Software Quality Intelligence Technology


Recent years witnessed an explosion of new developments and changes in the software development industry. The current nexus of software development lies at the intersection of containers, microservices, full test automation, modern cloud, serverless architecture, virtualized environments and many more. This combined with the move from centralized software development teams to distributed/decentralized ones has led to massive and multiple data resources.

The introduction and integration of data analytics into this new software development industry will precipitate an increase in the efficiency and speed with which software engineering teams develop and deploy software solutions.The volume of data obtained throughout the software development pipeline is enormous owing to the constant stream of big data flowing in through multiple channels. Gaining a clear visibility into this data is complex and difficult, making data-driven decisions impossible for humans.

What is Quality Intelligence Technology?

Quality Intelligence technology facilitates the collation and analysis of data from multiple sources thus providing software development teams with sufficient insight and visibility into factors affecting the velocity, quality, and efficiency of their software engineering processes and deliverables.


Since software development teams often work rapidly and deploy solutions swiftly, they have little or no time to perform structural quality analysis or identify critical programming errors, until the testing phase. This adverselyaffects the velocity of software development and product release.

Combating this problem requires a solution that can apply real-time analytics to thousands of data items, test executions, insights, builds, code changes, and historical data from production to determine software release readiness. This solution should also provide accurate, real-time data to R&D managers to enable them assess and plan efficient sprints through the analysis of quality trends and the risks presented by code changes over time.


The components of software quality intelligence include the following

Data Gathering

Collation of data from multiple sources across the software development pipeline, such as code changes, test stages, Code execution, CI tools, production data, historical build information and more.


Analysis of thousands of data items in real time for every single build.


Provision of meaningful views and metrics that will guide the software development team to the right decisions.

Test Quality Analytics

Directs teams on where to develop the minimal number of tests with the highest impact

Test Impact Analytics

Identifies which tests should be executed when a specific file or method is changed.

Release Quality Analytics

Determines release readiness and identifies and prevents untested code changes from reaching production.

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