TY - GEN
T1 - 'What You See Is What You Test'
T2 - 24th IEEE International Conference on Information Reuse and Integration for Data Science, IRI 2023
AU - Peng, Zedong
AU - Savolainen, Juha
AU - Zhang, Jianzhang
AU - Niu, Nan
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Requirements-based testing (RBT) advocates the design of test cases in order to adequately exercise the behavior of a software system without regard to the internal details of the implementation. To address the challenge that requirements descriptions may be inaccurate in practice, we align requirements engineering and software testing in a novel way by not counting on a complete and up-to-date requirements documentation. Rather, we maintain the black-box nature of RBT to recommend features as the units of testing from software's graphical user interfaces (GUIs). In particular, we exploit optical character recognition (OCR) to identify the textual information from GUIs, and further build the GUI-feature correspondences based on software's user-centric documentation which may exhibit partial correctness. Such correspondences from multiple software systems in the same domain serve as a foundation for our recommendation engine, which suggests the to-be-tested features related to a given GUI. We demonstrate our recommender's feasibility with a study of five products in the web conferencing domain, and the results show the more complete set of features against which a GUI needs to be tested.
AB - Requirements-based testing (RBT) advocates the design of test cases in order to adequately exercise the behavior of a software system without regard to the internal details of the implementation. To address the challenge that requirements descriptions may be inaccurate in practice, we align requirements engineering and software testing in a novel way by not counting on a complete and up-to-date requirements documentation. Rather, we maintain the black-box nature of RBT to recommend features as the units of testing from software's graphical user interfaces (GUIs). In particular, we exploit optical character recognition (OCR) to identify the textual information from GUIs, and further build the GUI-feature correspondences based on software's user-centric documentation which may exhibit partial correctness. Such correspondences from multiple software systems in the same domain serve as a foundation for our recommendation engine, which suggests the to-be-tested features related to a given GUI. We demonstrate our recommender's feasibility with a study of five products in the web conferencing domain, and the results show the more complete set of features against which a GUI needs to be tested.
KW - feature testing
KW - graphical user interfaces
KW - recommender
KW - requirements engineering and testing
UR - https://www.scopus.com/pages/publications/85171880561
U2 - 10.1109/IRI58017.2023.00057
DO - 10.1109/IRI58017.2023.00057
M3 - Conference contribution
AN - SCOPUS:85171880561
T3 - Proceedings - 2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science, IRI 2023
SP - 289
EP - 294
BT - Proceedings - 2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science, IRI 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 4 August 2023 through 6 August 2023
ER -