A quality assessment is an evaluation of software or a development artifact against defined quality criteria.
A quality assessment turns a code change into actionable feedback: automated checks can flag quality problems or compilation failures, notify the developer, and start another improvement loop. section 1.2.1
A practical quality-assessment workflow combines several checks:
Standardize formatting so work is consistent and easier for newcomers to understand. section 5.0section 5.8
Run linters and static code analysis to detect known anti-patterns, bugs, and vulnerabilities. section 5.0section 5.8
Place checks early in the delivery pipeline so failures arrive before deployment and reduce waiting time. section 5.8
Use code reviews, documentation, and process improvement to extend assessment beyond automated checks. section 5.0section 1.2.1
A startup’s first quality loop
Begin with simple tests for core functionality rather than comprehensive end-to-end coverage. When bugs or recurring errors appear, add targeted tests; once the product stabilizes, organize a cleanup effort that adds tests and documentation across the codebase. section 11.1
How Shipping Go: Develop, deliver, discuss, design, and go again covers it
How deep it goes
Explains formatting, static analysis, testing, and pipeline timing, but stops short of detailed assessment criteria or implementation internals.
Examples
Uses formatting tools, linters, static analysis, repository checks, unit tests, and code-cleanup efforts with added tests and documentation.
What it assumes
Assumes working knowledge of code and development workflows while introducing quality checks as an incremental practice.
What you can do after
Diagnose/debug with it by responding to automated failures, adding tests where errors recur, and improving the development process.
bugaddresses · A software bug is a defect in software or a related system component that causes behavior to differ from what is intended or expected.
data explorationhas part · Data exploration is the systematic examination and summarization of data to understand its structure, quality, distributions, relationships, patterns, and anomalies.
data cleaninghas part · Data cleaning is the process of detecting and correcting, removing, transforming, or otherwise handling inaccurate, incomplete, inconsistent, duplicate, or unsuitable data.
standardproduces · A standard is an established rule, specification, or convention that defines a consistent reference for practice, representation, behavior, or evaluation.
automationused by · Automation is the execution of tasks or processes by machines or software with reduced direct human intervention.
This explainer was generated by Manning's knowledge graph from the text of Shipping Go: Develop, deliver, discuss, design, and go again. The section links point to the parts of the book it draws on.
