Following the stabilization of the public surface in version 3.1, my focus for January turned toward the “engine of the engine.” As a solo maintainer, I don’t have a team to review my PRs or catch my blind spots. To maintain a professional-grade codebase, I must rely on a “Virtual Team” of automated feedback loops.

This month was defined by two major themes: hardening our quality gates and a fundamental pivot in how we handle internal documentation.

The Virtual Team: Qodana and PIT as Gatekeepers

While Qodana and PIT have been part of our repository for some time, this month they graduated from “informative” to “enforcement” in our CI pipeline. As a solo maintainer, I don’t have a team to review my PRs or catch my blind spots, so I must rely on a “Virtual Team” of automated feedback loops that actually block merging when quality drops.

Qodana (by JetBrains) has been our static analysis tool, but this month I updated the configuration to fail the build on new code smells and complexity spikes. It now acts as my static analysis lead, providing a consistent baseline for “clean code.” I’ve also updated our GitHub repository to feature the official Qodana badge, replacing the generic “analyze” workflow status.

Similarly, PIT (Mutation Testing) has been our quality auditor, but I’ve now tightened its integration into the main mvn verify cycle. As I discussed previously, line coverage is often an illusion. By making PIT results a first-class citizen in our CI reports, I’ve been able to identify and fix several “dark corners” in our utils module where code was executed but not truly verified.

The AsciiDoc Pivot: Zero Dependencies

The most significant architectural change this month happened in the asciidoc module. In late December, I had planned to adopt AsciidoctorJ to handle our internal doc-to-text conversions.

However, by mid-January, the “JRuby tax” became unbearable. The overhead of the JRuby runtime and the AsciidoctorJ wrapper increased our build times and introduced a heavy dependency chain that felt out of place for a “lightweight” library.

At the same time, I’ve been following the exciting work by the Eclipse Foundation on the AsciiDoc Language project, which is working toward a formal specification and a native Java implementation. While we wait for that standard to mature, I decided to pivot.

I’ve replaced the entire AsciidoctorJ integration with a custom, lightweight Java parser. This was also a perfect opportunity to experiment with GenAI to kickstart a side-project: I used AI to generate the first draft of the parser, which I then refined to fit our specific needs.

Why a Custom Parser?

  1. Performance: Startup time for the conversion pipeline is now instantaneous, removing the definitely too slow JRuby.
  2. Future-Proofing: By keeping our requirements minimal today, we’ll be better positioned to adopt the official Eclipse AsciiDoc implementation once it’s ready.
  3. Zero External Runtime: No more external binaries or heavy runtimes required.

This pivot aligns with my core philosophy: use the right tool for the job, but don’t be afraid to build a simpler one if the “standard” tool is overkill, especially when it serves as a bridge to a better future standard.

Site Hygiene: Navigation and Logos

With the 3.1 release, our documentation site structure needed to evolve. I’ve overhauled the site navigation to make the API guides and migration notes more discoverable. You’ll also notice a refined logo and color palette that matches the professional aspirations of the project.

Impact: Faster, Leaner, Safer

The result of this month’s work is a codebase that is not only faster to build but significantly safer to refactor. The “Virtual Team” handles the gatekeeping, allowing me to focus on the next major milestone: version 3.2.

Next Steps

With the quality gates in place, I’m now working on expanding the API surface to support complex table manipulation—a feature that will land in the 3.2 release.

February 1st 2026 — Commit Summary:

  • Feat: Integrated Qodana for advanced static analysis in CI.
  • Feat: Added PIT mutation testing to ensure test suite effectiveness.
  • Refactor: Replaced AsciidoctorJ with a custom lightweight AsciiDoc parser in the asciidoc module.
  • Docs: Restructured site navigation and updated branding/logos.
  • Refactor: Cleaned up utils module (replaced legacy instanceof checks and refined lambda naming).