In response to the complex treatment process and evolving medical needs of multimorbidity , multidisciplinary team (MDT) is dedicated to integrating the diagnosis opinions of experts and providing optimal treatment plans. Reaching consensus on disease treatment plans involves a dynamic and iterative group decision-making process, in which traditional methods for MDT meetings fail to address the standardized decision-making procedure, interactive trust relationships, and fuzzy information integration. Given the challenges, this study proposes a dynamic consensus framework based on dual-path feedback mechanism with q -rung orthopair fuzzy set ( q -ROFS). A hybrid trust evolution model is first established within MDT, in which the trust degree is composed of inherent trust and preference similarity in each round. Then the opinion dynamics model is also introduced to the fuzzy environment. Based on trust evolution and opinion dynamics, the dual-path feedback mechanism is employed to provide references for preference adjustment and weight adjustment. Correspondingly, the calculation methods for consensus measure, preference similarity and alternative selection with q -ROFS are proposed. Additionally, a case study about vascular MDT meeting is used to illustrate the effectiveness of the proposed method. The simulation experiments are performed to verify the impact of consensus threshold, group size, individual self-confidence, and trust evolution on the proposed method. The results of the comparative analysis show that increasing the q value can expand the fuzzy information expression space while ensuring the consensus level, and the proposed method is superior to other methods in terms of more efficient and high-quality consensus results.

The Multi-Agent Path Finding (MAPF) problem, i.e., the scheduling of multiple agents to reach their destinations, has been widely investigated. Testing MAPF systems is challenging, due to the complexity and variety of scenarios and the agents’ distribution and interaction. Moreover, MAPF testing suffers from the oracle problem, i.e., it is not always clear whether a test shows a failure or not. Indeed, only considering whether the agents reach their destinations without collision is not sufficient. Other properties related to the ‘quality’ of the generated paths should be assessed, e.g., an agent should not follow an unnecessarily long path. To tackle this issue, this article proposes MET-MAPF, a Metamorphic Testing approach for MAPF systems. We identified 10 Metamorphic Relations (MRs) that a MAPF system should guarantee, designed over the environment in which agents operate, the behaviour of the single agents and the interactions among agents. Starting from the different MRs, MET-MAPF automatically generates test cases addressing them, so possibly exposing different types of failures. Experimental results show that MET-MAPF can indeed find MR violations not exposed by approaches that only consider the completion of the mission as test oracle. Moreover, experiments show that different MRs expose different types of violations.

The multiagent path finding (MAPF) problem identifies the scheduling of multiple agents simultaneously, such that all of them can reach their targets efficiently. To date, MAPF systems have been assigned important tasks such as traffics and warehouses. It is essential to conduct testing for MAPF systems to detect potential failures. Namely, in an MAPF system, a test case is a specific MAPF scenario, including the initial locations of the agents and the environment for these agents to play in. By testing, we intend to find the scenarios (i.e., test cases) whose executions reveal failures. Testing MAPF systems is challenging due to the complexity of its input and the interactions among multiple agents. This article proposes the testing approach based on the adaptive random testing (ART) for MAPF systems. ART aims to generate new test cases far from the already executed ones. Particularly, to calculate the distance between each pair of test cases, we introduce two metrics, the initial density distribution and the destination density distribution, to characterize the distribution of the agents’ initial and destination nodes, respectively. Benefit from ART, the diversity of the information generated during testing can be improved. Experimental results show that compared with the random testing, our approach can detect more diverse failure-revealing scenarios.

Software defect prediction aims to predict defect-prone code regions automatically before defects are discovered. Accurate prediction helps software practitioners to prioritize their testing efforts. In recent decades, dozens of approaches have been put forward and acquired good results in this field. However, in practical scenarios, many projects have limited labeled instances; more than that, most of these labeled instances are nondefective. The lack of training data and class imbalance problem together bring serious challenges to software defect prediction tasks. So far, few of prevailing approaches can well handle these two difficulties simultaneously. One important reason is that they do not pay adequate attention to several key instances, which are difficult to classify in a small imbalanced dataset. This article introduces the concept of “instance hardness” to integrate various difficulties of imbalance classification tasks. Based on it, a novel imbalance learning framework named self-paced ensemble of ensembles (SPE$^{2}$) is proposed to perform software defect prediction. SPE$^{2}$aims to generate a strong ensemble of ensembles by self-paced harmonizing instance hardness via undersampling. Finally, SPE$^{2}$is extensively compared with eight imbalance learning approaches on ten open-source defect datasets. Experiments indicate that SPE$^{2}$improves the performance and achieves better and more significant F-measure values than its existing counterparts, based on Brunner’s statistical significance test and Cliff’s effect sizes.

Path planning algorithms play critical roles in the systems of robots and unmanned aerial vehicles (UAVs). However, it is always difficult to verify the correctness of the implementations for such algorithms because the "planning oracles", the expected planning results, are usually hard to be obtained for complicate planning tasks. To improve software reliability, in this paper, we present a testing technique for verifying the implementations of graph searching based path planning algorithms deployed on robots and UAVs. Our approach is based on the technique of Metamorphic Testing, which has been shown considerable effectiveness in alleviating the absence of Oracle problems. According to the characteristics of graph searching based path planning problem, we present a framework to systematically design metamorphic relations. Based on the framework, six categories of metamorphic relations are proposed. We conduct the empirical analysis on 21 implements of three different path planning algorithms applied in a released business software project. The experimental results show that our approach can effectively detect dormant faults.