Error Reproduction via Transactional Dependency Extraction
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Solution Overview
Problem
In database management systems, generating efficient error reproduction scenarios for software defects is challenging due to the complexity of workload capture data and transactional dependencies, which requires accurate and efficient analysis of massive concurrent workloads to recreate error scenarios, often consuming excessive computing resources and time.
Innovation Solution
The approach involves consolidating workload capture data based on transactional dependencies to create a compact data structure, identifying relevant sequences of operations for error reproduction, and optimizing the predecessor search process by grouping related objects and using parallel worker processes to reduce computational overhead, thereby generating accurate and efficient error reproduction scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complete workload capture data is used for error reproduction, then accuracy of error reproduction is improved, but time and computational resources required increase significantly
Solution Approach 1:
The patent extracts and identifies only the subset of workload requests that are transactionally dependent on the error-causing request. By taking out only the relevant requests from the complete workload capture data, the system maintains accurate error reproduction while significantly reducing the time and computational resources needed, as irrelevant requests are excluded from the reproduction scenario.
Solution Approach 2:
The patent segments the complete workload capture data into individual requests and analyzes their transactional dependencies. By dividing the workload into discrete units and identifying only those with direct transactional relationships to the error-causing request, the system creates a segmented reproduction scenario that is both accurate and efficient.
2Measurement precision
If complete workload capture data is used for error reproduction, then accuracy of error reproduction is improved, but computational resources consumed increase significantly
Solution Approach 1:
The patent extracts only the transactionally dependent requests from the complete workload capture data. By removing irrelevant requests that do not contribute to reproducing the error, the system maintains reproduction accuracy while significantly reducing computational resource consumption during the analysis and reproduction phases.
Solution Approach 2:
The patent applies partial action by analyzing only the necessary subset of requests that have transactional dependencies on the error-causing request, rather than performing exhaustive analysis on all workload capture data. This partial analysis approach achieves accurate error reproduction with reduced computational overhead.
3Measurement precision
If transactional dependencies are analyzed for all requests, then accuracy of error reproduction is improved, but complexity of the process increases
Solution Approach 1:
The patent extracts and focuses only on requests with transactional dependencies on the error-causing request, rather than analyzing all requests. This extraction approach maintains high accuracy in error reproduction while simplifying the analysis process by eliminating unnecessary complexity associated with irrelevant requests.
4Measurement precision
If relevant requests are identified through detailed analysis, then accuracy of error reproduction is improved, but time required for analysis increases
Solution Approach 1:
The patent performs preliminary analysis to identify and mark transactionally dependent requests before generating the error reproduction scenario. By conducting this preliminary identification in advance, the system ensures accurate selection of relevant requests while streamlining the subsequent scenario generation process, improving overall productivity.
Solution Approach 2:
The patent extracts the subset of transactionally dependent requests from the complete workload data through preliminary analysis. This extraction creates a refined input set that maintains high accuracy in error reproduction while enabling faster scenario generation by eliminating the need to process irrelevant requests.
Data Source
AI summary
Systems, methods, and computer media are described for creating efficient error reproduction scenarios. Raw workload capture data can be consolidated based on transactional dependence of the requests in the capture data. The consolidated workload capture data can be stored as a separate data structure that can be accessed to identify requests on which a request of interest in the raw workload capture data is transactionally dependent. For a request of interest (e.g., a request that caused an error), a lightweight error reproduction scenario can be generated that includes the identified transactionally dependent requests and excludes unrelated requests.