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As discussed earlier this week on the mabl blog, quality engineering and quality assurance are largely differentiated by how they manage product quality. While QA seeks to improve software primarily at the final stages of the development process, quality engineering integrates testing throughout development for a more collaborative and comprehensive quality process. The foundation for these new processes: data. 

With the right test automation platform, software testing becomes a fountain of valuable insights for the entire organization. Developers can use unit tests, integration tests, and end-to-end tests to build a better understanding of their code quality throughout development, while product managers can pinpoint quality trends for better planning. This unified approach is fundamental to adopting quality engineering, making it essential for testing solutions to integrate with data warehouses like BigQuery. 

Education, Action, and Impact for Software Testing

Part of the Google Cloud Platform, BigQuery democratizes data and insights across the enterprise. It’s designed to turn data into decisions so that teams can move faster with confidence. Combined with mabl, BigQuery enables quality teams to transform testing data into customized reports in Data Studio. When quality engineering teams are empowered to share their work with the rest of the software development team, they’re prepared to engage developers, product managers, and the C-suite in critical quality discussions that strengthen the product. 

Shared Data Creates Shared Quality Processes

Quality engineering takes a 360 degree view of the development pipeline to build team confidence in the product, accelerate product velocity, and improve customer retention. With such a broad impact, quality teams need the tools to amplify their goals and metrics for all stakeholders. 

The BigQuery integration makes it easier to find test run information, which includes details like test status and the browser on which it was run. Mabl users can find detailed information about plan runs, including application environment, deployment labels, and test run information. Using the mabl BigQuery integration will result in data tables like this: 

Data table containing test plan runs

Similar tables can be created for test runs, both of which will be automatically updated as more tests are executed. Mabl users are able to categorize every test failure, which can also be displayed in BigQuery. This data is particularly valuable when understanding overall application quality since it captures test trends at a glance, making investigating issues easier for developers and quality engineers alike. Our team detailed how dashboards created with BigQuery and Data Studio helped them uncover and resolve an issue in mabl’s production environment. Data Studio report showing test failure trends
Charts showing median test run time and average test run time

When things are running smoothly, these dashboards ensure that the entire team has visibility into mabl’s software testing strategy. When issues arise, data from mabl and BigQuery streamline the investigation so that problems can be resolved quickly. 

Measuring Application Quality with BigQuery

Quality engineering is a technical shift, new mindset, and process evolution. As QA teams expand their focus to the entire software development life cycle, increased awareness of the team’s software testing strategy is critical for building a culture of quality. With mabl and BigQuery, sharing these metrics is drastically simplified for actively improving product quality. When everyone is united behind testing and quality goals, the customer benefits. 

See how your team can harness the power of BigQuery + mabl as part of our 14-day free trial.