Log Report Automation

Overview

A public automation project focused on repeatable operational reporting and explicit data-quality feedback.

Problem / motivation

Raw CSV logs require validation, analysis and careful presentation before they become reliable reporting artifacts.

What it implements

• Validate input with strict and lenient modes and actionable command-line summaries.
• Calculate reliability metrics including error rates and P95/P99 response times.
• Generate formatted Excel worksheets and charts while protecting untrusted text from formula execution.

Architecture / workflow

• The CLI reads structured CSV logs and applies strict or lenient validation before analysis.
• Validated records feed reliability calculations, including error rates and response-time percentiles.
• The reporting layer writes formatted Excel worksheets, charts and explicit data-quality findings.

Key engineering decisions

Reliability / security considerations

• Validation failures remain visible instead of silently disappearing from the report.
• Untrusted text is handled before workbook output to reduce spreadsheet formula-execution risk.
• A command-line interface makes the same transformation repeatable in local scripts and CI.

Validation / testing

• pytest covers the report-generation path and its data-handling behaviour.
• Generated workbooks are checked through summary, detail, trend and data-quality views.
• GitHub Actions provides automated validation for the public repository.