Master’s Thesis Presentation • Software Engineering • Mitigating the Uncertainty and Imprecision of Log-Based Code Coverage Without Requiring Additional Logging Statements

Please note: This master’s thesis presentation will take place in DC 2310 and online.

Xiaoyan Xu, Master’s candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Shane McIntosh

Understanding code coverage is an important precursor to software maintenance activities (e.g., better testing). Although modern code coverage tools provide key insights, they typically rely on code instrumentation, resulting in significant performance overhead. An alternative approach to code instrumentation is to process an application’s source code and the associated log traces in tandem. This so-called “log-based code coverage” approach does not impose the same performance overhead as code instrumentation. Previous work has introduced LogCoCo — a tool that implements log-based code coverage for Java. While LogCoCo breaks important new ground, it has fundamental limitations, namely: uncertainty due to the lack of logging statements in conditional branches, and imprecision caused by dependency injection.

In this thesis, we propose Log2Cov, a tool that generates log-based code coverage for programs written in Python and addresses uncertainty and imprecision issues. We evaluate Log2Cov on three large and active open-source systems. More specifically, we compare the performance of Log2Cov to that of Coverage.py, an instrumentation-based coverage tool for Python.

Our results indicate that 1) Log2Cov achieves high precision, recall, and F1 score without introducing runtime overhead; and 2) uncertainty and imprecision can be reduced by up to 11% by statically analyzing the program’s source code and execution logs, without requiring additional logging instrumentation from developers. While our enhancements make substantial improvements, we find that future work is needed to handle conditional statements and exception handling blocks to achieve parity with instrumentation-based approaches. We conclude the thesis by drawing attention to these promising directions for future work.

To attend this master’s thesis presentation in person, please go to DC 2310. You can also attend virtually using Zoom at https://uwaterloo.zoom.us/j/99617363422.