Software Analytics Platform for Defect Identification
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Solution Overview
Problem
Current software development techniques are inadequate in determining the expected behavior of software programs, leading to inefficiencies in defect identification and resolution, especially in multi-platform environments where errors are more critical due to the subjective nature of expected behavior and the lack of readily available information.
Innovation Solution
An automated software analytics platform that utilizes debug and error logs to generate event records, identify patterns, and perform data analytics to determine the expected behavior of software programs, thereby identifying defects and generating reports for quick resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated data analytics is performed on event records to determine expected behavior, then defect identification capability is improved, but computational resource consumption increases
Solution Approach 1:
The system performs preliminary actions by collecting and storing event records during software execution before actual defect analysis is needed. Event records are generated and archived in advance, allowing the analytics engine to work with pre-processed data rather than raw execution traces, reducing real-time computational burden while maintaining defect detection accuracy
Solution Approach 2:
The system extracts only the necessary event records and pattern information relevant to defect detection from the complete software execution data. By filtering and selecting specific events that indicate potential defects rather than analyzing all execution events, the system reduces computational resource consumption while preserving the ability to identify software defects accurately
2Reliability
If comprehensive event records are collected from multiple platforms, then software quality assessment is improved, but data processing complexity increases
Solution Approach 1:
The system implements a universal event record format and analytics framework that can process data from multiple software platforms simultaneously. By designing platform-agnostic event structures and standardized processing pipelines, the system handles multi-platform data without proportionally increasing processing complexity, enabling comprehensive software quality assessment across diverse environments
Solution Approach 2:
The system changes parameters by transforming raw multi-platform event data into standardized patterns and metrics that are platform-independent. By converting diverse event formats into uniform analytical representations, the system maintains reliable software quality assessment across platforms while reducing the complexity of handling heterogeneous data sources
Data Source
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AI summary
A device may receive information associated with a software program executing on a device, and may generate event records based on the information associated with the software program. The device may identify pattern information associated with the event records, and may perform data analytics on the event records and the pattern information to identify one or more defects associated with the software program and to determine an expected behavior of the software program. The device may generate one or more reports based on the one or more defects associated with the software program and the expected behavior of the software program, and may provide the one or more reports.