Gray-box Technique Research Articles

White-Box Fuzzing RPC-Based APIs with EvoMaster: An Industrial Case Study

Remote Procedure Call (RPC) is a communication protocol to support client-server interactions among services over a network. RPC is widely applied in industry for building large-scale distributed systems, such as Microservices. Modern RPC frameworks include, for example, Thrift, gRPC, SOFARPC, and Dubbo. Testing such systems using RPC communications is very challenging, due to the complexity of distributed systems and various RPC frameworks the system could employ. To the best of our knowledge, there does not exist any tool or solution that could enable automated testing of modern RPC-based services. To fill this gap, in this article we propose the first approach in the literature, together with an open source tool, for fuzzing modern RPC-based APIs. The approach is in the context of white-box testing with search-based techniques. To tackle schema extraction of various RPC frameworks, we formulate a RPC schema specification along with a parser that allows the extraction from source code of any JVM RPC-based APIs. Then, with the extracted schema we employ a search to produce tests by maximizing white-box heuristics and newly defined heuristics specific to the RPC domain. We built our approach as an extension to an open source fuzzer (i.e., EvoMaster ), and the approach has been integrated into a real industrial pipeline that could be applied to a real industrial development process for fuzzing RPC-based APIs. To assess our novel approach, we conducted an empirical study with two artificial and four industrial web services selected by our industrial partner. In addition, to further demonstrate its effectiveness and application in industrial settings, we report results of employing our tool for fuzzing another 50 industrial APIs autonomously conducted by our industrial partner in their testing processes. Results show that our novel approach is capable of enabling automated test case generation for industrial RPC-based APIs (i.e., 2 artificial and 54 industrial). We also compared with a simple gray-box technique and existing manually written tests. Our white-box solution achieves significant improvements on code coverage. Regarding fault detection, by conducting a careful review with our industrial partner of the tests generated by our novel approach in the selected four industrial APIs, a total of 41 real faults were identified, which have now been fixed. Another 8,377 detected faults are currently under investigation.

Grey-Box Technique of Software Integration Testing Based on Message

In the software integration testing, the combined exploding problem was appeared for GUI (Graphic User Interface) software with many advantages. Based on the engineering thinking of staging and the feature of GUI software, the grey technique of integration testing for GUI Software based on message is proposed to solve this problem, in which, the white-box method is used to deal with the consistent and unified part and black-box method is mainly engaged to dispose the difficult and varied part of software testing. By analysis of basic GUI software, the application of this grey technique will improve the testing efficiency with 333% according to testing executed number, and the efficiency in terms of testing executed time will be 438%. An actual example in product quality monitoring software has verified the approach and its efficiency of the grey technique.

A dual search-based EPR with self-adaptive offspring creation and compromise programming model selection

Evolutionary polynomial regression (EPR) is extensively used in engineering for soil properties modeling. This grey-box technique uses evolutionary computing to produce simple, transparent and well-structured models in the form of polynomial equations that best explain the observed data. A key task is then to determine mathematical structures for modeling physical phenomena and to select the optimal EPR model. This requires an algorithm to search through the model structure space and successfully produce feasible solutions that honor a set of statistical metrics. The complexity of EPR models increases greatly, however, with the number of polynomial terms used to tune these models. In this paper, we propose an alternative EPR for modeling complex soil properties. We implement a dual search-based EPR with self-adaptive offspring creation as model structure search strategy and couple a compromise programming tool to select a model that is preferred statistically relative to models with different polynomial terms. We illustrate our method using real-world data to improve predictions of optimal moisture content and creep index for soils. Our results demonstrate that the models derived using the proposed methodology can predict soil properties with adequate accuracy, physical meaning and lower number of parameters and input variables.

Testing framework for on-board verification of HLS modules using grey-box technique and FPGA overlays

High-Level Synthesis (HLS) provides a simple way to implement complex applications using Field Programmable Gate Array (FPGA) devices. Unfortunately, this technology introduces non-negligible problems related to verification: speed, accuracy and behavior mismatch between co-simulation and implementation. This paper presents RC-Unity, a heterogeneous unit testing framework that integrates FPGA-in-the-loop devices in order to extend the scope and capabilities of current HLS tools. Verification engineers can focus on the design of the tests while the framework automates the generation of the underlying verification infrastructure, making the testbed reusable across different stages of the design flow as the experiments show.

A Novel Neural Networks Ensemble Approach for Modeling Electrochemical Cells.

Accurate modeling of electrochemical cells is nowadays mandatory for achieving effective upgrades in the fields of energetic efficiency and sustainable mobility. Indeed, these models are often used for performing accurate State-of-Charge (SoC) estimations in energy storage systems used in microgrids or powering pure electric and hybrid cars. To this aim, a novel neural networks ensemble approach for modeling electrochemical cells is proposed in this paper. Herein, the system identification has been faced by means of a gray box technique, in which different and specialized neural networks are used for identifying the unknown internal behaviors of the cell. In particular, the a priori knowledge on the system dynamic is used for defining the network architecture. Specifically, each nonlinear function appearing in the system equations is approximated by a distinct neural network. The proposed model has been validated upon three different data sets both in terms of model accuracy and effectiveness in the SoC estimation task. The achieved performances have been compared with those of other computational intelligence approaches proposed in the literature. The results prove the effectiveness of the gray box scheme, achieving very promising performances in both the system identification accuracy and the SoC estimation task.

Identification and Model Predictive Position Control of Two Wheeled Inverted Pendulum Mobile Robot

In order to predict and analyse the behaviour of a real system, a simulated model is needed. The more accurate the model the better the response is when dealing with the real plant. This paper presents a model predictive position control of a Two Wheeled Inverted Pendulum robot. The model was developed by system identification using a grey box technique. Simulation results show superior performance of the gains computed using the grey box model as compared to common linearized mathematical model.

Catalytic activity evaluation of industrial Pd/C catalyst via gray-box dynamic modeling and simulation of hydropurification reactor

In this paper, dynamic modeling and simulation of the hydropurification reactor in a purified terephthalic acid production plant has been investigated by gray-box technique to evaluate the catalytic activity of palladium supported on carbon (0.5wt.% Pd/C) catalyst. The reaction kinetics and catalyst deactivation trend have been modeled by employing artificial neural network (ANN). The network output has been incorporated with the reactor first principle model (FPM). The simulation results reveal that the gray-box model (FPM and ANN) is about 32 percent more accurate than FPM. The model demonstrates that the catalyst is deactivated after eleven months. Moreover, the catalyst lifetime decreases about two and half months in case of 7 percent increase of reactor feed flowrate. It is predicted that 10 percent enhancement of hydrogen flowrate promotes catalyst lifetime at the amount of one month. Additionally, the enhancement of 4-carboxybenzaldehyde concentration in the reactor feed improves CO and benzoic acid synthesis. CO is a poison to the catalyst, and benzoic acid might affect the product quality. The model can be applied into actual working plants to analyze the Pd/C catalyst efficient functioning and the catalytic reactor performance.

A method for measuring strip temperature in the steel industry

In the steel industry, there are many processes that include measuring and control of temperatures. With higher demand on quality, increased production, and effective energy consumption, the use of noncontact temperature measuring techniques has increased. After the cooling section in a continuous annealing-pickling line, the strip temperature is estimated by using the grey box technique. Temperatures are measured in the cavity between the strip and the roller using radiation thermometers. A model is made for estimating strip temperature using the measured temperatures and knowledge of the physics of the process.