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44th ACM/IEEE International Conference on software Engineering, ICSE 2024

作者： Wang, Jun Li, Yanhui Chen, Zhifei Chen, Lin Zhang, Xiaofang Zhou, Yuming Nanjing University State Key Laboratory for Novel Software Technology China School of Computer Science and Engineering Nanjing University of Science and Technology China School of Computer Science and Technology Soochow University China

ISBN: (纸本)9798400702174

In the wake of developments in the field of Natural Language Processing, Question Answering (QA) software has penetrated our daily lives. Due to the data-driven programming paradigm, QA software inevitably contains bugs, i.e., misbehaving in real-world applications. Current testing techniques for testing QA software include two folds, reference-based testing and metamorphic testing. This paper adopts a different angle to achieve testing for QA software: we notice that answers to questions would have inference relations, i.e., the answers to some questions could be logically inferred from the answers to other questions. If these answers on QA software do not satisfy the inference relations, an inference bug is detected. To generate the questions with the inference relations automatically, we propose a novel testing method Knowledge Graph driven Inference Testing (KGIT), which employs facts in the Knowledge Graph (KG) as the seeds to logically construct test cases containing questions and contexts with inference relations. To evaluate the effectiveness of KGIT, we conduct an extensive empirical study with more than 2.8 million test cases generated from the large-scale KG YAGO4 and three QA models based on the state-of-the-art QA model structure. The experimental results show that our method (a) could detect a considerable number of inference bugs in all three studied QA models and (b) is helpful in retraining QA models to improve their inference ability. © 2024 ACM.

关键词： Knowledge graph

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Simulation Might Change Your Results: A Comparison of Context-Aware System Input Validation in Simulated and Physical Environments

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Journal of computer science & technology 2022年第1期37卷 83-105页

作者： Jin-Chi Chen Yi Qin Hui-Yan Wang Chang Xu State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China

Context-aware systems(***)integrate cyber and physical space to provide adaptive functionalities in response to changes in *** context-aware systems is challenging due to the uncertain running ***,many input validation approaches have been proposed to protect context-aware systems from uncertainty and keep them executing ***,in contrast to context-aware systems'prevailing in physical environments,most of those academic solutions(83%)are purely evaluated in simulated *** this article,we study whether this evaluation setting could lead to biased *** build a testing platform,RM-Testing,based on DJI RoboMaster robot car,to conduct the physical-environment based *** select three up-to-date input validation approaches,and compare their performance in the simulated environment and in the physical *** experimental results show that all three approaches'performance in simulated environments(improving task success rate by 82%compared with the system without the support of input validation)does differ from their performance in a physical environment(improving the task success rate by 50%).We also recognize three factors(scenario setting,physical platform and environmental model)that affect the performance of input validation approaches,based on an execution model of the context-aware system.

关键词： context-aware system input validation self-driving car testing infrastructure

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Leveraging TLA+ Specifications to Improve the Reliability of the ZooKeeperCoordination Service 9th

Leveraging TLA+ Specifications to Improve the Reliability ...

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9th International Symposium on Dependable software Engineering: Theories, Tools and Applications, SETTA 2023

作者： Ouyang, Lingzhi Huang, Yu Huang, Binyu Ma, Xiaoxing State Key Laboratory for Novel Software Technology Nanjing210023 China

ISBN: (纸本)9789819986637

ZooKeeper is a coordination service, widely used as a backbone of various distributed systems. Though its reliability is of critical importance, testing is insufficient for an industrial-strength system of the size and complexity of ZooKeeper, and deep bugs can still be found. To this end, we resort to formal TLA+ specifications to further improve the reliability of ZooKeeper. Our primary objective is usability and automation, rather than full verification. We incrementally develop three levels of specifications for ZooKeeper. We first obtain the protocol specification, which unambiguously specifies the Zab protocol behind ZooKeeper. We then proceed to a finer grain and obtain the system specification, which serves as the super-doc for system development. In order to further leverage the model-level specification to improve the reliability of the code-level implementation, we develop the test specification, which guides the explorative testing of the ZooKeeper implementation. The formal specifications help eliminate the ambiguities in the protocol design and provide comprehensive system documentation. They also help find critical deep bugs in system implementation, which are beyond the reach of state-of-the-art testing techniques. Our specifications have been merged into the official Apache ZooKeeper project. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Model checking Specification TLA+ Zab ZooKeeper

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A Contrastive Collaborative Filtering Method for Personalized Recommendation with Self-Attention 22

A Contrastive Collaborative Filtering Method for Personalize...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Chen, Cheng Tian, Hao Zhang, Hang Dou, Wanchun Nanjing University State Key Laboratory for Novel Software Technology China

ISBN: (纸本)9798331509712

Recently, sequential and Collaborative Filtering (CF) based recommender systems have shown their research popularity in both academia and industry. Graph-based CF and Transformer-based sequential models have independently shown state-of-art recommendation performance respectively. However, each approach has its own limitation and retrieves different potential factors from users and items. CF aims to retrieve collaborative signals between users and items while Transformer models the temporal dependencies between user historical actions. The under utilization of latent factors can limit the performance and generalization of recommender systems. To address the above limitation, in this paper, we proposed a novel multi-view recommender method called Contrastive Attentive Collaborative Filtering (CACF) that combines collaborative signals and temporal dependencies. Our method leverages the strengths of both approaches by combining the temporal dependecies captured by the Transformer with the user-item collaborative signals retrieved by graph convolutional network (GCN). The Transformer learns temporal dependencies and generates embeddings of items while GCN component capture collaborative signals. By contrastivly optimizing output embeddings from both methods, our approach aims to provide more accurate and personalized recommendations. Finally, we evaluate our method on several benchmark datasets and compare its performance with state-of-the-art recommendation systems. The experimental results show that our method outperforms in several evaluation metrices. © 2024 IEEE.

关键词： Contrastive Learning

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Mining Long Short-Term Evolution Patterns for Temporal Knowledge Graph Reasoning 27th

Mining Long Short-Term Evolution Patterns for Temporal Know...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Wang, Fengyi Zhu, Guanghui Hou, Haojun Yuan, Chunfeng Huang, Yihua State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (纸本)9783031781278

Temporal knowledge graphs (TKGs), consisting of graph snapshots evolving over time, have attracted substantial research attention in various areas like recommender systems and relation networks. Although significant research efforts have been devoted to this field, the regularity and abruptness of graph evolution make it still challenging to model temporal dynamics in TKGs. Existing works only characterize the short-term evolution due to the limitations of modeling long sequences, or lack consideration of fine-grained differences between recent snapshots in time. To tackle the issues, we propose LSEN (Long Short-Term Evolution Learning Network), an effective model that jointly captures short-term and long-term evolution patterns in TKGs. By introducing a short-term and a long-term evolution pattern mining module, we boost the memorization and generalization of LSEN for TKG reasoning. Specifically, the short-term module of LSEN simultaneously consider the topology structure at each moment and model the chronologically sequential effect across recent snapshots. In addition, we devise a long-term module leveraging the frequencies of constrained triple occurrences to explore long-term evolution patterns in the entire historical sequence. We conduct extensive experiments on five real-world datasets, and LSEN achieves state-of-the-art results, demonstrating the significant superiority of our method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Knowledge graph

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Ordered Momentum for Asynchronous SGD 38

Ordered Momentum for Asynchronous SGD

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Shi, Chang-Wei Yang, Yi-Rui Li, Wu-Jun National Key Laboratory for Novel Software Technology School of Computer Science Nanjing University Nanjing China

Distributed learning is essential for training large-scale deep models. Asynchronous SGD (ASGD) and its variants are commonly used distributed learning methods, particularly in scenarios where the computing capabilities of workers in the cluster are heterogeneous. Momentum has been acknowledged for its benefits in both optimization and generalization in deep model training. However, existing works have found that naively incorporating momentum into ASGD can impede the convergence. In this paper, we propose a novel method called ordered momentum (OrMo) for ASGD. In OrMo, momentum is incorporated into ASGD by organizing the gradients in order based on their iteration indexes. We theoretically prove the convergence of OrMo with both constant and delay-adaptive learning rates for non-convex problems. To the best of our knowledge, this is the first work to establish the convergence analysis of ASGD with momentum without dependence on the maximum delay. Empirical results demonstrate that OrMo can achieve better convergence performance compared with ASGD and other asynchronous methods with momentum. © 2024 Neural information processing systems foundation. All rights reserved.

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BOOSTING GRAPH ANOMALY DETECTION WITH ADAPTIVE MESSAGE PASSING 12

BOOSTING GRAPH ANOMALY DETECTION WITH ADAPTIVE MESSAGE PASSI...

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12th International Conference on Learning Representations, ICLR 2024

作者： Chen, Jingyan Zhu, Guanghui Yuan, Chunfeng Huang, Yihua State Key Laboratory for Novel Software Technology Nanjing University China

Unsupervised graph anomaly detection has been widely used in real-world applications. Existing methods primarily focus on local inconsistency mining (LIM), based on the intuition that establishing high similarities between abnormal nodes and their neighbors is difficult. However, the message passing employed by graph neural networks (GNNs) results in local anomaly signal loss, as GNNs tend to make connected nodes similar, which conflicts with the LIM intuition. In this paper, we propose GADAM, a novel framework that not only resolves the conflict between LIM and message passing but also leverages message passing to augment anomaly detection through a transformative approach to anomaly mining beyond LIM. Specifically, we first propose an efficient MLP-based LIM approach to obtain local anomaly scores in a conflict-free way. Next, we introduce a novel approach to capture anomaly signals from a global perspective. This involves a hybrid attention based adaptive message passing, enabling nodes to selectively absorb abnormal or normal signals from their surroundings. Extensive experiments conducted on nine benchmark datasets, including two large-scale OGB datasets, demonstrate that GADAM surpasses existing state-of-the-art methods in terms of both effectiveness and efficiency. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Message passing

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Enhancing Complex Question Answering over Knowledge Graphs through Evidence Pattern Retrieval 24

Enhancing Complex Question Answering over Knowledge Graphs t...

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33rd ACM Web Conference, WWW 2024

作者： Ding, Wentao Li, Jinmao Luo, Liangchuan Qu, Yuzhong State Key Laboratory for Novel Software Technology Nanjing University China

ISBN: (纸本)9798400701719

Information retrieval (IR) methods for KGQA consist of two stages: subgraph extraction and answer reasoning. We argue that current subgraph extraction methods underestimate the importance of structural dependencies among evidence facts. We propose Evidence Pattern Retrieval (EPR) to explicitly model the structural dependencies during subgraph extraction. We implement EPR by indexing the atomic adjacency pattern formed by resource pairs. Given a question, we perform dense retrieval to obtain atomic patterns. We then enumerate their combinations to construct candidate evidence patterns. These evidence patterns are scored using a neural model, and the best one is selected to extract a subgraph for downstream answer reasoning. Experimental results demonstrate that the EPR-based approach has significantly improved the F1 scores of IR-KGQA methods by over 10 points on ComplexWebQuestions and achieves competitive performance on WebQuestionsSP. © 2024 ACM.

关键词： Information retrieval

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PredRacer: Predictively Detecting Data Races in Android Applications 31

PredRacer: Predictively Detecting Data Races in Android Appl...

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31st IEEE International Conference on software Analysis, Evolution and Reengineering, SANER 2024

作者： Guo, Xin Qi, Xiaofang Li, Yanhui Wu, Chao School of Computer Science and Engineering Southeast University China Nanjing University State Key Laboratory for Novel Software Technology China

ISBN: (纸本)9798350330663

Android platform offers a hybrid concurrency model encompassing multi-threading and asynchronous messaging for concurrent programming. The model is powerful but complex, making it difficult for developers to analyze concurrent behaviors. Data race, a prevalent concurrency defect in real-world Android applications, often results in abnormal executions of mobile applications, even crashes. Despite quite a few studies on detecting data races, it still suffers from high false positives with static analysis techniques and high false negatives with dynamic analysis techniques. To address this issue, this paper presents a predictive approach, PredRacer, for detecting data races in Android applications. It first captures an execution trace of an Android application, and then reorders the events within the trace based on partial orders. Finally, it checks the feasibility of the generated event sequence, which contains a potential data race. PredRacer increases the search scope and reduces false negatives while mitigating false positives by incorporating the happen-before relations specific to the Android concurrency model. The effectiveness of PredRacer is evaluated using the BenchERoid data set. Experimental results demonstrate that PredRacer achieves high precision, recall, and F1 score, outperforming the state-of-the-art techniques. A collection of 20 open-source Android applications is further utilized to assess the effectiveness of PredRacer, and an evaluation of 300 wild apps is conducted to assess its efficiency and scalability. © 2024 IEEE.

关键词： Static analysis

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A Blockchain System for Clustered Federated Learning with Peer-to-Peer Knowledge Transfer 50th

A Blockchain System for Clustered Federated Learning with Pe...

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50th International Conference on Very Large Data Bases, VLDB 2024

作者： Wu, Honghu Zhu, Xiangrong Hu, Wei State Key Laboratory for Novel Software Technology Nanjing University China

Federated Learning (FL) is a novel distributed, privacy-preserving machine learning paradigm. Conventional FL suffers from drawbacks such as single point of failure and client drift. Blockchain is a distributed computing architecture famous for decentralization, transparency, and traceability. Incorporating blockchain as the underlying basis for FL decentralizes the FL process and brings opportunities to resolve the drawbacks. However, there still remain challenges to fulfilling FL with blockchain, regarding effectiveness, efficiency, and security. In this paper, we propose a new blockchain system for FL, called FedChain. To mitigate client drift and accelerate training, we present a clustered semi-asynchronous method for model aggregation. To optimize the local training in FL, we introduce a knowledge transfer method using other clients on the peer-to-peer network of blockchain. Moreover, we implement an access control mechanism to store and transmit models safely and efficiently. Extensive experiments on various benchmark datasets show that FedChain achieves superior results in accuracy, convergence, throughput, and latency. © 2024, VLDB Endowment. All rights reserved.

关键词： Blockchain

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