Adaptive Hypermutation for Search-Based System Test Generation: A Study on REST APIs with EvoMaster

REST web services are widely popular in industry, and search techniques have been successfully used to automatically generate system-level test cases for those systems. In this article, we propose a novel mutation operator which is designed specifically for test generation at system-level, with a particular focus on REST APIs. In REST API testing, and often in system testing in general, an individual can have a long and complex chromosome. Furthermore, there are two specific issues: (1) fitness evaluation in system testing is highly costly compared with the number of objectives (e.g., testing targets) to optimize for; and (2) a large part of the genotype might have no impact on the phenotype of the individuals (e.g., input data that has no impact on the execution flow in the tested program). Due to these issues, it might be not suitable to apply a typical low mutation rate like 1/ n (where n is the number of genes in an individual), which would lead to mutating only one gene on average. Therefore, in this article, we propose an adaptive weight-based hypermutation, which is aware of the different characteristics of the mutated genes. We developed adaptive strategies that enable the selection and mutation of genes adaptively based on their fitness impact and mutation history throughout the search. To assess our novel proposed mutation operator, we implemented it in the EvoMaster tool, integrated in the MIO algorithm, and further conducted an empirical study with three artificial REST APIs and four real-world REST APIs. Results show that our novel mutation operator demonstrates noticeable improvements over the default MIO. It provides a significant improvement in performance for six out of the seven case studies, where the relative improvement is up to +12.09% for target coverage, +12.69% for line coverage, and +32.51% for branch coverage.

Assembly Quality Inspection of Combine Harvester Based on Whale Algorithm Optimization LSSVM

In order to detect the assembly quality of the combine harvester accurately and effectively, a method for the assembly quality inspection of the combine harvester based on the improved whale algorithm (IWOA) to optimize the least square support vector machine is proposed. Aiming at the characteristics of whale optimization algorithm’s weak search ability and easy maturity, this paper introduces the cosine control factor and the sine time-varying adaptive weight to improve it and uses the benchmark function to verify the general adaptability of the algorithm. Combined with the local mean decomposition (LMD), the assembly quality inspection model of the combine harvester was established and applied to the Dongfanghong 4LZ-9A2 combine harvester for experimental verification. The experimental results show that the IWOA proposed in this paper has better optimization ability and adaptability. The average accuracy of the IWOA model proposed in this paper reaches 90.5%, which is 4% higher than that of the WOA model, and the standard deviation of the average accuracy is reduced by 0.15%, which indicates that the IWOA model has better stability.

Predictive Course Control and Guidance of Autonomous Unmanned Sailboat Based on Efficient Sampled Gaussian Process

In view of the vulnerability of ocean unmanned sailboats to the large lateral velocities due to wind and waves during navigation, this paper proposes a Gaussian Process Model Predictive Control (GPMPC) method based on data-driven learning technique to improve the navigation tracking accuracy of unmanned sailboats. The feature model of the sailing course change subject to the wind and waves is learned from the efficient sampling data. It is then combined with the model predictive control to form the course controller. To reduce the influence of wind and waves disturbances, an adaptive weight term is designed in the object function to improve the tracking accuracy of the model predictive control. The guidance commands received by the model predictive controller take into account the path deviation caused by the current and lateral motion of the ship. The results show that GPMPC has the advantages of fast response time and less overshoot; the unmanned sailboat can better achieve waypoint tracking by learning navigation data.

Automated Diagnosis of Childhood Pneumonia in Chest Radiographs Using Modified Densely Residual Bottleneck-Layer Features

Pneumonia is a severe infection that affects the lungs due to viral or bacterial infections such as the novel COVID-19 virus resulting in mild to critical health conditions. One way to diagnose pneumonia is to screen prospective patient’s lungs using either a Computed Tomography (CT) scan or chest X-ray. To help radiologists in processing a large amount of data especially during pandemics, and to overcome some limitations in deep learning approaches, this paper introduces a new approach that utilizes a few light-weighted densely connected bottleneck residual block features to extract rich spatial information. Then, shrinking data batches into a single vector using four efficient methods. Next, an adaptive weight setup is proposed utilizing Adaboost ensemble learning which adaptively sets weight for each classifier depending on the scores generated to achieve the highest true positive rates while maintaining low negative rates. The proposed method is evaluated using the Kaggle chest X-ray public dataset and attained an accuracy of 99.6% showing superiority to other deep networks-based pneumonia diagnosis methods.