Machine Learning SDLC Anomaly Detection With Adaptive Kill Switches
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Solution Overview
Problem
Conventional SDLC approaches in large enterprises face challenges in achieving comprehensive end-to-end traceability of software feature changes, leading to software delivery risks, quality impacts, and increased likelihood of production incidents due to inconsistent data collection and manual processes, which hinder effective risk management and compliance.
Innovation Solution
A machine learning-powered system that integrates data from various SDLC tools, provides a single pane of glass view, and generates actionable recommendations, including a kill switch mechanism to manage risks proactively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual data collection processes are used in SDLC, then implementation simplicity is maintained, but traceability reliability deteriorates due to inconsistent data collection
Solution Approach 1:
The patent replaces manual data collection processes with machine learning-based automated anomaly detection systems. The ML models automatically analyze SDLC data from multiple sources (code repositories, issue trackers, CI/CD pipelines) to identify anomalies and generate traceability information, eliminating the need for manual data gathering while ensuring consistent and reliable traceability across the software development lifecycle.
Solution Approach 2:
The system enables self-service anomaly detection by implementing ML models that autonomously collect, analyze, and interpret SDLC data without requiring manual intervention. The models automatically process data from integrated tools, identify patterns and anomalies, and generate actionable insights, allowing the system to serve itself in maintaining traceability while reducing dependency on manual processes.
2Measurement precision
If comprehensive end-to-end traceability is implemented, then measurement precision improves, but data collection complexity increases
Solution Approach 1:
The patent segments the complex traceability problem into manageable components by implementing separate ML models for different anomaly detection tasks (code quality anomalies, process flow anomalies, risk prediction). Each model focuses on specific aspects of SDLC data, processing information from particular tool integrations independently before synthesizing comprehensive traceability insights, thereby reducing overall system complexity while maintaining high measurement precision.
Solution Approach 2:
The system introduces ML-based anomaly detection models as intermediary layers between raw SDLC data sources and traceability outputs. These intermediary models automatically process and filter data from multiple integrated tools (version control systems, issue trackers, build servers), transforming raw data into structured anomaly information that enhances traceability precision without requiring direct complex integration between all data sources.
3Productivity
If machine learning models are deployed for anomaly detection, then productivity improves through automation, but device complexity increases
Solution Approach 1:
The patent implements universal ML models that perform multiple functions within the SDLC anomaly detection system. The same model architecture processes different types of data (code commits, issue resolutions, build results) and detects various anomaly types, generating comprehensive traceability information. This multi-functionality increases productivity by consolidating multiple detection capabilities into unified models while managing system architecture complexity through standardized processing pipelines.
4Adaptability or versatility
If dynamic thresholds are implemented for anomaly detection, then adaptability improves, but measurement precision requirements increase
Solution Approach 1:
The patent implements dynamic threshold mechanisms where ML models automatically adjust anomaly detection thresholds based on learned patterns from historical SDLC data. The system adapts to different project contexts, team behaviors, and development processes by continuously learning from data and adjusting sensitivity parameters, thereby improving detection adaptability while using the ML models themselves to manage the precision requirements through automated calibration rather than manual threshold setting.
Data Source
AI summary
Systems and methods for enhanced software development lifecycle management using a pipeline of machine learning and adaptive models for anomaly detection and termination. In some aspects, the system may generate a system data stream for an SDLC management platform that stitches together source data from multiple sources, generates a query to a first machine learning model for a user story identifier to generate trend information and anomalies, based on output from a second machine learning model including dynamic thresholds for the anomalies, determining at least one anomaly that satisfies a corresponding dynamic threshold, based on output from an adaptive model not including recommended actions to address the at least one anomaly, executing a kill switch for the corresponding user story identifier and generating for inclusion in the graphical user interface an indication of the kill switch.