Understanding Project Quality Management in Game Development: Theory, Practices, and QA Workflows

The article begins by defining management in a broad sense and then narrows focus to quality management, emphasizing how theoretical definitions can guide daily QA work in game projects.

Management is split into task‑oriented activities (plan, execute, control) and people‑oriented activities (guide, communicate, motivate), providing a framework for understanding quality processes.

Project quality management, as described in the PMBOK® Guide, consists of three core processes: planning quality, implementing quality, and controlling quality, each mapped to specific phases of a project.

Planning quality focuses on identifying required quality standards; implementing quality translates those standards into test cases, evaluation tools, and QA activities; controlling quality compares actual results with the defined standards to ensure acceptability.

The article connects these processes to concrete QA tasks in game development, such as defining bug handling standards, release criteria, test case design, and maintaining a standardized knowledge base, all supported by organizational process assets.

It stresses the need to assess the cost‑benefit of standards, communicate them clearly to all stakeholders, and use the influence of PMs, producers, and functional managers to ensure adoption.

QA work is described in three progressive layers: basic testing (cases, bugs, regression), intermediate testing (system modules, full development cycle), and advanced testing (document analysis, code review, resource checks), each adding depth to quality assurance.

The author categorises quality issues into product quality, project quality, and people quality, arguing that all three must be addressed for effective management.

Supervision and recording are highlighted as essential tools, with examples of reports such as smoke tests, test summaries, bug analysis, version risk reports, and performance metrics, while warning that mere recording without analysis adds little value.

An illustrative PDCA cycle is presented: analyzing bug‑priority distribution, identifying root causes, implementing corrective actions, and planning the next iteration, demonstrating data‑driven continuous improvement.

Finally, the article dispels the myth that high quality inevitably raises costs, advocating for “getting it right the first time” through systematic quality management and ongoing refinement.