DevOps Quality Measurement Support with Automated Maturity Recommendations
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Solution Overview
Problem
Existing methods for measuring and managing software development and operations (DevOps) systems are inadequate due to the intangible nature of software objects, leading to challenges in data communication, aggregation, and system size, which hinders efficient quality and performance evaluation.
Innovation Solution
A support system that processes quality and performance indicators using a configuration module, input interface, modeler module, recommender module, and output interface to analyze and recommend actions for improving software development and operations systems, incorporating KPI systems and artifact-based maturity models for automated measurement and feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional value engineering and earned value management methods are applied to software development, then project performance can be measured, but the intangible nature of software objects makes accurate measurement and aggregation difficult
Solution Approach 1:
The patent introduces an intermediary measurement system that acts as a mediator between software development processes and quality assessment. This system collects data from multiple sources (development tools, deployment systems, monitoring tools) and transforms them into measurable quality indicators, bridging the gap between intangible software objects and measurable properties.
Solution Approach 2:
The patent replaces traditional mechanical measurement approaches with automated digital measurement and aggregation systems. Instead of manual measurement of software properties, the system uses automated data collection from development tools, deployment pipelines, and monitoring systems to quantify software quality and performance indicators.
2Reliability
If comprehensive data collection is implemented to improve measurement accuracy, then quality evaluation becomes more reliable, but system complexity and data aggregation challenges increase
Solution Approach 1:
The patent segments the complex data aggregation system into modular components: data collection modules from various sources, data processing modules that normalize and transform data, and evaluation modules that generate quality indicators. This segmentation reduces overall system complexity while maintaining comprehensive measurement capability.
Solution Approach 2:
The patent creates a universal measurement framework that can collect and process data from multiple diverse sources (development tools, deployment systems, monitoring tools) through a common architecture. This multi-functional system handles various data types and sources using standardized processing mechanisms, reducing complexity compared to separate specialized systems.
3Productivity
If automated measurement and aggregation systems are deployed to improve efficiency, then processing speed increases, but initial system setup and configuration complexity increases
Solution Approach 1:
The patent implements preliminary configuration of data sources, collection mechanisms, and processing rules before actual measurement operations begin. The system is pre-configured with connectors to development tools, deployment systems, and monitoring tools, along with predefined data transformation logic, enabling automated high-speed processing without complex on-demand configuration.
4Measurement precision
If detailed quality indicators and performance metrics are implemented, then measurement precision improves, but communication and aggregation of results becomes more challenging
Solution Approach 1:
The patent extracts and isolates specific quality indicators and performance metrics from the vast amount of available data. By identifying and extracting only the most relevant measurable properties (such as deployment frequency, system availability, error rates), the system reduces data communication overhead while maintaining high measurement precision for the most important quality attributes.
Data Source
AI summary
A support system includes a processor, wherein the support system is configured to process measured quality data representing at least one quality and/or performance indicator for controlling a software development and operations system, further including: a configuration module used to set up connectors to at least one history data source of the DevOps system and/or to at least one enterprise resource planning data source, an input interface module configured to import a data model including predefined model parameters with predefined initial values, a modeler module including the data model, a recommender module configured to analyze current evolution degree depending on assigned values, to estimate a quality and/or performance trend derived from the evolution degree and from the aggregated data from the at least one history data source and an output interface to output at least one message containing recommendation for at least one action to be taken.