integer overflow risks and unsafe casts
- Dead code detection and unreachable branches
- Incorrect parameter usage (passed by value vs reference expectations)
- Unit and dependency problems (circular uses, unnecessary units in interface sections)
Installation and setup
- Obtain DSCA:
- Download a binary/package from the project site, or clone the repository and build from source if available.
- Place DSCA in your PATH or in a tools directory inside your project.
- Create a configuration file (commonly named .dsca.yaml, dsca.json, or similar) at the project root to control rule sets, severity levels, and file inclusion/exclusion patterns.
Example (conceptual) settings you might find in a config:
- File globs: src//*.pas, include//*.inc
- Exclusions: src/thirdparty/, test/
- Rule enable/disable: no-unused-vars: warn, resource-leak: error
- Complexity thresholds: cyclomatic: 10, max-lines-per-method: 200
Running DSCA
Basic CLI usage (example):
dsca analyze --project MyApp.dproj dsca analyze --src src --config .dsca.yaml dsca analyze --output report.json --format json Common options:
- –config: path to configuration file
- –format: text, json, xml, html
- –output: write report to file
- –level: set minimum severity to show (info/warn/error)
- –threads: adjust parallelism for faster scanning
Interpreting results
DSCA outputs a list of findings with:
- File path and line number
- Rule identifier and severity
- Short description and recommended action
- Optional code snippet and suggested fix
Example finding:
- src/MainForm.pas:42 — warning: Unused local variable “iLoop” — consider removing or using it.
Prioritize fixes:
- Errors that can cause crashes or data corruption (nil dereferences, resource leaks).
- Warnings that frequently lead to bugs (uninitialized variables, incorrect API use).
- Style and complexity issues, which improve long-term maintainability.
Integrating into development workflows
- Editor/IDE integration: Use editor plugins or configure your IDE to run DSCA on file save or on-demand. Some setups support inline diagnostics in the editor.
- Pre-commit hooks: Run DSCA (or a subset of checks) in git pre-commit to prevent low-quality code from entering the repository.
- Pull request checks: Add DSCA as a step in CI to fail PRs that introduce new high-severity issues.
- Scheduled analysis: Run full-project DSCA weekly to catch issues that slip through incremental checks.
Example GitHub Actions job (conceptual):
name: DSCA Check on: [pull_request] jobs: analyze: runs-on: ubuntu-latest steps: - uses: actions/checkout@v3 - name: Install DSCA run: ./tools/install-dsca.sh - name: Run DSCA run: dsca analyze --src src --format json --output dsca-report.json - name: Upload report uses: actions/upload-artifact@v3 with: name: dsca-report path: dsca-report.json Custom rules and extensions
Many DSCA-style tools let you add custom checks:
- Scripting: Write rules in Lua, Python, or a domain-specific language supported by DSCA.
- AST plugins: For advanced rules, extend DSCA to walk the parsed AST and apply custom logic.
- Rule configuration: Tweak thresholds, ignore patterns, and per-module rules via the config file.
Examples of useful custom rules:
- Disallow certain third-party APIs (e.g., banned components)
- Enforce specific memory ownership patterns
- Flag methods missing documentation or not following naming conventions
Common pitfalls and limitations
- False positives: Static analysis can report issues that are not real problems; tune rules and add local suppressions where justified.
- Context sensitivity: Some checks can’t fully understand runtime behavior, especially with dynamic constructs or complex RTTI usage.
- Build-time dependencies: If your project uses conditional compilation or complex build configurations, ensure DSCA parses the correct defines and search paths.
- Performance on very large codebases: Use incremental runs or per-file analysis to keep feedback fast.
Best practices
- Start small: Enable a subset of high-value rules, fix issues, then expand the rule set.
- Baseline existing code: Run DSCA once, mark existing issues as acceptable (baseline), and enforce only new issues going forward.
- Use severity levels: Treat errors as build blockers, warnings as review items, and info as optional guidance.
- Combine static analysis with tests and code reviews for better coverage.
Example workflow to adopt DSCA
- Install DSCA and create a minimal config with critical checks (resource leaks, uninitialized variables).
- Run DSCA on the codebase and create a baseline file for current findings.
- Add DSCA to CI and fail builds that introduce new errors.
- Gradually tighten rules and require fixes on critical warnings.
- Add IDE integration and pre-commit hooks for developer convenience.
- Periodically review and refine rules, measuring metrics like number of new issues per week.
Conclusion
Delphi Simple Code Analyzer provides an accessible path to improve Delphi code quality through fast, actionable static analysis. By introducing DSCA incrementally, integrating it into CI and developer workflows, and tuning rules to your project’s needs, you can reduce bugs, enforce consistency, and make your codebase easier to maintain. Static analysis is not a silver bullet, but used alongside testing and good engineering practices, it significantly raises the bar for software reliability.
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