Code Instrumentation for Regression Detection
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Solution Overview
Problem
Software development organizations face challenges in identifying and pinpointing regressions in code changes early in the development process, which can lead to errors, inefficiencies, and performance issues.
Innovation Solution
The technique involves instrumenting compiled code with annotations to generate timing information, comparing it to baseline data, and taking actions based on differences found, such as rejecting code changes or generating reports, to detect and address regressions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual testing and code review are used to identify regressions, then measurement precision can be maintained, but productivity decreases and loss of time increases
Solution Approach 1:
The system performs preliminary instrumentation of code with timing annotations before execution, establishing baseline timing information in advance. This allows automatic comparison against future changes without requiring manual re-testing, thereby accelerating regression detection while maintaining precision through automated baseline comparisons.
Solution Approach 2:
The system automatically compares timing information from executed code against stored baseline data and provides immediate feedback when regressions are detected. This closed-loop feedback mechanism enables rapid identification of performance regressions without manual intervention, significantly reducing the time to pinpoint issues while maintaining detection accuracy through automated measurement comparison.
2Measurement precision
If code instrumentation with timing annotations is added, then measurement precision improves for detecting execution time changes, but device complexity increases
Solution Approach 1:
The instrumentation process segments code into distinct sections with specific timing annotations placed at defined boundaries. This segmentation approach allows precise measurement of individual code sections without requiring comprehensive instrumentation of entire systems, thereby achieving high measurement precision while managing complexity through targeted annotation placement.
Solution Approach 2:
The system creates a copy of the instrumentation framework that can be applied to different code bases without modifying the original code structure. This templated copying approach allows precise timing measurements to be replicated across multiple projects using the same annotation pattern, achieving measurement precision while reducing complexity through reuse of proven instrumentation templates.
3Productivity
If automatic comparison of timing information is implemented, then productivity increases, but device complexity increases
Solution Approach 1:
The timing comparison system is designed as a universal framework that can compare timing information across different code versions, projects, and execution contexts using the same comparison logic. This multi-functional design enables automated regression detection throughout the software development lifecycle without requiring project-specific complex implementations, thereby improving productivity while managing complexity through standardized comparison mechanisms.
Data Source
AI summary
Techniques are described for detecting the presence of regressions in software, such as software that is being developed and tested in a development environment. Compiled code can be instrumented to introduce annotations to the compiled code. For example, the annotations can be added at the start and end of a code section, where a code section can be specified at any suitable degree of specificity (e.g., a line of code, a function, a component, multiple components, and so forth). When the instrumented code is executed, the annotations generate timing information that describes the execution times of the various annotated code sections. The timing information can be analyzed and compared to previously generated timing information for the code base, and detected differences may trigger actions such as reports, alerts, denied requests to pull code from the source repository, rejected code check-ins, bug reports, and so forth.