Cause Point Analysis for Static Alarm Prioritization
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Solution Overview
Problem
Manual inspection of static analysis alarms is costly, time-consuming, and tedious, as users must classify each alarm as true or false, with repetitive reviews making the process less interesting and inefficient.
Innovation Solution
A system and method for cause point analysis that identifies and classifies the cause points of alarms generated during static analysis, using metrics like cause point type, contribution score, and spatial proximity to prioritize and rank alarms, allowing for more efficient manual inspection and error identification, with customizable ranking criteria and interactive querying to resolve alarms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual inspection of each alarm is performed to classify true errors from false warnings, then software quality assurance is improved, but time consumption and cost increase significantly
Solution Approach 1:
The alarm inspection process is segmented into multiple phases: automated static analysis generates initial alarms, cause point analysis identifies critical program points, and manual inspection focuses only on high-priority cause points rather than all alarms equally. This segmentation reduces the volume of manual work while maintaining quality assurance.
Solution Approach 2:
Cause point analysis acts as an intermediary between automated static analysis and manual alarm inspection. It processes alarm information and generates prioritized cause points, serving as a mediator that filters and prepares data for manual review, thereby reducing the time burden on inspectors.
2Measurement precision
If comprehensive manual review of all alarms is conducted to ensure no errors are missed, then detection precision is improved, but productivity decreases due to repetitive work
Solution Approach 1:
Different levels of inspection effort are applied to different alarms based on their characteristics. High-priority cause points receive detailed manual review, while lower-priority alarms are handled through automated analysis or simplified review processes. This local quality approach maintains detection precision for critical errors while improving overall productivity.
Solution Approach 2:
The inspection process dynamically adjusts parameters such as review depth, time allocation, and inspection methodology based on alarm priority and cause point characteristics. This allows the system to maintain high detection precision for critical issues while optimizing productivity across the entire alarm set.
3Measurement precision
If detailed analysis of each alarm is performed to accurately identify true errors, then measurement precision is improved, but the complexity of the inspection process increases
Solution Approach 1:
Cause point analysis is performed as a preliminary action before manual alarm inspection. It pre-processes alarm data, identifies critical program points, and generates prioritized lists of cause points that require detailed analysis. This preliminary action simplifies the subsequent manual inspection process while maintaining accuracy.
Solution Approach 2:
The inspection process maintains continuity by seamlessly integrating automated cause point analysis with manual review. The output of automated analysis directly feeds into manual inspection workflows, creating a continuous process that maintains measurement precision without requiring complex transitions or rework between automated and manual phases.
Data Source
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AI summary
A method and system is disclosed herein to identify causes of alarms generated corresponding to each alarm of the static analysis. The system models the basic reasons for alarms as alarm cause points of several types. Further, the system ranks these cause points based on different metrics such as unknown type-based ranking, grouping of cause points based on their lexical similarity or proximity and last but not least contribution score based ranking. The system also uses a workflow in which a user answers queries about the cause points and their answers are used in subsequent round of the static analysis. The collaboration between the answers of the user and the static analysis helps the tool to resolve the unknowns encountered during the analysis and weeding out the alarms. It also helps the user to expedite the manual inspections of alarms.