Software Quality Scoring for Consistent Release Readiness
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Solution Overview
Problem
Conventional software testing systems lack the ability to provide an easily understood and actionable analysis of software quality, making it difficult to determine when a product or code is 'ready' for release, and human assessments often lead to inconsistent results due to bias and oversight.
Innovation Solution
An integrated system that generates a software quality score (AQS) based on various factors, including testing results and user-defined criteria, providing a visual interface for decision-making and enabling users to trigger releases when quality thresholds are met.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional testing systems are used to comprehensively test software, then testing coverage is improved, but the ability to convey actionable quality analysis deteriorates
Solution Approach 1:
The patent extracts and isolates the essential quality metrics from comprehensive testing data, separating actionable quality indicators (pass rate, failure rate, critical bug counts) from the full testing dataset. This allows the system to maintain comprehensive testing coverage while presenting only the most relevant quality information in an actionable format.
Solution Approach 2:
The patent segments the quality assessment into distinct measurable components including pass rate, failure rate, critical bug counts, and testing coverage percentages. Each component is calculated separately from testing data and can be independently analyzed and presented, making the overall quality assessment more actionable and easier to interpret.
2Adaptability or versatility
If human experts assess software readiness, then judgment flexibility is improved, but consistency and objectivity deteriorate
Solution Approach 1:
The patent transforms subjective human judgment into objective numerical parameters including pass rate thresholds, failure rate limits, and critical bug counts. These quantitative parameters provide consistent, reproducible assessment results while allowing configuration of thresholds to match different organizational requirements, maintaining flexibility through parameter adjustment rather than subjective decision-making.
Solution Approach 2:
The patent replaces human expert judgment with an automated computational system that objectively calculates quality metrics based on testing data. The system automatically determines readiness status by comparing measured parameters against predefined criteria, eliminating human bias and inconsistency while maintaining the ability to adapt to different assessment requirements through configurable parameters.
3Reliability
If comprehensive testing is performed, then software quality is improved, but the ability to determine readiness deteriorates
Solution Approach 1:
The patent performs preliminary calculations of quality metrics during the testing process itself, continuously tracking pass rate, failure rate, and bug counts as testing progresses. This preliminary aggregation of data enables readiness determination to be made quickly at the end of testing without requiring re-analysis of the entire test suite, as all necessary metrics are already computed and stored.
Solution Approach 2:
The patent creates simplified representations of testing results in the form of aggregated quality metrics (pass rate, failure rate, critical bug counts) that capture the essence of comprehensive testing without requiring re-examination of individual test cases. These metric copies enable rapid readiness assessment while maintaining the integrity of the comprehensive testing that was originally performed.
4Measurement precision
If detailed quality metrics are provided, then assessment accuracy is improved, but ease of understanding deteriorates
Solution Approach 1:
The patent segments detailed quality metrics into distinct, clearly labeled components including pass rate, failure rate, critical bug counts, and testing coverage. Each metric is presented separately with its own calculation basis and interpretation guidelines, making the overall assessment more understandable while maintaining the detail needed for accurate quality evaluation.
Solution Approach 2:
The patent employs visual indicators such as color-coded status labels (e.g., green for passing, yellow for warning, red for failing) that provide immediate intuitive understanding of quality status. These visual elements complement the numerical metrics, allowing users to quickly grasp assessment results without needing to interpret detailed data, while the underlying precise metrics remain available for in-depth analysis.
Data Source
AI summary
An assessment system can generate a software quality value based on testing results and analysis of a multitude of factors that impact a readiness evaluation. For example, the system generates a software quality score (e.g., an Applause Quality Score “AQS”) that enables development teams to understand the level of quality they are achieving for a given release and build-over-build. In various examples, the system generates a data-driven score to enable development teams or quality assurance teams to make decisions for when a build is ready for release. In further embodiments, the system can integrate user interfaces that present a software quality score in a user dashboard that is linked to version control systems. On review and acceptance of the score, a user can trigger the release of their new code or product.