Development Analysis System for Productivity-Based Task Scheduling
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Solution Overview
Problem
Conventional data analytics tools fail to provide comprehensive visibility across the software development lifecycle, making it difficult for development teams to measure relationships between activity and impact, establish velocity metrics, and track performance metrics across tools, leading to inaccurate task scheduling.
Innovation Solution
A development analysis system that analyzes code check-ins, user activities, and other features to determine productivity scores, allowing for the scheduling of tasks based on expected productivity levels, and adjusts scores for reworked code to optimize task timing and reduce productivity impact.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional data analytics tools are used to track performance, then individual tool dashboards and reports can be generated, but comprehensive visibility across the software development lifecycle is not provided
Solution Approach 1:
The patent combines multiple software development tools and their data streams into a single comprehensive analytics platform. This integration merges version control data, IDE activity data, task management data, and other development tool data into one unified system that provides end-to-end visibility across the entire software development lifecycle, resolving the information loss problem while managing complexity through consolidation.
Solution Approach 2:
The analytics platform is designed to be universal, supporting multiple data sources and types from various software development tools. It provides multi-functional capabilities including performance tracking, productivity measurement, task scheduling, and predictive analytics, allowing a single platform to serve multiple purposes across different development stages and tools.
2Productivity
If task scheduling is performed without comprehensive productivity metrics, then tasks can be assigned quickly, but scheduling accuracy is reduced
Solution Approach 1:
The system performs preliminary actions by continuously collecting and analyzing development activity data in real-time, building up comprehensive productivity metrics before task scheduling decisions are needed. This ongoing data collection and analysis prepares the system in advance, so when task scheduling is required, accurate productivity metrics are already available, improving scheduling accuracy without adding delay.
Solution Approach 2:
The analytics platform implements feedback mechanisms that continuously monitor development activities and update productivity metrics dynamically. This real-time feedback loop ensures that the most current performance data is available for task scheduling decisions, allowing the system to adapt to changing productivity patterns while maintaining scheduling accuracy.
Data Source
AI summary
Embodiments relate to analyzing developmental progress and productivity of users based upon monitored activity features, and inferring expected levels of productivity for future time intervals. By analyzing code check-ins submitted by a user over a time interval, as other features associated with user activities during the time interval, a metric indicating a level of quality or productivity for the time interval can be determined. Based upon the determined metrics for the time intervals, expected levels of productivity for the user can be inferred for future time intervals. In addition, tasks can be automatically performed to reduce an amount of impact on the productivity of the user during time intervals of expected high productivity, such as scheduling of tasks outside of the time intervals, adjusting certain settings of a user device, and/or the like.