Challenges
Since software development teams often work rapidly and deploy solutions swiftly, they have little or no there is often not much time to perform structural code quality analysis or identify critical programming errors until the testing phase. This adversely affects the velocity of software development and Typically, such deferrals adversely affect product development velocity and confidence in the product release.
Combating What is needed to combat this problem requires is a solution that can apply is adept at assessing software release viability by performing real-time analytics to on thousands of data items, test executions, insights, builds, code changes, and historical production data from production to determine software release readiness. This . Such a solution should also provide accurate, real-time data to R&D managers to enable them to assess and plan efficient sprints through the analysis of quality trends and the risks presented by code changes over time.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 so that they can analyze quality trends, assess risks, and refine development sprints to be more efficient.
SeaLights Quality Intelligence Technology enables easy, rapid collation and data analysis from multiple sources. These new abilities provide software development teams with better insight and clearer visibility into factors that affect the velocity, quality, and efficiency of their software engineering processes and deliverables.
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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.
Analytics — Analysis Real-time analysis of thousands of data items in real time for each and every single build.
Insight — Provision of meaningful Meaningful views and metrics that will guide the software development team to the right best decisions.
Test Impact Analytics — Identifies which tests should be executed need to run when a specific file or method is changedchanges.
Test Quality Analytics — Directs teams on where to develop the minimal number of tests with the highest impactthat will have the greatest benefit in achieving quality improvements.
Release Quality Analytics — Determines release readiness and , then identifies and prevents untested code changes from reaching production.
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