What metrics should you track in QA engineering for better product quality?

In QA engineering, focus on tracking metrics such as Defect Density, Test Coverage, Customer Reported Defect Rate, and Regression Test Stability to enhance product quality effectively.

A porcentagem do código ou funcionalidades do produto que s?o testadas ajuda a garantir que uma ampla gama de cenários e casos de uso sejam abordados nos testes. Isso pode incluir cobertura de código, cobertura de requisitos ou cobertura de funcionalidades.

Test Execution and Bug Fixing Test Design Test Coverage User Acceptance Testing Product Exploitation and Support Test Economics Metrics Test Execution Status Defects Created vs. Defects Resolved Chart Overall Metrics

Functional coverage starts with user (external) and stakeholder (internal) needs that flow-down to system, product and component-level requirements. A trace-matrix from user/stakeholder needs to functional requirements to tests executed/passed/failed/blocked. For structural coverage: system, product and component architecture, design and implementation are the reference-models for testing. At the system-level this can include APIs/Functions called vs. the total population. Interface coverage measures inter-dependencies tested vs. the total (often derived from build-tools and integrated development environments). Component-level measures total statements, conditions and decisions (tested vs. actual). GAI for selective-paths test coverage.

Defect Density is the number of defects confirmed in production line during a specific period of operation or product development divided by the size of the production in terms of quantity/units. This metric helps companies analyze the performance of their products. Defect density can also help companies identify improvement opportunities within their new product development (NPD) teams and processes, particularly in R&D phase. Before contributing this article I did some research and I found that apart from manufacturing, software and IT industry also count their defect density to know how extent the is software defect/bug free. However its a simple mathematical calculation, no of defects divided by no of units being produced.

Defect density is a crucial metric to track in QA engineering for better product quality. It measures the number of defects identified in a specific module or component divided by the size or volume of that module. This metric provides insights into the quality of code and helps teams pinpoint areas with higher defect rates, enabling targeted improvements. A lower defect density indicates higher code quality and better product reliability. By monitoring defect density, QA engineers can focus efforts on areas prone to issues, leading to enhanced overall product quality.

Defect density can be as simple as pareto analysis to separate the critical few (20%) from trivial many (80%). Product defect databases are mined for this data fairly easily however, key fields may be missing for structural and process information (e.g., component(s), when found, when caused, etc.). Defect density data is best used by quality engineers and product owners to develop preventive actions and not just corrections for defects reported.

When a defect is resolved a new fault may be added to the design/implementation--a regression. Therefore, along with defect resolution time consider regression failures introduced. Measure twice and cut once definitely applies to hardware, software and systems implementations.

Tracking defect resolution time is crucial in QA engineering for enhancing product quality. This metric measures the time taken from identifying a defect to resolving it. A shorter resolution time indicates efficient bug-fixing processes and faster turnaround for addressing issues, leading to improved product stability and reliability. By monitoring this metric, QA teams can identify bottlenecks in the development and testing workflow, prioritize critical fixes, and implement corrective actions promptly, ultimately ensuring a smoother and more efficient software development lifecycle.

Se podría tener un excel por equipo o quizá una herramienta de recopilación de casos automatizados, por ejemplo Jira XRay puede ser de gran ayuda, ya que es una herramienta de trabajo colaborativo, la cual permite obtener informes.

Ao coletar e analisar feedbacks dos usuários, as empresas podem identificar pontos fortes a serem mantidos e áreas de melhoria a serem abordadas. Isso permite ajustar e adaptar produtos de acordo com as preferências e demandas dos usuários, promovendo assim uma experiência mais satisfatória. Além disso, o feedback do usuário pode ajudar a fortalecer o relacionamento entre empresa e cliente, demonstrando que suas opini?es s?o valorizadas e levadas em considera??o.

As a quality assurance officer in an aircraft overhaul agency, tracking specific metrics is vital for enhancing product quality. Monitor defect density to assess the effectiveness of defect identification and resolution processes. Track mean time to detect and mean time to resolve defects to ensure timely issue resolution. Measure adherence to regulatory standards and compliance requirements to guarantee aircraft safety and airworthiness. Additionally, track customer satisfaction through feedback surveys or NPS to gauge overall satisfaction with the aircraft maintenance services provided. Continuously analyzing and improving these metrics will contribute to maintaining high product quality standards.