Strengthening pyaslreport reliability through automated testing and CI

The pyaslreport package currently has no automated tests for its core processing logic and no CI pipeline that runs on code changes, meaning regressions in validation behavior, parameter extraction, or report generation can go undetected until a user encounters them in a real research context. This project will build a meaningful automated test suite around the real architecture of the package and integrate it into a GitHub Actions CI workflow. The suite has two layers: an example-based integration runner that accepts a directory of input and expected output pairs and verifies the tool produces correct results for each one, and focused unit tests covering the internal normalization pipeline, schema-driven validators, M0 and TSV validation branches, file grouping logic, and sequence factory dispatch. Adding a new test case to the integration layer requires no code changes, only a new subfolder with the right files. The same runner works locally against any directory of examples and in CI against a smaller committed set, which is exactly the design Jan described in his feedback. Deliverables: an expanded test suite for pyaslreport, a committed example set covering clean, warning, error, and major error cases, a GitHub Actions workflow with coverage reporting, and a contributor guide explaining how to run and extend the tests.