18442933. METHOD FOR AUTOMATED ANALYSIS OF SOFTWARE TESTS simplified abstract (Robert Bosch GmbH)
Contents
METHOD FOR AUTOMATED ANALYSIS OF SOFTWARE TESTS
Organization Name
Inventor(s)
Safouane Sfar of Pfullingen (DE)
METHOD FOR AUTOMATED ANALYSIS OF SOFTWARE TESTS - A simplified explanation of the abstract
This abstract first appeared for US patent application 18442933 titled 'METHOD FOR AUTOMATED ANALYSIS OF SOFTWARE TESTS
The abstract describes a method for the automated analysis of software tests, focusing on identifying errors in software execution based on test logs and error logs.
- The method involves analyzing error logs detailing incorrect software executions and test logs from previous software tests.
- The evaluation of test logs is based on the similarity between the execution context of the incorrect execution and the existing test cases.
- Machine learning is used to assist in the evaluation process, enhancing the accuracy and efficiency of error detection.
Potential Applications: - Quality assurance in software development - Debugging and error detection in software systems - Automation of software testing processes
Problems Solved: - Efficient identification of errors in software execution - Streamlining the software testing and debugging process - Enhancing the overall quality and reliability of software systems
Benefits: - Improved accuracy in error detection - Time and cost savings in software testing - Enhanced overall performance and reliability of software systems
Commercial Applications: Title: Automated Software Testing and Error Detection Technology This technology can be utilized by software development companies to streamline their testing processes, improve the quality of their products, and reduce time-to-market. It can also be integrated into software testing tools and platforms to enhance their capabilities.
Prior Art: Researchers and developers in the field of software testing and quality assurance may find relevant prior art in academic journals, conference papers, and patents related to automated error detection and software testing methodologies.
Frequently Updated Research: Researchers in the field of machine learning and software testing are continuously exploring new techniques and algorithms to improve the efficiency and accuracy of automated error detection systems. Stay updated on recent developments in these areas to enhance the effectiveness of this technology.
Questions about Automated Software Testing and Error Detection Technology: 1. How does machine learning contribute to the evaluation of test logs in this method? Machine learning algorithms are used to analyze the similarity between the execution context of incorrect software executions and existing test cases, improving the accuracy and efficiency of error detection.
2. What are the potential challenges in implementing this automated software testing method in real-world software development environments? Implementing this method may require integration with existing software testing tools and processes, as well as training personnel on how to interpret and act on the results generated by the automated analysis system.
Original Abstract Submitted
A method for the automated analysis of software tests of a software. The method includes: ascertaining an error log about an incorrect execution of the software, wherein the error log specifies an execution context of the incorrect execution; ascertaining test logs that result from a performance of the software tests of the software that preceded the incorrect execution of the software, wherein the software tests include a plurality of existing test cases, through which various functions of the software are tested, wherein the test logs specify a respective execution context of the existing test cases; carrying out an evaluation of the test logs based on the error log, wherein the evaluation takes place based on a similarity of the execution context of the incorrect execution to the respective execution context of the existing test cases, wherein the evaluation takes place at least partially based on machine learning.
