ML Model Predicts App Development Metrics
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The software application development process faces challenges in measuring quality and consistency due to tracking multiple variables, leading to wasted time and resources on generating unreliable software applications and unmaintainable code bases.
Innovation Solution
A metric platform utilizing a machine learning model processes historical application creation data to generate predictions for new applications, predicting success or failure during development, deployment, and release phases, and performs actions based on these predictions to prevent resource wastage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple variables are tracked in the software application development process, then measurement capability is improved, but device complexity and resource consumption increase
Solution Approach 1:
The patent segments the complex tracking of multiple development variables into distinct phases (application development, build, test, artifact, deployment, release, monitoring, support). Each phase is tracked independently with phase-specific metrics, reducing the complexity of overall measurement while maintaining comprehensive quality assessment across the entire development lifecycle.
2Measurement precision
If comprehensive tracking of development variables is implemented, then quality measurement is improved, but time and resource efficiency deteriorate
Solution Approach 1:
The patent performs preliminary actions by training machine learning models on historical application creation data before actual development occurs. The models predict potential issues, success probabilities, and resource requirements in advance, enabling proactive decision-making that prevents wasted time and resources on problematic applications while maintaining comprehensive quality tracking.
3Loss of energy
If machine learning models are used to predict application outcomes, then resource wastage is reduced, but device complexity increases
Solution Approach 1:
The patent implements self-service by enabling the system to automatically analyze historical data, train machine learning models, generate predictions for new applications, and provide actionable insights without requiring external intervention. The system serves itself by continuously learning from past applications and autonomously identifying patterns that predict resource wastage, thereby reducing manual analysis complexity while maintaining resource efficiency.
Data Source
AI summary
A device receives historical application creation data that includes data associated with creation of a plurality of applications, and processes the historical application creation data, with one or more data processing techniques, to generate processed historical application creation data. The device trains a machine learning model, with the processed historical application creation data, to generate a trained machine learning model, and receives new application data associated with a new application to be created. The device processes the new application data, with the trained machine learning model, to generate one or more predictions associated with the new application, and performs one or more actions based on the one or more predictions associated with the new application.