6 results about "Mutation testing" patented technology

The invention belongs to the field of software testing and defect detection, focuses on reliability verification of a physical simulation engine, is suitable for scenes depending on physical simulation, such as robot simulation, automatic driving testing, unmanned aerial vehicle control, education and scientific research, and particularly relates to a variation-based physical simulation engine defect detection method. Potential defects of core modules of collision detection, mechanical calculation, dynamics and the like are efficiently found through a variation test method. According to the method, a feature library is constructed through static library API analysis and AST / IR code analysis, test code expansion is performed by utilizing a large model, a test driver adaptive to a specific data type is generated, a physical semantic constraint mutation seed is combined, a test is executed through libFuzzer, defect and coverage rate data are analyzed, a use case selection strategy is optimized, and the test efficiency is improved. Defects of core modules such as collision detection and dynamic solution are efficiently detected, and the test pertinence and efficiency are improved.

The invention is applicable to the technical field of software testing, and provides a mutation testing method based on a co-location coverage relationship, which comprises the following steps of: applying a mutation operator to a variable original statement of a tested program to generate a mutation statement, and constructing a corresponding mutation branch, on the premise of keeping the semantics of the original program unchanged, all variation branches are integrated to obtain a new tested program, then the tested program is executed by using a test case set, the coverage information of the test case on the original statement and the variation branches is collected, and whether a co-location coverage relationship exists between the original statement and the variation branches is judged according to the coverage information; on the basis of the coverage relation, it can be directly determined that a test case covers a variable original statement and also covers a variable branch of the variable original statement, and for variants which cannot be judged according to the in-situ coverage relation, weak variation testing is executed, and a variation score is calculated in combination with the actual killing condition. According to the method, the variation test execution amount is remarkably reduced, the test efficiency is improved, the resource consumption is reduced, and meanwhile, high variation scores and test quality are kept.

The application provides a code testing method, a code testing device and a computer storage medium. The code testing method comprises the following steps: performing code mutation testing on a to-be-tested code to generate a target test case; mapping an initial compilation command maintained by a target test framework based on a compiler command mapping rule to obtain a target compilation instruction; calling a corresponding compiler based on the target compilation instruction to compile the to-be-tested code and the target test case to obtain a target execution file; and executing the target execution file in a virtual hardware model integrated in the target test framework and generating a test report corresponding to the to-be-tested code based on an execution result. The code testing efficiency is improved by the code testing method.

The application discloses a deep learning framework mutation testing method based on developer experience, which adopts a mutation operator and a constraint condition generation model based on the professional knowledge of developers to simulate the common operation of developers in the development process and detect more diversified defects in multiple stages (such as model training and inference) of the life cycle of a deep learning model. The method comprises two parts: first, model mutation, which is used to generate mutants; second, defect detection, which is used to perform defect detection. The model mutation comprises the following steps: first, the application adopts two deep Q networks to select a mutation operator and a seed model; then, the seed model is mutated under the constraint of criteria, and the Q network is updated according to the reward calculated by the current target evaluation; then, it filters out the mutants that violate any constraint, and adds the legal mutants to the generated model pool. The process is iterated until the mutation is completed. The defect detection comprises the following steps: first, the application detects the defects exposed in the mutants and records the defect reports. Then, based on the reward, the application selects part of the models and further executes them to detect defects in resource scheduling, execution crash, model performance and output precision. Through the method of the application, meaningless models in defect detection can be filtered, diversified defects can be detected in the execution stage of the model, the effectiveness of defect detection is improved, and the method has important application value and popularization prospect.

This invention discloses a method and apparatus for testing the defense capabilities of a Web Application Firewall (WAF) based on protocol parsing differences, relating to the field of network security technology. The method constructs protocol mutation verification test cases, generates mutation test messages containing non-standard protocol features and verification payloads, and sends them to the backend via both the test link and the baseline link. By comparing the response fingerprint with the expected result, it accurately determines whether the WAF is in a state of successful interception, bypassed, or repaired and bypassed. This invention enables a comprehensive and automated evaluation of the WAF's protocol-layer defense capabilities.

A security detection method for reinforcement learning frameworks based on multi-level operator mutation includes: 1) Reinforcement learning source model training: using the interaction between the reinforcement learning agent and the environment to perform goal-oriented learning, obtaining an initial reinforcement learning agent model M. o ;2) For the original reinforcement learning agent model M o Targeting potential defects; designing mutation operators, and inserting mutation operators of different levels into the original reinforcement learning agent model M. o S3) Modify the test environment design in the initial reinforcement learning environment, and design a mutation score for the reinforcement learning system. The mutation score is the ratio of the number of eliminated mutants to the total number of mutants; S4) Modify the original reinforcement learning agent model M o And the mutation reinforcement learning agent model M M Game tests are conducted in different testing environments, and the generated mutation test scores are compared to determine whether the framework is a vulnerability of this type.