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Logging assists in monitoring events that transpire during the execution ofsoftware. Previous research has highlighted the challenges confronted bydevelopers when it comes to logging, including dilemmas such as where to log,what data to record, and which log level to employ (e.g., info, fatal). In thiscontext, we introduced LANCE, an approach rooted in deep learning (DL) that hasdemonstrated the ability to correctly inject a log statement into Java methodsin ~15% of cases. Nevertheless, LANCE grapples with two primary constraints:(i) it presumes that a method necessitates the inclusion of logging statementsand; (ii) it allows the injection of only a single (new) log statement, even insituations where the injection of multiple log statements might be essential.To address these limitations, we present LEONID, a DL-based technique that candistinguish between methods that do and do not require the inclusion of logstatements. Furthermore, LEONID supports the injection of multiple logstatements within a given method when necessary, and it also enhances LANCE'sproficiency in generating meaningful log messages through the combination of DLand Information Retrieval (IR).

Log Statements Generation via Deep Learning: Widening the Support Provided to Developers