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• W4286331401 abstract "The textual descriptions in logging statements (i.e., logging texts) are printed during system executions and exposed to multiple stakeholders including developers, operators, users, and regulatory authorities. Writing proper logging texts is an important but often challenging task for developers. However, despite extensive research on automated logging suggestions, research on suggesting logging texts rarely exists. In this paper, we present LoGenText, an automated approach that generates logging texts by translating the related source code into short textual descriptions. LoGenText takes the preceding source code of a logging text as the input and considers other context information such as the location of the logging statement, to automatically generate the logging text using neural machine translation models. We evaluate LoGenText on 10 open-source projects, and compare the automatically generated logging texts with the developer-inserted logging texts in the source code. We find that LoGenText generates logging texts that achieve BLEU scores of 23.3 to 41.8 and ROUGE-L scores of 42.1 to 53.9, which outperforms the state-of-the-art approach by a large margin. In addition, we perform a human evaluation involving 42 participants, which further demonstrates the quality of the logging texts generated by LoGenText. Our work is an important step towards automated generation of logging statements, which can potentially save developers&#x0027; efforts and improve the quality of software logging." @default.
• W4286331401 created "2022-07-21" @default.
• W4286331401 creator A5013795174 @default.
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• W4286331401 date "2022-03-01" @default.
• W4286331401 modified "2023-10-18" @default.
• W4286331401 title "LoGenText: Automatically Generating Logging Texts Using Neural Machine Translation" @default.
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• W4286331401 doi "https://doi.org/10.1109/saner53432.2022.00051" @default.
• W4286331401 hasPublicationYear "2022" @default.
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