TY - GEN
T1 - A Retrospective Analysis of Grey Literature for AI-Supported Test Automation
AU - Ricca, Filippo
AU - Marchetto, Alessandro
AU - Stocco, Andrea
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - This paper provides the results of a retrospective analysis conducted on a survey of the grey literature about the perception of practitioners on the integration of artificial intelligence (AI) algorithms into Test Automation (TA) practices. Our study involved the examination of 231 sources, including blogs, user manuals, and posts. Our primary goals were to: (a) assess the generalizability of existing taxonomies about the usage of AI for TA, (b) investigate and understand the relationships between TA problems and AI-based solutions, and (c) systematically map out the existing AI-based tools that offer AI-enhanced solutions. Our analysis yielded several interesting results. Firstly, we assessed a high degree of generalization of the existing taxonomies. Secondly, we identified TA problems that can be addressed using AI-enhanced solutions integrated into existing tools. Thirdly, we found that some TA problems require broader solutions that involve multiple software testing phases simultaneously, such as test generation and maintenance. Fourthly, we discovered that certain solutions are being investigated but are not supported by existing AI-based tools. Finally, we observed that there are tools that supports different phases of TA and may have a broader outreach.
AB - This paper provides the results of a retrospective analysis conducted on a survey of the grey literature about the perception of practitioners on the integration of artificial intelligence (AI) algorithms into Test Automation (TA) practices. Our study involved the examination of 231 sources, including blogs, user manuals, and posts. Our primary goals were to: (a) assess the generalizability of existing taxonomies about the usage of AI for TA, (b) investigate and understand the relationships between TA problems and AI-based solutions, and (c) systematically map out the existing AI-based tools that offer AI-enhanced solutions. Our analysis yielded several interesting results. Firstly, we assessed a high degree of generalization of the existing taxonomies. Secondly, we identified TA problems that can be addressed using AI-enhanced solutions integrated into existing tools. Thirdly, we found that some TA problems require broader solutions that involve multiple software testing phases simultaneously, such as test generation and maintenance. Fourthly, we discovered that certain solutions are being investigated but are not supported by existing AI-based tools. Finally, we observed that there are tools that supports different phases of TA and may have a broader outreach.
KW - Artificial Intelligence
KW - Grey Literature
KW - Test Automation
UR - http://www.scopus.com/inward/record.url?scp=85172411770&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-43703-8_7
DO - 10.1007/978-3-031-43703-8_7
M3 - Conference contribution
AN - SCOPUS:85172411770
SN - 9783031437021
T3 - Communications in Computer and Information Science
SP - 90
EP - 105
BT - Quality of Information and Communications Technology - 16th International Conference, QUATIC 2023, Proceedings
A2 - Fernandes, José Maria
A2 - Travassos, Guilherme H.
A2 - Lenarduzzi, Valentina
A2 - Li, Xiaozhou
PB - Springer Science and Business Media Deutschland GmbH
T2 - 16th International Conference on the Quality of Information and Communications Technology, QUATIC 2023
Y2 - 11 September 2023 through 13 September 2023
ER -