Software Fault Location Identification via Assertion Risk Scoring
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Solution Overview
Problem
Current software fault detection methods are limited in precision, often resulting in false positives and inefficient use of computing resources, as they primarily focus on whether a test case passes or fails without considering the granular factors affecting assertions.
Innovation Solution
A method that calculates a risk score for lines of code based on their influence on test case assertions, considering both passing and failing test cases, to identify likely fault locations with increased granularity and precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional test case pass/fail analysis is used to identify faults, then the detection process is simple and fast, but the precision is low resulting in false positives
Solution Approach 1:
The patent segments the test case execution analysis into multiple dimensions: (1) identifying affected lines of code that influence assertions, (2) categorizing test cases as passing or failing, (3) calculating risk scores based on multiple factors including assertion influence and test case outcomes. This segmentation transforms a simple pass/fail check into a multi-factor analysis system that improves precision without requiring a single complex method.
Solution Approach 2:
The patent adds new dimensions to traditional fault detection by introducing risk score calculation that considers multiple factors beyond simple pass/fail status. The risk score incorporates: (1) the number of assertions affected by each line of code, (2) whether test cases passing or failing are affected by the line, (3) the proportion of affected assertions. This dimensional expansion transforms 1D pass/fail analysis into multi-dimensional risk assessment, improving detection precision.
2Measurement precision
If comprehensive analysis of all lines of code is performed, then fault detection precision improves, but computing resource consumption increases
Solution Approach 1:
The patent segments the code base by identifying only the affected lines of code that influence test case assertions. Instead of analyzing all lines of code uniformly, the system: (1) traces which lines affect which assertions, (2) focuses risk score calculation only on affected lines, (3) excludes unaffected lines from detailed analysis. This segmentation reduces the analysis scope to only relevant code portions, improving precision while conserving computing resources.
Solution Approach 2:
The patent applies local quality by differentiating the analysis depth applied to different lines of code. Affected lines receive comprehensive risk score calculation considering multiple factors, while unaffected lines are excluded from detailed analysis. The risk score itself provides a quality metric that prioritizes lines most likely to contain faults, allowing resources to be focused on high-priority areas rather than uniformly analyzing all code.
3Reliability
If risk score calculation considers multiple factors including assertion influence, then false positives are reduced, but the calculation complexity increases
Solution Approach 1:
The patent segments the risk score calculation into distinct computational steps: (1) identifying affected lines of code, (2) determining which test cases are affected, (3) categorizing test cases as passing or failing, (4) calculating the risk score based on the proportion of affected assertions. This segmentation of the calculation process makes the complex multi-factor analysis more manageable and implementable, reducing the perceived complexity while maintaining high reliability.
Solution Approach 2:
The risk score calculation incorporates feedback from multiple sources: test case outcomes (pass/fail), assertion results, and the relationship between code lines and assertions. The system uses this feedback to dynamically adjust risk scores, where lines affecting failing assertions receive higher risk scores. This feedback mechanism improves reliability by continuously refining fault probability estimates based on actual test execution results.
Data Source
AI summary
A method may include obtaining multiple lines of programming code of a program, and obtaining multiple test cases for testing the program, where each of the test cases includes an assertion upon which a result of a respective test case is based. The method may also include executing the program for each of the test cases, and identifying affected lines of programming code that influence the assertions. The method may additionally include calculating a risk score for at least one of the lines of programming code based on the affected lines of programming code and the assertion, the risk score indicative of a likelihood that the at least one of the lines of programming code includes a fault.