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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. Prior studies find that developers spend significant efforts modifying their logging texts. However, despite extensive research on automated logging suggestions, research on suggesting logging texts rarely exists. To fill this knowledge gap, we first propose\n            <jats:italic>LoGenText<\/jats:italic>\n            (initially reported in our conference paper), an automated approach that uses neural machine translation (NMT) models to generate logging texts by translating the related source code into short textual descriptions.\n            <jats:italic>LoGenText<\/jats:italic>\n            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.\n            <jats:italic>LoGenText<\/jats:italic>\n            \u2019s evaluation on 10 open source projects indicates that the approach is promising for automatic logging text generation and significantly outperforms the state-of-the-art approach. Furthermore, we extend\n            <jats:italic>LoGenText<\/jats:italic>\n            to\n            <jats:italic>LoGenText-Plus<\/jats:italic>\n            by incorporating the syntactic templates of the logging texts. Different from\n            <jats:italic>LoGenText<\/jats:italic>\n            ,\n            <jats:italic>LoGenText-Plus<\/jats:italic>\n            decomposes the logging text generation process into two stages.\n            <jats:italic>LoGenText-Plus<\/jats:italic>\n            first adopts an NMT model to generate the syntactic template of the target logging text. Then\n            <jats:italic>LoGenText-Plus<\/jats:italic>\n            feeds the source code and the generated template as the input to another NMT model for logging text generation. We also evaluate\n            <jats:italic>LoGenText-Plus<\/jats:italic>\n            on the same 10 projects and observe that it outperforms\n            <jats:italic>LoGenText<\/jats:italic>\n            on 9 of them. According to a human evaluation from developers\u2019 perspectives, the logging texts generated by\n            <jats:italic>LoGenText-Plus<\/jats:italic>\n            have a higher quality than those generated by\n            <jats:italic>LoGenText<\/jats:italic>\n            and the prior baseline approach. By manually examining the generated logging texts, we then identify five aspects that can serve as guidance for writing or generating good logging texts. Our work is an important step toward the automated generation of logging statements, which can potentially save developers\u2019 efforts and improve the quality of software logging. Our findings shed light on research opportunities that leverage advances in NMT techniques for automated generation and suggestion of logging statements.\n          <\/jats:p>","DOI":"10.1145\/3624740","type":"journal-article","created":{"date-parts":[[2023,9,18]],"date-time":"2023-09-18T11:55:57Z","timestamp":1695038157000},"page":"1-45","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":16,"title":["<i>LoGenText-Plus<\/i>\n            : Improving Neural Machine Translation Based Logging Texts Generation with Syntactic Templates"],"prefix":"10.1145","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0803-5609","authenticated-orcid":false,"given":"Zishuo","family":"Ding","sequence":"first","affiliation":[{"name":"University of Waterloo, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2378-8972","authenticated-orcid":false,"given":"Yiming","family":"Tang","sequence":"additional","affiliation":[{"name":"Rochester Institute of Technology, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-9958-5836","authenticated-orcid":false,"given":"Xiaoyu","family":"Cheng","sequence":"additional","affiliation":[{"name":"University of Waterloo, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5441-6763","authenticated-orcid":false,"given":"Heng","family":"Li","sequence":"additional","affiliation":[{"name":"Polytechnique Montr\u00e9al, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6222-7444","authenticated-orcid":false,"given":"Weiyi","family":"Shang","sequence":"additional","affiliation":[{"name":"University of Waterloo, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,12,22]]},"reference":[{"key":"e_1_3_2_2_2","volume-title":"Contextual handling in neural machine translation: Look behind, ahead and on both sides","author":"Agrawal Ruchit","year":"2018","unstructured":"Ruchit Agrawal, Marco Turchi, and Matteo Negri. 2018. 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