Fast PokeEMU

Over the past decade, Deep Neural Networks (DNNs) have achieved remarkable progress. However, the quality of such kind of systems is far from perfect. Software test is one of the most effective techniques for finding bugs in DNNs. Test case generation is the key factors of the success of software test. Existing test case generation approaches for DNNs always generate a large number of test cases, most of which do not meet the test requirements or the actual situation. In this paper, we propose CAGTest, a condition-guided adversarial generative testing tool for other DNNs to generate their test inputs to find potential defects. In general, CAGTest can generate test cases conditionally, which is not only efficient, but also does not produce a large number of invalid test cases and reduces the scale of test cases.

Many methods in data structures contain a loop structure on a collection type. These loops result in a large number of test cases and are one of the main obstacles to systematically test these methods. To deal with the loops in methods, in this paper, we propose a novel loop-extended model checking approach, abbreviated as LEMC, to efficiently test whether methods satisfy their own invariant. Our main idea is to combine dynamic symbolic execution with static analysis techniques. Specifically, a concrete execution of the method under test is initially done to collect dynamic execution information, which is used to statically identify the loop-extended similar paths of the concrete execution path. LEMC statically checks and prunes all the states which follow these loop-extended similar paths. The experiments on several case studies show that LEMC can dramatically reduce as many as 90% of the search space and achieve much better performance, compared with the existing approaches such as the Glass Box model checker and Korat.

Web applications are crucial role for daily user activities such as online banking, online shopping and searching. It is important to ensure the reliability and web application testing has been used in finding various faults in order to improve the quality of reliable web services. Among test cases generation approaches, user session based testing is an approach to create test cases with real user data. However, real user data usage is extremely large and executing all the test cases can be time consuming in practice. This paper describes the test cases reduction approach for analyzing and replaying the large number of test cases generated from user session data. The entropy gain theory is applied in test cases reduction process to get the best test suite that covers all user accesses of web application. To evaluate the effectiveness of proposed method, the analytical results are described in terms of URLs coverage, reduction time and test cases reduction rate.

Regression testing and retesting of a modified Web Service can be very costly as it tends to generates large number of test cases. Regression testing cost can be reduced significantly by identifying and testing the modified portion of the Web Service only. This avoids the costly construction of new test cases and the unproductive rerunning of existing test cases when it can be guaranteed that the unmodified code of web service will produce the same results as it produced previously. In this paper, by designing Web service graph we propose an effective method of identifying the modified areas (in this paper area means code of web service to be tested) and use this information to reduce regression testing efforts. In this process, we define and use a set of metrics to categorize the testing of Web service. We demonstrate the applicability of the proposed approach using a case study that consisted of two steps. In the first step, we compared two versions of WSDL's to identify the inserted, modified and deleted areas of the Web services. Next, we use this information to generate the reduced test cases.

With the development of society, humans are becoming more and more dependent on the Internet. And there are exploitable vulnerabilities in network sharing protocol vulnerabilities that will cause great risks to individuals and society. Therefore, vulnerability mining technology has developed into an important research problem in the field of information security. To this end, this paper uses fuzzy testing method for vulnerability mining of network protocols. The fuzzy testing technique performs vulnerability mining by sending a large amount of abnormal data to the test target and monitoring whether the software system is working properly. The vulnerability mining approach in this paper prioritizes the need to analyze and model the protocol format and generate a large number of test cases by using fuzzy test values to vary the boundaries of different parts of the protocol. These test cases are then sent to the test target, and the network state and process state of the test target are monitored in real time. Finally, if the test cases trigger a vulnerability, the system automatically records the test case information as well as the vulnerability information. The test cases evaluated by machine learning as easy to trigger vulnerabilities are sent to the test H-target, which can save the time of vulnerability mining for everyone and improve the efficiency of vulnerability mining. The vulnerability mining technology researched in this paper is of great significance to network security, which can prevent problems before they occur, discover vulnerabilities in the network in time, take effective measures to prevent them, and possibly avoid the spread of some major network vulnerability viruses.

In this paper we describe the design and implementation of an action-driven automation test framework especially for GUI software testing. The idea of action-driven automation test framework comes from the core concept of #x201C;Quality Assurance (QA)". Better quality can be ensured by increasing the coverage of test cases on the software but the process of creating large number of test cases has to be optimized. With this goal the framework was designed to primarily increase the efficiency and flexibility in composing test cases and simplify the process of learning the test cases. This paper describes the background, features, and implementation details of the framework.

A secure software demands effective techniques for vulnerability detection during its development cycle. The practice of detecting security flaws before the deployment phase eliminates the risks that vulnerabilities may impose for the company. Static analysis and dynamic analysis techniques offer two complimentary approaches for checking vulnerabilities. Static analysis involves the scanning of source code or binary eliminating the need of executing it. This approach is fast and has no run time overhead. However, static analysis are quite imprecise and generate huge false positives and false negatives. On the other hand, dynamic analysis involves the running of the software. The problem of false positives and negatives is less in case of dynamic analysis because they analyze by running the test cases. But this approach requires large number of test cases to ensure a certain confidence level in detecting security bugs. This paper describes a methodology which integrates the two approaches in a complimentary manner. It adopts the strengths of the two and eliminates their weaknesses. We are currently dealing with buffer overflow vulnerability with pointer aliasing. However the idea can be extended to other vulnerabilities also for e.g memory related errors, race conditions( Time of Check to Time to Use vulnerability), dangling pointer vulnerability, integer errors etc.

A Systolic Genetic Search for reducing the execution cost of regression testing

The FLUKA general purpose radiation transport Monte Carlo code being developed and maintained by CERN (https://fluka.cern) has adopted modern software development standards including a formal quality assurance process. This includes the FLUKAVAL testing framework that takes into account the specific needs of testing a Monte Carlo radiation transport simulation code. FLUKAVAL allows to submit, process and validate a large number of test cases for a new FLUKA version and compare the results against previous versions and reference data. It produces a quantitative and qualitative comparison and compiles a summary report over all selected test cases, which is reviewed before the new FLUKA version is released. The overall validation requires only few manual steps and makes use of large computing clusters to run simulations in parallel.

A generalized finite-volume framework for the solution of fluid flows at all speeds in complex geometries and on unstructured meshes is presented. Starting from an existing pressure-based and fully-coupled formulation for the solution of incompressible flow equations, the additional implementation of pressure–density–energy coupling as well as shock-capturing leads to a novel solver framework which is capable of handling flows at all speeds, including quasi-incompressible, subsonic, transonic and supersonic flows. The proposed numerical framework features an implicit coupling of pressure and velocity, which improves the numerical stability in the presence of complex sources and/or equations of state, as well as an energy equation discretized in conservative form that ensures an accurate prediction of temperature and Mach number across strong shocks. The framework is verified and validated by a large number of test cases, demonstrating the accurate and robust prediction of steady-state and transient flows in the quasi-incompressible as well as subsonic, transonic and supersonic speed regimes on structured and unstructured meshes as well as in complex domains.

Validation of CFD-BWR, a new two-phase computational fluid dynamics model for boiling water reactor analysis

We present , a fuzzer to generate random word-level model checking problems in Btor2 format. Btor2 is one of the mainstream input formats for word-level hardware model checking and was used in the most recent hardware model checking competition. Compared to bit-level one, word-level model checking is a more complex research field at an earlier stage of development. Therefore, it is necessary to develop a tool that can produce a large number of test cases in Btor2 format to test either existing or under-developed word-level model checkers. To evaluate the practicality of , we tested the state-of-the-art word-level model checkers and with the generated benchmarks. Experimental results show that both tools are buggy and not mature enough, which reflects the practical value of .

Back-to-back testing

A key concern for micro device design is its power consumption. When such a device involves microflows, actively controlling the flow losses often reduces the power requirements. In the present study, a micro synthetic jet is proposed as a flow control device. The method used is an automated design optimization methodology coupled with computational fluid dynamics. Microflows in the Knudsen range of 10−3 to 10−1 are modeled using a Navier-Stokes solver but with slip velocity and temperature jump boundary conditions derived for micro-sized geometries. First, an uncontrolled flow past a backward facing step in a channel is computed. Then, a synthetic jet actuator is placed downstream of the step where the separation occurs. A large number of test cases have been analyzed. It has been observed that the reattachment point of the separated flow and the flow dissipation are quite sensitive to the location and the geometry of the synthetic jet as well as the parameters of the oscillating membrane. The best flow control, defined as the largest decrease in dissipation, is obtained when the actuator cavity width and the membrane oscillation amplitude are increased simultaneously.

A major assumption in the analysis of (s, S) inventory systems with stochastic lead times is that orders are received in the same sequence as they are placed. Even under this assumption, much of the work to date has focused on the unconstrained optimization of the system, in which a penalty cost for unsatisfied demand is assigned. The literature on constrained optimization, wherein a service level requirement needs to be met, is more sparse. In this paper, we consider the constrained optimization problem, where orders are allowed to cross in time. We propose a feasible directions procedure that is simulation based, and present computational results for a large number of test cases. In the vast majority of cases, we come within 5% of estimated optimality.