Please note: This master’s thesis presentation will take place in DC 2568 and virtually over Zoom.
Xueyao Yu, Master’s candidate
David R. Cheriton School of Computer Science
Supervisors: Professors Mike Godfrey, Shane McIntosh
While code review is a critical component of modern software quality assurance, defects can still slip through the review process undetected. Previous research suggests that the main reason for this is a lack of reviewer awareness about the likelihood of defects in proposed changes; even experienced developers may struggle to evaluate the potential risks. If a change’s riskiness is underestimated, it may not receive adequate attention during review, potentially leading to defects being introduced into the codebase.
In this thesis, we investigate how risk assessment analytics can influence the level of awareness among developers regarding the potential risks associated with code changes; we also study how effective and efficient reviewers are at detecting defects during code review with the use of such analytics. We conduct a controlled experiment using Gherald, a risk assessment prototype tool that analyzes the riskiness of change sets based on historical data.
Following a between-subjects experimental design, we assign participants to the treatment (i.e., with access to Gherald) or control group. All participants are asked to perform risk assessment and code review tasks. Through our experiment with 48 participants, we find that the use of Gherald is associated with statistically significant improvements (one-tailed, unpaired Mann-Whitney U test, alpha = 0.05) in developer awareness of riskiness of code changes and code review effectiveness. Moreover, participants in the treatment group tend to identify the known defects more quickly than those in the control group; however, the difference between the two groups is not statistically significant.
Our results lead us to conclude that the adoption of a risk assessment tool has a positive impact on code review practices, which provides valuable insights for practitioners seeking to enhance their code review process and highlights the importance for further research to explore more effective and practical risk assessment approaches.
To To attend this PhD seminar in person, please go to DC 2568. You can also attend virtually using Zoom at https://uwaterloo.zoom.us/j/97965668887.
200 University Avenue West
Waterloo, ON N2L 3G1