ML Defect Forecasting for Software Deployment Control
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Solution Overview
Problem
Existing software development processes lack effective methods for predicting and controlling software defects during deployment, leading to inefficiencies and increased testing costs.
Innovation Solution
A machine learning-based approach utilizing time series forecasting models, combined with features derived from software stories and defects, to predict and optimize software deployment, incorporating generative AI for sentiment analysis and transfer learning to enhance model accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional software development processes are used without machine learning-based defect prediction, then the deployment process is simple, but software quality deteriorates and testing costs increase
Solution Approach 1:
The system performs preliminary defect prediction using machine learning models before software deployment. Historical defect data and software story data are analyzed in advance to forecast potential defects, allowing the system to identify high-risk software modules beforehand and allocate testing resources proactively, thereby improving software quality without significantly complicating the deployment process.
2Reliability
If machine learning-based defect prediction is implemented, then software quality improves, but computational resources and time are consumed
Solution Approach 1:
The system pre-processes and stores historical defect data and software story data in structured formats before deployment. During the prediction phase, the machine learning model leverages this pre-prepared data with optimized feature engineering, significantly reducing the computational time required for real-time defect prediction while maintaining high software quality assessment accuracy.
3Measurement precision
If comprehensive feature analysis from software stories and defects is performed, then prediction accuracy improves, but data processing complexity increases
Solution Approach 1:
The system extracts and isolates the most critical features from software stories and defect data, such as story complexity metrics, defect density, and code change patterns. By focusing on these key predictive features rather than analyzing all available data, the system achieves high prediction accuracy while reducing the overall data processing complexity and computational burden.
4Productivity
If deployment control based on defect prediction is implemented, then testing costs are reduced, but deployment flexibility decreases
Solution Approach 1:
The system applies deployment control selectively based on defect prediction results. For software modules with low predicted defect risk, the system allows rapid deployment with minimal testing. For high-risk modules, enhanced testing and review processes are applied. This partial application of control measures maintains deployment flexibility for low-risk items while ensuring quality for high-risk items, thereby reducing overall testing costs without significantly compromising deployment adaptability.
Data Source
AI summary
An apparatus comprises at least one processing device configured to generate a first data structure comprising a set of features characterizing software defects encountered and software stories generated for one or more pieces of software over a first period of time. The at least one processing device is also configured to generate, utilizing at least one time series forecasting machine learning model that takes as input the first data structure, a second data structure characterizing predicted software defects for the one or more pieces of software over a second period of time. The at least one processing device is further configured to control deployment, during at least a portion of the second period of time, of at least one of the one or more pieces of software on one or more information technology assets of an information technology infrastructure based at least in part on the second data structure.