Software Analytics System for Project Completion Estimation
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Solution Overview
Problem
Existing solutions for estimating software development project completion and status lack the ability to compare project data with industry best practices while ensuring data privacy across organizations, and they do not effectively collect and analyze data from various software development tools to provide accurate insights into task completion times.
Innovation Solution
A system that collects data from multiple software development tools, aggregates it across organizations, and stores it securely to derive best practices, which are then compared with individual project data to estimate project completion and status, using a client-server architecture with monitor and aggregator modules to track time spent on activities and generate metrics and best practices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data is collected from multiple software development tools to provide accurate project status estimation, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent introduces a data collector as an intermediary component that automatically gathers data from various software development tools (Jira, Confluence, GitHub, etc.). This mediator simplifies the system architecture by centralizing data collection functions rather than requiring direct integration between multiple tools, thereby improving measurement precision while managing complexity through a dedicated intermediary layer.
Solution Approach 2:
The system employs a universal data collection framework that can interface with multiple different software development tools through standardized APIs. This multi-functional approach allows the same core system to collect data from diverse sources (project management, code repositories, documentation tools) without requiring separate specialized collectors for each tool, thus improving comprehensive measurement capability while controlling system complexity.
2Measurement precision
If project data is compared with industry best practices to evaluate performance, then measurement precision is improved, but loss of information increases due to data privacy concerns
Solution Approach 1:
The patent extracts and aggregates only the necessary statistical metrics and best practice data from individual organization data, while removing and suppressing personally identifiable information and organization-specific sensitive data. This extraction process allows performance evaluation against industry benchmarks to proceed with improved measurement precision, while data privacy is protected by taking out only the essential analytical data and discarding sensitive information.
Solution Approach 2:
The system creates anonymized copies of project data for analysis and comparison purposes, rather than working with raw sensitive data. These copies contain only the aggregated metrics needed for performance evaluation, with all identifying information removed. This copying approach enables accurate performance measurement against best practices while maintaining data privacy, as the original sensitive data remains protected and unexposed.
3Ease of operation
If team members manually fill out timesheets to estimate time spent on projects, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The system implements automatic time tracking by having software development tools themselves record and report time spent on various activities. Instead of relying on manual timesheet entries, the system uses self-service mechanisms where tools like Jira, GitHub, and Confluence automatically log their own usage data, task completion times, and workflow transitions. This self-service approach eliminates the need for manual data entry while providing precise, objective time measurement data.
Solution Approach 2:
The system establishes continuous feedback loops where software development tools automatically report their operational data back to the analytics system in real-time or near-real-time. This feedback mechanism ensures that time tracking data is continuously updated without requiring manual intervention, providing both ease of operation (automatic updates) and high measurement precision (continuous, objective data collection) simultaneously.
Data Source
AI summary
The present invention provides for tracking time spent on various activities in a software development project by one or more users working on the project. The present invention also stores this data to derive metrics and best practices. The metrics and best practices are compared with project data to analyze the current status of the project and to estimate the completion timeframe for the current project.