Testing Data Consistency of Data-Intensive Applications Using QuickCheck

被引:5
|
作者
Castro, Laura M. [1 ]
Arts, Thomas [2 ]
机构
[1] Univ A Coruna, Dept Comp Sci, La Coruna, Spain
[2] Chalmers Univ, Comp Sci & Engn, Gothenburg, Sweden
关键词
Software verification; Software testing; Model Based Testing; Software Tools; QuickCheck;
D O I
10.1016/j.entcs.2011.02.010
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Many software systems are data-intensive and use a data management systems for data storage, such as Relational Database Management Systems (RDBMS). RDBMSs are used to store information in a structured manner, and to define several types of constraints on the data, to maintain basic consistency. The RDBMSs are mature, well tested, software products that one can trust to reliably store data and keep it consistent within the defined constraints. There are, however, scenarios in which passing the responsibility of consistency enforcement to the RDBMS is not convenient, or simply not possible. In such cases, the alternative is to have that responsibility at the business logic level of the system. Hence, from the point of view of testing data-intensive applications, one of the most relevant aspects is to ensure correctness of the business logic in terms of data consistency. In this article, we show how QuickCheck, a tool for random testing against specifications, can be used to test the business logic of an application to increase confidence on data integrity. We build an abstract model of the data containing the minimum information necessary to create meaningful test cases, while keeping its state substantially smaller than the data in the complete database. From the abstract model we automatically generate and execute test cases which check that data constraints are preserved.
引用
收藏
页码:41 / 62
页数:22
相关论文
共 50 条
  • [41] MetaFa: Metadata Management Framework for Data Sharing in Data-Intensive Applications
    Ikebe, Minoru
    Inomata, Atsuo
    Fujikawa, Kazutoshi
    Sunahara, Hideki
    DISTRIBUTED COMPUTING, ARTIFICIAL INTELLIGENCE, BIOINFORMATICS, SOFT COMPUTING, AND AMBIENT ASSISTED LIVING, PT II, PROCEEDINGS, 2009, 5518 : 655 - 658
  • [42] A brief survey on big data: technologies, terminologies and data-intensive applications
    Abdalla, Hemn Barzan
    JOURNAL OF BIG DATA, 2022, 9 (01)
  • [43] Distributed data structure templates for data-intensive remote sensing applications
    Ma, Yan
    Wang, Lizhe
    Liu, Dingsheng
    Yuan, Tao
    Liu, Peng
    Zhang, Wanfeng
    CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, 2013, 25 (12): : 1784 - 1797
  • [44] BPELDT - Data-Aware Extension for Data-Intensive Service Applications
    Habich, Dirk
    Richly, Sebastian
    Preissler, Steffen
    Grasselt, Mike
    Lehner, Wolfgang
    Maier, Albert
    EMERGING WEB SERVICES TECHNOLOGY, VOL II, 2008, 2 : 111 - +
  • [45] Data-intensive applications, challenges, techniques and technologies: A survey on Big Data
    Chen, C. L. Philip
    Zhang, Chun-Yang
    INFORMATION SCIENCES, 2014, 275 : 314 - 347
  • [46] A brief survey on big data: technologies, terminologies and data-intensive applications
    Hemn Barzan Abdalla
    Journal of Big Data, 9
  • [47] Data-Intensive Science
    Strawn, George
    IT PROFESSIONAL, 2016, 18 (05) : 66 - 68
  • [48] Supporting data-intensive wireless sensor applications using smart data fragmentation and buffer management
    Masilela, Mbonisi
    Wang, Ju
    Pidaparti, Ramana
    2007 INNOVATIONS IN INFORMATION TECHNOLOGIES, VOLS 1 AND 2, 2007, : 607 - 611
  • [49] Enhancing Accessibility to Data in Data-Intensive Web Applications by Using Intelligent Web Prefetching Methodologies
    Buyuktanir, Tolga
    Sigirci, I. Onur
    Aktas, Mehmet S.
    INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING, 2023, : 1405 - 1438
  • [50] Deadline based scheduling for data-intensive applications in clouds
    Fu Xiong
    Cang Yeliang
    Zhu Lipeng
    Hu Bin
    Deng Song
    Wang Dong
    TheJournalofChinaUniversitiesofPostsandTelecommunications, 2016, 23 (06) : 8 - 15