Fuzzing is increasingly being utilized as a method to test the reliability of Deep Learning (DL) systems. In order to detect more errors in DL systems, exploring the internal logic of more DNNs has become the main objective of fuzzing. Despite advancements in the seed selection aspect of fuzzing, considerable opportunities still exist for improving testing efficiency. Current research has issues with the repeated consideration of neurons in the model that will be covered in the future by other seeds, leading to redundant seeds and lower testing efficiency. Additionally, there is a lack of a method to measure the potential of seeds to increase coverage, making it difficult to select the most worthy seeds for mutation in each iteration. We propose an uncovered neurons information based (UNIB) fuzzing method for DNN. UNIB uses clustering methods to organize the seed queue based on initial seed data, aiming to enhance the coverage rate improved in each iteration. It also integrates coverage information from the testing phase to identify the seeds with the greatest potential. The experimental results show that UNIB achieved a higher NC than the second-best method by 1.1% and 3% in LetNet-4 and LetNet-5, respectively. UNIB consistently required the fewest number of iterations to reach the same NC as other methods. For both LetNet-4 and LetNet-5, the adversarial test case sets generated by UNIB exhibited the highest diversity.

Abstract Evacuation scene simulation has become one important approach for public safety decision‐making. Although existing research has considered various factors, including social forces, panic emotions, and so forth, there is a lack of consideration of how complex environmental factors affect human psychology and behavior. The main idea of this paper is to model complex evacuation environmental factors from the perspective of knowledge and explore pedestrians' emergency response mechanisms to this knowledge. Thus, a knowledge‐driven dynamic perception model (KDPM) for evacuation scene simulation is proposed in this paper. This model combines three modules: knowledge dissemination, dynamic scene perception, and stress response. Both scenario knowledge and hazard source knowledge are extracted and expressed. The improved intelligent agent perception model is designed by adopting position determination. Moreover, a general adaptation syndrome (GAS) model is first presented by introducing a modified stress system model. Experimental results show that the proposed model is closer to the reality of real data sets.

Abstract Covering array generation (CAG) is the key research problem in combinatorial testing and is an NP‐complete problem. With the increasing complexity of software under test and the need for higher interaction covering strength t , the techniques for constructing high‐strength covering arrays are expected. This paper presents a hybrid heuristic memetic algorithm named QSSMA for high‐strength CAG problem. The sub‐optimal solution acceptance rate is introduced to generate multiple test cases after each iteration to improve the efficiency of constructing high‐covering strength test suites. The QSSMA method could successfully build high‐strength test suites for some instances where t up to 15 within one day cutoff time and report five new best test suite size records. Extensive experiments demonstrate that QSSMA is a competitive method compared to state‐of‐the‐art methods.

Combinatorial testing (CT) is an effective testing method that can detect failures caused by the interaction of parameters of the software under test (SUT). With the increasing complexity of SUT and the parameters involved, the variable strength test suite supporting high strength interaction is challenging in a practical testing scenario. This paper presents a multi-learning-based quantum particle swarm optimization (IQIPSO) for high and variable strength covering array generation (VSCAG). Specifically, a specially designed data structure and several combination location methods are proposed to support and speed up the high-strength VSCAG. Besides, multi-learning strategies, including Lamarckian and Baldwinian learning, are applied to IQIPSO to address the premature convergence leading to a large test suite size. Studies for parameter settings of IQIPSO are presented systematically. The IQIPSO method successfully builds test suites where strength is up to 15 and totally reports 13 new best test suite size records. Extensive experiments demonstrate that IQIPSO tends to outperform most other existing methods.