Node.js has become one of the most popular development platforms due to its superior concurrency support. However, races induced by the nondeterministic execution order of event handlers may occur in Node.js applications, causing serious runtime failures. The state-of-the-art Node.js race detector NRace builds a happens-before (HB) graph before detection with a set of HB relation rules. In detection, NRace utilizes a heavy-weight BFS-based algorithm to query the reachability between resource operations, which introduces substantial overhead in practice, causing NRace inapplicable to real-world Node.js application test processes. This paper proposes a more practical Node.js dynamic race detection approach called NodeRT (Node.js Race Tracker). To reduce unnecessary overhead, NodeRT simplifies the HB relation rules, and divides the detection into three stages: trace collection stage, race candidate detection stage, and false positive removal stage. In the trace collection stage, NodeRT constructs a partial HB graph called asynchronous call tree (ACTree), enabling efficient reachability queries between event handlers. In the race candidate detection stage, NodeRT performs detection on the ACTree, which effectively eliminates most non-racing event handlers and outputs race candidates. In the false positive removal stage, NodeRT utilizes matching rules derived from HB relation rules and features of resources to reduce false positives in the race candidates. In experiments, NodeRT detects all known races and 9 unknown harmful races in real-world applications, whereas NRace only detects 3 of the unknown harmful races, with 64× more time consumption on average. Compared with NRace, NodeRT significantly reduces the overhead, making it practical to be integrated into real-world test processes.

Most recently proposed approaches in dialogue state tracking (DST) leverage the context and the last dialogue states to track current dialogue states, which are often slot-value pairs.Although the context contains the complete dialogue information, the information is usually indirect and even requires reasoning to obtain.The information in the lastly predicted dialogue states is direct, but when there is a prediction error, the dialogue information from this source will be incomplete or erroneous.In this paper, we propose the Dialogue State Tracking with Multi-Level Fusion of Predicted Dialogue States and Conversations network (FPDSC).This model extracts information of each dialogue turn by modeling interactions among each turn utterance, the corresponding last dialogue states, and dialogue slots.Then the representation of each dialogue turn is aggregated by a hierarchical structure to form the passage information, which is utilized in the current turn of DST.Experimental results validate the effectiveness of the fusion network with 55.03% and 59.07%joint accuracy on MultiWOZ 2.0 and MultiWOZ 2.1 datasets, which reaches the state-of-the-art performance.Furthermore, we conduct the deleted-value and related-slot experiments on MultiWOZ 2.1 to evaluate our model.