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Mastropaolo_2024_Elsevier_Journ of syst and softw_Log statements generation
Journal article

Log statements generation via deep learning : widening the support provided to developers

  • Mastropaolo, Antonio ORCID Istituto del software (SI), Facoltà di scienze informatiche, Università della Svizzera italiana, Svizzera
  • Ferrari, Valentina Istituto del software (SI), Facoltà di scienze informatiche, Università della Svizzera italiana, Svizzera
  • Pascarella, Luca Center for Project-Based Learning, ETH Zurich, Switzerland
  • Bavota, Gabriele ORCID Istituto del software (SI), Facoltà di scienze informatiche, Università della Svizzera italiana, Svizzera
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  • 2024
Published in:
  • The Journal of Systems & Software. - 2024, vol. 210, p. 111947
Logging
DL for software engineering
English 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).
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Language
  • English
Classification
Computer science and technology
License
CC BY
Open access status
hybrid
Identifiers
Persistent URL
https://n2t.net/ark:/12658/srd1331184
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