Overview Leaderboard Methodology
Industry Report

Code Review and Collaboration Tools

Tools focused on pull request workflows, inline code review, merge approvals, and developer collaboration to ensure code quality and team coordination.

Brands tracked: 35
Brands analyzed: 35
Last updated: 2026-04-22
Model: OpenAI GPT-5
Prompts: 271
Total responses: 1,392
Top Brand Overall?
GitHub
100/100

Highest overall AI Visibility Score in this industry.

LBA Leader?
GitHub
100

Highest score on the LBA metric in this industry.

Authority Leader?
GitHub
100

Highest score on the Authority metric in this industry.

TOM Leader?
GitHub
100

Highest score on the TOM metric in this industry.

GitHub is the default answer in AI responses for Code Review and Collaboration Tools

GitHub and Microsoft together dominate AI responses for Code Review and Collaboration Tools. Both brands consistently surface unprompted, with the model treating them as the default answers for most category queries. Brands outside the top two face a structural disadvantage: users are usually given these two before the model even considers alternatives.

Brand Leaderboard All 35 Code Review and Collaboration Tools brands ranked

Ranked by overall AI Visibility Score (smoothed geometric mean of LBA, Authority, and TOM with an LBA-based floor on Authority and TOM, see methodology). Click any brand for the full report.

# Brand LBA Authority TOM Overall
1
GitHub
github.com
 100
100
100
100
2
Microsoft
microsoft.com
 96
100
100
99
3
GitLab
gitlab.com
 87
73
95
85
4
Atlassian
atlassian.com
 94
53
88
76
5
Bitbucket
bitbucket.org
 75
39
70
59
6
Gerrit
gerritcodereview.com
 72
37
37
46
7
Slack
slack.com
 86
9
56
37
8
Azure DevOps
azure.com
 89
9
43
35
9
P
Phabricator
phacility.com
 63
15
41
35
10
Linear
linear.app
 69
7
49
31
11
Notion
notion.so
 84
8
29
29
12
CodeStream
codestream.com
 63
6
24
23
13
SonarQube
sonarsource.com
 82
8
8
20
14
DeepSource
deepsource.com
 76
8
8
18
15
Gitea
gitea.com
 74
7
7
18
16
Mergify
mergify.com
 72
7
7
18
17
Reviewable
reviewable.io
 45
5
19
18
18
Review Board
reviewboard.org
 55
12
6
17
19
SourceHut
sr.ht
 67
7
7
17
20
CodeRabbit
coderabbit.ai
 66
7
7
16
21
CodeScene
codescene.com
 66
7
7
16
22
Codeberg
codeberg.org
 62
6
6
15
23
Gogs
gogs.io
 60
6
6
15
24
Graphite
graphite.dev
 57
6
6
14
25
Pagure
pagure.io
 55
5
5
14
26
Beanstalk
beanstalkapp.com
 51
5
5
13
27
RhodeCode
rhodecode.com
 52
5
5
13
28
Upsource
jetbrains.com
 52
5
5
13
29
PullApprove
pullapprove.com
 49
5
5
12
30
ReviewNB
reviewnb.com
 49
5
5
12
31
Kallithea
kallithea-scm.org
 37
4
4
10
32
Collaborator
smartbear.com
 35
3
3
9
33
S
Sider
sider.review
 34
3
3
9
34
Hound
houndci.com
 31
3
3
8
35
Helix TeamHub
perforce.com
 18
2
2
5

How is this calculated? Methodology

Every brand in this leaderboard is scored against the same set of 271 shared Code Review and Collaboration Tools prompts. The same prompts, same model, same iterations. So differences in scores reflect actual differences in AI visibility, not differences in measurement.

Overall AI Visibility Score
Smoothed geometric mean of LBA, Authority and TOM. Authority and TOM are floored at LBA × 0.1 before the geometric mean (the same floor used in the per-metric cards above, so brand cards and the composite tell the same story). Formula: composite = ((LBA + 5)(Authority + 5)(TOM + 5))^(1/3) - 5. The floor keeps brands the model clearly recognises but doesn't yet recommend from collapsing to zero, while a single genuinely weak metric still pulls the composite down. Full methodology.
Shared industry prompts
For Authority and TOM, all brands in the industry are scored against the same 271 category prompts (e.g. "best SEO tools for agencies"). This makes brand-to-brand comparisons valid - everyone faces identical inputs. LBA prompts are per-brand because they ask brand-specific questions.
Latent Brand Association (LBA)
5 brand probes + 1 control prompt, each run 5 times in recall mode (no web search). LBA = quality × meta × stability × share × recognition × 100. Read the full LBA methodology →
LLM Authority
50 organic category prompts (discovery, comparison, problem and transactional intents), each run once in recall mode and once in retrieval mode. Score = frequency × log-decayed prominence × intent weight, then 50/50 averaged across the two modes. Read the full Authority methodology →
Top of Mind (TOM)
15 high-volume discovery prompts (sourced from Keywords Everywhere search-volume data), each run 5 times in pure recall mode (no web). Score = frequency × (0.5 + 0.5 × log-prominence), volume-weighted. Read the full TOM methodology →