Software Analytics System for Defect Level Comparison
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Solution Overview
Problem
Current software testing methods lack standardization and coherence, making it difficult for stakeholders to obtain a comprehensive and uniform view of software health or feature health during the development cycle, as they are overwhelmed by numerous metrics without a logical correlation.
Innovation Solution
A data analytics system that integrates a project data manager, data mining module, and report generation module to analyze and present software development data, comparing expected and actual defect levels across development stages, providing a unified and objective analysis through standardized data formats and algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If multiple software testing models and tools are used to gather comprehensive software metrics, then the quantity of information about software health is improved, but the complexity and difficulty of understanding and correlating this information increases
Solution Approach 1:
The patent segments the comprehensive software metrics into distinct categories (e.g., code quality metrics, test coverage metrics, defect density metrics) and presents them through separate visualizations and analysis sections. This segmentation allows stakeholders to understand individual metric types without being overwhelmed by the complexity of all metrics simultaneously, while still maintaining comprehensive coverage of software health.
Solution Approach 2:
The patent introduces an intermediary analysis layer that automatically correlates and contextualizes multiple software metrics. This intermediary layer translates raw metric data into meaningful insights and relationships, acting as a mediator between the comprehensive metrics and stakeholder understanding. The system provides standardized definitions and frameworks that serve as intermediaries to unify diverse metrics from different testing tools.
2Stability of the object's composition
If standardized data formats and algorithms are implemented across different testing tools, then the coherence and uniformity of software health view is improved, but the difficulty of integrating existing diverse tools decreases
Solution Approach 1:
The patent implements a universal data framework that can accommodate multiple existing testing tools and metrics while maintaining coherence. This universal framework serves as a multi-functional platform that standardizes data formats and algorithms across different tools without requiring complete replacement of existing tools. The system provides standardized definitions and integration layers that enable diverse tools to contribute to a unified software health view.
Solution Approach 2:
The patent changes the parameters of data representation and organization to achieve standardization. By transforming diverse metrics into standardized parameter formats and using consistent algorithms for analysis, the system achieves coherence while maintaining flexibility to integrate existing tools. The parameter changes involve standardized data schemas, uniform measurement approaches, and consistent presentation formats.
3Ease of manufacture
If manual processes are used for partial reporting, then the simplicity of implementation is maintained, but the productivity and completeness of software health assessment decreases
Solution Approach 1:
The patent implements automated self-service functionality that continuously monitors, collects, and analyzes software metrics without requiring manual intervention. The system automatically generates comprehensive software health assessments and reports, freeing stakeholders from manual processes while maintaining simplicity of use. The automated system serves itself by continuously updating software health status based on incoming metric data.
Solution Approach 2:
The patent incorporates automated feedback mechanisms that continuously assess software health and provide real-time insights. The system automatically compares current metrics against established thresholds and historical data, generating actionable feedback reports without manual analysis. This automated feedback loop significantly improves productivity while keeping the interface simple for stakeholders.
Data Source
AI summary
Embodiments describe methods, apparatuses, and systems for performing data analytics on one or more features of software under development. In one exemplary embodiment, a data mining module receives a first set of data including an expected timeline of a plurality of features of program code being developed. The data mining module further retrieves a second set of data from a program testing system based on the first set of data. The second set of data includes defect information and a testing result for each of the plurality of features indicated in the first set of data. Moreover, a data analysis module executed performs an analysis on the first and second sets of data. Based on the analysis, a report generator generates an analysis report that includes an expected defect level and an actual defect level of each of the plurality of features.