Log statements generation via deep learning
widening the support provided to developers
Informazioni aggiuntive
Autori
Mastropaolo A.,
Ferrari V.,
Pascarella L.,
Bavota G.
Tipo
Articolo pubblicato in rivista scientifica
Anno
2024
Lingua
Inglese
Sommario
Logging assists in monitoring events that transpire during the execution of software. Previous research has highlighted the challenges confronted by developers 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 this context, we introduced LANCE, an approach rooted in deep learning (DL) that has demonstrated the ability to correctly inject a log statement into Java methods in 15% of cases. Nevertheless, LANCE grapples with two primary constraints: (i) it presumes that a method necessitates the inclusion of logging statements and; (ii) it allows the injection of only a single (new) log statement, even in situations where the injection of multiple log statements might be essential. To address these limitations, we present LEONID, a DL-based technique that can distinguish between methods that do and do not require the inclusion of log statements. Furthermore, LEONID supports the injection of multiple log statements within a given method when necessary, and it also enhances LANCE’s proficiency in generating meaningful log messages through the combination of DL and Information Retrieval (IR).
Parole chiave
Logging, DL for software engineering
Periodico
The Journal of Systems & Software
Volume
210
Pagine (o numero dell’articolo)
111947
Diffusione
Licenza
CC BY
Visibilità
Pubblico
Status open access
Hybrid
