The modern processes used in software development are swift, streamlined and distributed; however, insufficient data sources make software testing an arduous and time-consuming process. The introduction and integration of data analytics into the 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.
This aids in process optimization and enables software managers to attain organization-wide visibility. A Quality Intelligence solution brings all the disparate information together to give software engineering teams unparalleled data visibility into the entire software development process, enabling them to mitigate weaknesses and magnify strengths in their operations.
Since software development teams often work rapidly and deploy solutions swiftly, they have little or no time to perform structural quality analysis and measurement, identify critical programming errors and optimize their solutions for reliability, efficiency, security, maintainability, and size considerations. During the software testing phase, selecting the right kinds and quality of tests is integral to software quality control and assurance.
Failure to do this leads to low quality of deliverables, reduced efficiency, and loss of operational effectiveness. Also, software development teams today rely on insufficient data resulting in bad release decisions. Ensuring software quality 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
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.