Pull Request Fake-Test Detection Through Changed-File Segmentation
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Solution Overview
Problem
Conventional software testing tools fail to efficiently identify and detect software tests that consistently provide false positive results, leading to inefficient and time-consuming processes.
Innovation Solution
A platform, language, and cloud agnostic fake tests detection module that utilizes processors and memory to analyze code changes, identify disallowed commands, and detect assertions in pull requests, flagging fake tests that always pass.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional tools perform full line by line recursive checks for deny list changes, then they may identify code changes, but they fail to efficiently identify fake tests and the process becomes extremely time consuming and costly
Solution Approach 1:
The patent segments the codebase by focusing only on changed files in pull requests rather than analyzing the entire codebase. It further segments the analysis by specifically targeting test files and looking for fake test patterns, thereby reducing the scope from full recursive checks to targeted analysis of only relevant portions.
Solution Approach 2:
The system performs preliminary filtering by identifying changed files before detailed analysis. It pre-processes the input by obtaining a list of changed files from the pull request, then applies fake test detection only to those specific files, avoiding the need for full line-by-line recursive checks across the entire codebase.
2Reliability
If conventional tools check across all source code to prove conditional issues, then they may identify problems, but the process becomes extremely time consuming and costly
Solution Approach 1:
The patent applies local quality by making the detection process context-aware. It specifically targets test files within changed files in pull requests, applying fake test pattern matching only where relevant. This localized approach maintains detection reliability for fake tests while dramatically improving productivity by avoiding unnecessary analysis of unrelated code.
Solution Approach 2:
The system performs partial action by implementing selective analysis rather than comprehensive checking. It applies fake test detection patterns only to test files that have been modified in the current pull request, rather than checking all source code, thereby achieving sufficient detection coverage with much higher efficiency.
3Ease of operation
If fake tests are not detected, then the testing process continues normally, but false positive results are generated leading to wasted resources
Solution Approach 1:
The system performs preliminary detection of fake tests before the full testing process executes. By analyzing test files for fake test patterns upfront and providing feedback to developers, it prevents wasteful execution of tests that would inevitably produce false positives, thereby conserving computational resources while maintaining operational simplicity.
Solution Approach 2:
The patent converts the potential harm of running fake tests (wasted computational resources) into a benefit by detecting and flagging them beforehand. The harmful fake tests are identified through pattern matching, and this information is used to prevent resource waste by alerting developers to remove or fix the problematic tests before execution.
Data Source
AI summary
Various methods, apparatuses/systems, and media for detecting fake software tests are disclosed. A processor detects changes in a code against a commit timeline and stores historical record of code change differences over time onto a database. An API call is made to obtain from the database, for only the changed files in a given pull request, a list of the change differences which comes as a structured object. The processor converts the structured object into a single string value containing all of the change differences; and searches the string for a set of disallowed commands for checking for assertions in the given pull request which is attempting only to change code with a fake test that always passes the code in connection with testing the application. When such a command is found, comments back to the given pull request indicating that the given pull request contains the fake test.