Framework for SQL Error Message Design: A Data-Driven Approach

被引:4
|
作者
Taipalus, Toni [1 ]
Grahn, Hilkka [1 ]
机构
[1] Univ Jyvaskyla, POB 35, FI-40014 Jyvaskyla, Finland
关键词
Structured Query Language; SQL; compiler; error message; database management system; human-computer interaction; human factor; usability; readability; USER ERRORS; QUERY;
D O I
10.1145/3607180
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
Software developers use a significant amount of time reading and interpreting error messages. However, error messages have often been based on either anecdotal evidence or expert opinion, disregarding novices, who arguably are the ones who benefit the most from effective error messages. Furthermore, the usability aspects of Structured Query Language (SQL) error messages have not received much scientific attention. In this mixed-methods study, we coded a total of 128 error messages from eight database management systems (DBMS), and using data from 311 participants, analysed 4,796 queries using regression analysis to find out if and how acknowledged error message qualities explain SQL syntax error fixing success rates. Additionally, we performed a conventional content analysis on 1,505 suggestions on how to improve SQL error messages, and based on the analysis, formulated a framework consisting of nine guidelines for SQL error message design. The results indicate that general error message qualities do not necessarily explain query fixing success in the context of SQL syntax errors and that even some novel NewSQL systems fail to account for basic error message design guidelines. The error message design framework and examples of its practical applications shown in this study are applicable in educational contexts as well as by DBMS vendors in understanding novice perspectives in error message design.
引用
收藏
页数:50
相关论文
共 50 条
  • [21] Frequency-domain data-driven control design in the Loewner framework
    Kergus, P.
    Poussot-Vassal, C.
    Demourant, F.
    Formentin, S.
    IFAC PAPERSONLINE, 2017, 50 (01): : 2095 - 2100
  • [22] Design of big data-driven framework based on manufacturing value chain
    Song, Jingwen
    Wang, Aihui
    Liu, Ping
    Li, Daming
    Han, Xiaobo
    Yan, Yuhao
    2021 INTERNATIONAL CONFERENCE ON ADVANCED MECHATRONIC SYSTEMS (ICAMECHS), 2021, : 70 - 74
  • [23] Data-Driven Strain Sensor Design Based on a Knowledge Graph Framework
    Ke, Junmin
    Liu, Furong
    Xu, Guofeng
    Liu, Ming
    SENSORS, 2024, 24 (17)
  • [24] A Data-Driven Diagnostic Framework for Wind Turbine Structures: A Holistic Approach
    Bogoevska, Simona
    Spiridonakos, Minas
    Chatzi, Eleni
    Dumova-Jovanoska, Elena
    Hoeffer, Rudiger
    SENSORS, 2017, 17 (04)
  • [25] A Data-Driven, Multidimensional Approach to Hint Design in Video Games
    Wauck, Helen
    Fu, Wai-Tat
    IUI'17: PROCEEDINGS OF THE 22ND INTERNATIONAL CONFERENCE ON INTELLIGENT USER INTERFACES, 2017, : 137 - 147
  • [26] Data-driven control design in the Loewner framework: Dealing with stability and noise
    Kergus, Pauline
    Formentin, Simone
    Poussot-Vassal, Charles
    Demourant, Fabrice
    2018 EUROPEAN CONTROL CONFERENCE (ECC), 2018, : 1704 - 1709
  • [27] Deep Optical Coding Design in Computational Imaging: A data-driven framework
    Arguello, Henry
    Bacca, Jorge
    Kariyawasam, Hasindu
    Vargas, Edwin
    Marquez, Miguel
    Hettiarachchi, Ramith
    Garcia, Hans
    Herath, Kithmini
    Haputhanthri, Udith
    Ahluwalia, Balpreet Singh
    So, Peter
    Wadduwage, Dushan N.
    Edussooriya, Chamira U. S.
    IEEE SIGNAL PROCESSING MAGAZINE, 2023, 40 (02) : 75 - 88
  • [28] Data-driven approach to design of passive flow control strategies
    Gomez, F.
    Blackburn, H. M.
    PHYSICAL REVIEW FLUIDS, 2017, 2 (02):
  • [29] An artificial intelligence based data-driven approach for design ideation
    Chen, Liuqing
    Wang, Pan
    Dong, Hao
    Shi, Feng
    Han, Ji
    Guo, Yike
    Childs, Peter R. N.
    Xiao, Jun
    Wu, Chao
    JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION, 2019, 61 : 10 - 22
  • [30] A DATA-DRIVEN DESIGN APPROACH FOR CARBON EMISSION PREDICTION OF MACHINING
    Chen, Yuxuan
    Yan, Wei
    Zhang, Hua
    Liu, Ying
    Jiang, Zhigang
    Zhang, Xumei
    PROCEEDINGS OF ASME 2022 INTERNATIONAL DESIGN ENGINEERING TECHNICAL CONFERENCES AND COMPUTERS AND INFORMATION IN ENGINEERING CONFERENCE, IDETC-CIE2022, VOL 2, 2022,