Testing Graph Database Systems via Graph-Aware Metamorphic Relations

被引:2
|
作者
Zhuang, Zeyang [1 ]
Li, Penghui [1 ]
Ma, Pingchuan [2 ]
Meng, Wei [1 ]
Wang, Shuai [2 ]
机构
[1] Chinese Univ Hong Kong, Hong Kong, Peoples R China
[2] Hong Kong Univ Sci & Technol, Hong Kong, Peoples R China
来源
PROCEEDINGS OF THE VLDB ENDOWMENT | 2023年 / 17卷 / 04期
关键词
D O I
10.14778/3636218.3636236
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Graph database systems (GDBs) have supported many important real-world applications such as social networks, logistics, and path planning. Meanwhile, logic bugs are also prevalent in GDBs, leading to incorrect results and severe consequences. However, the logic bugs largely cannot be revealed by prior solutions which are unaware of the graph native structures of the graph data. In this paper, we propose Gamera (Graph-aware metamorphic relations), a novel metamorphic testing approach to uncover unknown logic bugs in GDBs. We design three classes of novel graph-aware Metamorphic Relations (MRs) based on the graph native structures. Gamera would generate a set of queries according to the graph-aware MRs to test diverse and complex GDB operations, and check whether the GDB query results conform to the chosen MRs. We thoroughly evaluated the effectiveness of Gamera on seven widely-used GDBs such as Neo4j and OrientDB. Gamera was highly effective in detecting logic bugs in GDBs. In total, it detected 39 logic bugs, of which 15 bugs have been confirmed, and three bugs have been fixed. Our experiments also demonstrated that Gamera significantly outperformed prior solutions including Grand, GD-smith and GDBMeter. Gamera has been well-recognized by GDB developers and we open-source our prototype implementation to contribute to the community.
引用
收藏
页码:836 / 848
页数:13
相关论文
共 50 条
  • [21] Graph-Aware, Workload-Adaptive SPARQL Query Caching
    Papailiou, Nikolaos
    Tsoumakos, Dimitrios
    Karras, Panagiotis
    Koziris, Nectarios
    SIGMOD'15: PROCEEDINGS OF THE 2015 ACM SIGMOD INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, 2015, : 1777 - 1792
  • [22] Testing Gremlin-Based Graph Database Systems via Query Disassembling
    Zheng, Yingying
    Dou, Wensheng
    Tang, Lei
    Cui, Ziyu
    Gao, Yu
    Song, Jiansen
    Xu, Liang
    Zhu, Jiaxin
    Wang, Wei
    Jun Wei
    Zhong, Hua
    Huang, Tao
    PROCEEDINGS OF THE 33RD ACM SIGSOFT INTERNATIONAL SYMPOSIUM ON SOFTWARE TESTING AND ANALYSIS, ISSTA 2024, 2024, : 1695 - 1707
  • [23] Testing Graph Database Engines via Query Partitioning
    Kamm, Matteo
    Rigger, Manuel
    Zhang, Chengyu
    Su, Zhendong
    PROCEEDINGS OF THE 32ND ACM SIGSOFT INTERNATIONAL SYMPOSIUM ON SOFTWARE TESTING AND ANALYSIS, ISSTA 2023, 2023, : 140 - 149
  • [24] Knowledge Graph-Aware Deep Interest Extraction Network on Sequential Recommendation
    Wang, Zhenhai
    Xu, Yuhao
    Wang, Zhiru
    Fan, Rong
    Guo, Yunlong
    Li, Weimin
    NEURAL PROCESSING LETTERS, 2024, 56 (04)
  • [25] Efficient flow migration for NFV with Graph-aware deep reinforcement learning
    Sun, Penghao
    Lan, Julong
    Li, Junfei
    Guo, Zehua
    Hu, Yuxiang
    Hu, Tao
    COMPUTER NETWORKS, 2020, 183 (183)
  • [26] Graph-Aware Deep Fusion Networks for Online Spam Review Detection
    He, Li
    Xu, Guandong
    Jameel, Shoaib
    Wang, Xianzhi
    Chen, Hongxu
    IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS, 2023, 10 (05) : 2557 - 2565
  • [27] Graph-aware collaborative reasoning for click-through rate prediction
    Xin Zhang
    Zengmao Wang
    Bo Du
    World Wide Web, 2023, 26 : 967 - 987
  • [28] Machine Reading Comprehension Using Structural Knowledge Graph-aware Network
    Qiu, Delai
    Zhang, Yuanzhe
    Feng, Xinwei
    Liao, Xiangwen
    Jiang, Wenbin
    Lyu, Yajuan
    Liu, Kang
    Zhao, Jun
    2019 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING AND THE 9TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING (EMNLP-IJCNLP 2019): PROCEEDINGS OF THE CONFERENCE, 2019, : 5896 - 5901
  • [29] Graph-aware collaborative reasoning for click-through rate prediction
    Zhang, Xin
    Wang, Zengmao
    Du, Bo
    WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS, 2023, 26 (03): : 967 - 987
  • [30] Train Your Own GNN Teacher: Graph-Aware Distillation on Textual Graphs
    Mavromatis, Costas
    Ioannidis, Vassilis N.
    Wang, Shen
    Zheng, Da
    Adeshina, Soji
    Ma, Jun
    Zhao, Han
    Faloutsos, Christos
    Karypis, George
    MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES: RESEARCH TRACK, ECML PKDD 2023, PT III, 2023, 14171 : 157 - 173