Execution Trace Diffing for Fast Software Fault Isolation
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Solution Overview
Problem
Identifying and resolving undesired software behaviors, such as execution crashes and performance issues, is challenging due to their rarity and difficulty in reproduction, requiring extensive time and resources for analysis.
Innovation Solution
The method involves diffing prior executions of an executable program based on replayable execution traces, using entity weighting, search space partitioning, and replacement restriction techniques to efficiently calculate edit distances between ordered sets of entities, reducing computational resources needed for analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If conventional debugging and analysis methods are used on replayable execution traces, then diagnostic capability is maintained, but analysis time becomes excessively long (days) due to the vast amount of trace data generated by modern processors
Solution Approach 1:
The execution trace is divided into multiple segments based on function call boundaries. Each segment contains a subset of entities (function calls, instructions, etc.) that can be independently processed. This segmentation allows the diffing algorithm to work on smaller, manageable portions of the trace data rather than the entire trace at once, significantly reducing computation time while maintaining analysis accuracy.
Solution Approach 2:
The patent extracts only the essential entities from the execution trace that are relevant to identifying software faults, such as function calls, kernel calls, and telemetry datapoints. By filtering out redundant information and focusing on critical entities, the analysis process operates on a reduced dataset, thereby decreasing analysis time without sacrificing diagnostic capability.
2Measurement precision
If detailed replayable execution traces are captured to enable precise fault identification, then measurement precision is improved, but the quantity of data to be analyzed increases significantly, requiring excessive computational resources
Solution Approach 1:
The patent extracts and retains only the most relevant entities from the execution trace, such as function calls, kernel calls, and telemetry datapoints, while discarding redundant information. This selective extraction maintains the precision needed for fault identification by preserving critical diagnostic information while reducing the overall data volume that requires computational analysis.
Solution Approach 2:
By segmenting the trace into function-call-based units, the patent organizes detailed trace data into structured, manageable sections. This segmentation allows the system to process and analyze only the relevant portions of the trace associated with potential faults, rather than processing the entire voluminous trace data set, thus reducing computational resource requirements while maintaining analysis precision.
3Reliability
If traditional edit distance algorithms are applied to compare execution traces, then comprehensive comparison is achieved, but computational complexity increases, requiring excessive processing power and time
Solution Approach 1:
The patent applies segmentation to divide the execution traces into smaller segments based on function calls before performing edit distance comparison. This segmentation transforms the complex problem of comparing entire traces into multiple simpler comparisons of smaller segments, significantly reducing computational complexity while maintaining the reliability of the comparison through systematic analysis of all relevant segments.
Solution Approach 2:
The patent applies different weighting schemes to different types of entities within the traces based on their relative importance for fault detection. Critical entities such as function calls and kernel calls are assigned higher weights, while less important entities receive lower weights. This local quality approach allows the edit distance algorithm to focus computational effort on the most diagnostically valuable parts of the trace, improving reliability while reducing overall computational complexity.
Data Source
AI summary
Diffing prior executions of an executable program. A computer system assigns a weighting to each entity type in a first ordered set of a first plurality of entities and a second ordered set of a second plurality of entities, including assigning different weightings to different entity types. The computer system calculates an edit distance between the first and second ordered sets based on calculating a minimal cost path through a diffing structure. Calculating the minimal cost path includes partitioning the diffing structure around intersecting entries that are identified based on (i) only permitting replacements between entities that have a common entity type, and (ii) utilizing the different weightings to identify single-entity replacements that have higher cost than adjoining multi-entity replacements. Calculating the minimal cost path also includes applying a differencing algorithm to these partitions, in order to identify corresponding segments of the minimal cost path.