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Several Reviewer Recommendation Approaches have
been Developed to Improve Code Review Process
Expertise/Experience-based
Approaches
Finding reviewers who review
many similar patches in the past
[Balachandran ICSE2013,
Thongtanunam et al SANER2015,
Zanjani et al TSE2016, Xia et al ICSME2016]
Exp. + Past Collaboration
Approaches
Finding reviewers who often work
with the author in the past
[Yu et al ICSME2014, Ouni et al IST2017]
!
Requesting only experts or active reviewers for a
review could potentially burden them
Invited reviewers often consider
their workload when accepting
new invitations
[Ruangwan et al EMSE 2019]
At Google, review tasks are
assigned in a round-robin
manner
[Sadowski et al. ICSE 2018]
WLRRec:
Workload-aware Reviewer Recommendation
NSGA-II
A new patch
Experience &
Activeness
Past
Collaboration
Obj 1: Maximize the chance of
participating a review
Workload
Obj 2: Mimize the Skewness of the
Reviewing Workload Distribution
Our WLRRec outperforms the single-objective
approaches
0%
45%
90%
135%
180%
Precision Recall F1
0%
35%
70%
105%
140%
Precision Recall F1
%Gain WLRRec vs GA-Obj1
Precision Recall F-Measure Precision Recall F-Measure
%Gain WLRRec vs GA-Obj2
WLRRec is 88%-142% higher precision,
111%-178% higher recall than GA-Obj1
WLRRec is 55%-101% higher precision,
96%-138% higher recall than GA-Obj2
Our WLRRec with NSGA-II is better than other two
multi-objective approaches
0%
25%
50%
75%
100%
Precision Recall F1 HV
0%
25%
50%
75%
100%
Precision Recall F1 HV
%Gain WLRRec with NSGA-II vs MOCell
Precision Recall F-Measure
NSGA-II is 31%-95% higher F-measure,
NSGA-II is 19%-95% higher F-measure,
%Gain WLRRec with NSGA-II vs SPEA2
Hypervolume Precision Recall F-Measure Hypervolume
Our WLRRec with NSGA-II is better than other two
multi-objective approaches
0%
25%
50%
75%
100%
Precision Recall F1 HV
0%
25%
50%
75%
100%
Precision Recall F1 HV
%Gain WLRRec with NSGA-II vs MOCell
Precision Recall F-Measure
NSGA-II is 31%-95% higher F-measure,
21%-31% higher hypervolume than MOCell
NSGA-II is 19%-95% higher F-measure,
29%-47% higher hypervolume than SPEA2
%Gain WLRRec with NSGA-II vs SPEA2
Hypervolume Precision Recall F-Measure Hypervolume
Our work highlights the potential of leveraging
the multi-objective algorithm that consider
review workload and other important
information to find reviewers
[email protected]
@patanamon
http://patanamon.com
Our WLRRec outperforms the four alternative
approaches