Date of Award
Summer 7-21-2020
Document Type
Thesis
Degree Name
Master of Arts (MA)
Department
Computer Science
First Advisor
Raffi Khatchadourian
Second Advisor
Saptarshi Debroy
Academic Program Adviser
Subash Shankar
Abstract
This thesis presents and explores two techniques for automated logging statement evolution. The first technique reinvigorates logging statement levels to reduce information overload using degree of interest obtained via software repository mining. The second technique converts legacy method calls to deferred execution to achieve performance gains, eliminating unnecessary evaluation overhead.
Recommended Citation
Spektor, Allan R., "Two Techniques For Automated Logging Statement Evolution" (2020). CUNY Academic Works.
https://academicworks.cuny.edu/hc_sas_etds/631