Exploring the Effectiveness of LLMs in Automated Logging Statement Generation: An Empirical Study

Li, Yichen; Huo, Yintong; Jiang, Zhihan; Zhong, Renyi; He, Pinjia; Su, Yuxin; Briand, Lionel C.; Lyu, Michael R.

doi:10.1109/tse.2024.3475375

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Exploring the Effectiveness of LLMs in Automated Logging Statement Generation: An Empirical Study

Cited by 3 publications

References 60 publications

Case Study: Applying Automated Optimization Tooling to Microservice Environments that Scale Safely at Ancestry.com and the Learnings

Case Study: Applying Automated Optimization Tooling to Microservice Environments that Scale Safely at Ancestry.com and the Learnings

Demystifying and Extracting Fault-indicating Information from Logs for Failure Diagnosis

Analyzing the Impact of Context Representation and Scope in Code Infilling

Contact Info

Product

Resources

About