Fault Localization in Data-Centric Programs via Spectral Differencing
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Solution Overview
Problem
Conventional methods are inadequate for quickly identifying and resolving bugs in production applications, especially when the debugging programmer is not the original coder, and for uncovering unanticipated corner cases in data-centric programs.
Innovation Solution
A method that assimilates a program, models its output behavior, annotates statement occurrences, differs passing and failing spectra to identify semantic differences, and locates faults using these differences, employing semantic and corner-case differencing techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional debugging methods are used, then the debugging process can be performed, but the time required to locate faults is excessive and accuracy is insufficient
Solution Approach 1:
The patent segments the program execution into discrete spectra representing different execution paths and statements. By dividing the program behavior into comparable spectral segments, the system can efficiently identify which specific segment contains the fault through differencing operations, thereby reducing both debugging time and improving localization accuracy.
Solution Approach 2:
The patent introduces spectral representations as an intermediary between program execution and fault analysis. Instead of directly analyzing program statements, the system converts execution behavior into spectral form, performs differencing operations on these spectra, and then maps the differences back to locate faults. This intermediary representation enables more efficient and accurate fault localization.
2Reliability
If conventional testing methods are used, then basic functionality can be verified, but unanticipated corner cases remain undetected
Solution Approach 1:
The patent employs dynamic spectral analysis that adapts to different execution scenarios. By comparing spectra from passing and failing executions dynamically, the system can automatically detect corner cases without requiring预先 defined test cases. This dynamic approach improves reliability for detecting unexpected cases while avoiding the complexity of manually designing comprehensive test suites.
Solution Approach 2:
The system performs self-diagnosis by automatically comparing execution spectra and identifying differences without external intervention. The debugging process serves itself by using the program's own execution behavior as the basis for fault detection, eliminating the need for complex external testing infrastructure and improving corner case detection capability.
3Measurement precision
If detailed statement analysis is performed, then fault precision improves, but the computational overhead increases significantly
Solution Approach 1:
The patent applies partial action by performing differencing operations only on relevant spectral components rather than analyzing entire program executions in detail. By focusing computational effort only on the portions of spectra that differ between passing and failing runs, the system achieves statement-level precision while minimizing overall computational overhead.
Data Source
AI summary
Methods and arrangements for localizing faults in programs. A program is assimilated, the program comprising statements. Output behavior of the statements is modeled, and statement occurrences are annotated. Passing and failing spectra are differenced to yield a difference, and a fault is located via employing the difference.