reviewability 0.2.1

CI quality gate that scores pull requests by how hard they are to review

reviewability

A CI/CD quality gate that scores pull requests by how hard they are to review.

Catch diffs that are too large, too tangled, or too scattered to review safely — before they merge.

It doesn't matter how fast AI generates code — the bottleneck is the human reviewer.

Installation

pip install reviewability

Requires Python 3.12+.

The Idea

A pull request can be hard to review not because the code is poorly written, but because of how the changes are combined. Mixing renames, movements, and logic changes in one PR makes each harder to verify. This is especially common with AI-generated code. Unlike linters, Reviewability does not analyze the code — only how the changes are structured.

A clean-code change can still turn into a reviewability disaster when refactors, renames, and behavior updates are mixed together.

When a diff scores low, the typical remedies are splitting it into focused pull requests or deferring non-essential changes.

Reviewability computes metrics at the level of individual hunks, files, and the whole diff, feeding into Reviewability Scores (0.0 = hardest, 1.0 = easiest) with configurable thresholds for what counts as problematic.

Key Concepts

• Hunk — a contiguous block of changes within a single file (the smallest unit of analysis)
• Metric — a calculated value attached to a hunk, a file, or the whole diff
• Score — a float [0.0, 1.0] representing reviewability at hunk, file, or diff level

Extensibility

The metric system is designed to be extended:

• Add a metric — subclass HunkMetric, FileMetric, or OverallMetric, implement calculate(), register via registry.add()
• Adjust scoring — provide a custom ReviewabilityScorer implementation
• Adjust thresholds — edit the default config or provide your own reviewability.toml

Usage

# Analyze a range of commits
reviewability HEAD~1 HEAD

# Analyze from stdin
git diff HEAD~1 | reviewability --from-stdin

# Use a custom config
reviewability --config path/to/reviewability.toml HEAD~1 HEAD

# Include per-file and per-hunk breakdowns
reviewability --detailed HEAD~1 HEAD

Output is JSON. Exit code is 0 if the gate passes, 1 if it fails.

Claude Code Skill

If you use Claude Code, a /reviewability skill is included. It runs the tool on the current diff, summarizes the results, and attempts to address any recommendations directly.

Configuration

All thresholds and limits are configured via a single reviewability.toml file. The tool looks for it in the current directory, or you can specify a path explicitly:

reviewability -c path/to/reviewability.toml HEAD~1 HEAD

If no config file is found, the built-in default is used. You can edit that file directly to change the defaults, or copy it into your project root. The config must contain all mandatory fields — there is no merging with defaults.

# Scores below these thresholds mark hunks/files as problematic
hunk_score_threshold = 0.5
file_score_threshold = 0.5

# Size limits (used for score normalisation)
max_diff_lines = 500
max_hunk_lines = 50

# Gate: fail if overall score drops below this (provisional, based on calibration)
min_overall_score = 0.7

# Optional limits (remove a line to disable that check)
max_problematic_hunks = 3
max_problematic_files = 2
max_file_hunk_count = 5
max_files_changed = 10
max_added_lines = 400

[movement_detection]
hunk_min_lines = 8
file_min_lines = 15
similarity_threshold = 0.95

Movement Detection

Moved code is easy to review — the logic hasn't changed, only the location. The tool detects when a block of code is deleted from one place and inserted elsewhere (accounting for reindentation and package/import changes), and treats those hunks and files as relocations.

Relocations receive a perfect score and are excluded from the size calculations that drive the overall score. A diff that is large only because of relocations is not penalized.

Metrics

Metrics are calculated at three levels: hunk, file, and overall diff.

Hunk-level

Metric	Description
hunk.lines_changed	Total lines added and removed in a hunk
hunk.added_lines	Lines added in a hunk
hunk.removed_lines	Lines removed in a hunk
hunk.context_lines	Unchanged context lines surrounding the change
hunk.change_balance	Ratio of added lines to total changed lines (0.0 = pure deletion, 1.0 = pure addition)
hunk.is_likely_moved	Whether this hunk is a movement of code from another location

File-level

Metric	Description
file.lines_changed	Total lines added and removed across all hunks in a file
file.added_lines	Total lines added in a file
file.removed_lines	Total lines removed in a file
file.hunk_count	Number of separate change regions in a file
file.max_hunk_lines	Lines changed in the largest single hunk within a file
file.is_likely_moved	Whether this file is a movement from another path

Overall-level

Metric	Description
overall.lines_changed	Total lines changed across the entire diff
overall.added_lines	Total lines added across the entire diff
overall.removed_lines	Total lines removed across the entire diff
overall.files_changed	Number of files changed
overall.moved_lines	Total lines in hunks identified as code movements
overall.change_entropy	Shannon entropy of the distribution of changes across files
overall.largest_file_ratio	Fraction of total diff lines in the most-changed file
overall.scatter_factor	Normalized entropy of how changes are distributed across files (0.0 = all in one file, 1.0 = evenly spread)
overall.problematic_hunk_count	Hunks with a score below the configured threshold
overall.problematic_file_count	Files with a score below the configured threshold

Overall Scoring

score = max(0, 1 − effective_size_ratio × (1 + scatter_factor))

effective_size_ratio = (lines_changed − moved_lines) / max_diff_lines   [capped at 1.0]

The score is driven by effective diff size and scatter. Moved lines are excluded from the size count — relocations are easy to review and should not penalize the score.

scatter_factor measures how evenly changes are spread across files (normalized entropy, 0.0 = all in one file, 1.0 = evenly spread). It amplifies the size penalty: a large diff that touches many files evenly scores worse than an equally large diff concentrated in a few files.

A large but focused diff (e.g. a bulk rename in one file) or a scattered but small diff each score better than a diff that is both large and scattered.

Validation

The scoring formula was calibrated against ~2,000 pull requests from 15 permissively licensed open-source repositories. Ground truth labels were derived from review outcomes (change requests, revision cycles, comment density). Metrics that did not improve prediction over a naive size baseline were removed from the formula.

Research

Metrics are informed by peer-reviewed research on code review effectiveness. Most are heuristics derived from research concepts rather than direct paper-defined variables:

• Jureczko et al. — Code review effectiveness: an empirical study on selected factors influence (IET Software, 2021) https://doi.org/10.1049/iet-sen.2020.0134

• McIntosh et al. — An Empirical Study of the Impact of Modern Code Review Practices on Software Quality (EMSE, 2015) https://doi.org/10.1007/s10664-015-9381-9

• Fregnan et al. — First Come First Served: The Impact of File Position on Code Review (EMSE, 2022) https://doi.org/10.1007/s10664-021-10034-0

• Uchôa et al. — Predicting Design Impactful Changes in Modern Code Review (MSR, 2020) https://doi.org/10.1145/3379597.3387480

• Baum et al. — The Choice of Code Review Process: A Survey on the State of the Practice (EMSE, 2019) https://doi.org/10.1007/s10664-018-9657-6

• Hijazi et al. — Using Biometric Data to Measure Code Review Quality (TSE, 2021) https://doi.org/10.1109/TSE.2020.2992169

• Olewicki et al. — Towards Better Code Reviews: Using Mutation Testing to Prioritise Code Changes (2024) https://arxiv.org/abs/2402.01860

• Barnett et al. — Helping Developers Help Themselves: Automatic Decomposition of Code Review Changesets (ICSE, 2015) https://doi.org/10.1109/ICSE.2015.35

• Brito & Valente — RAID — Refactoring-Aware and Intelligent Diffs (2021) https://doi.org/10.1109/ICSME52107.2021.00037

• Hu & Pradel — CodeMapper: Mapping and Analyzing Code Changes across Commits (ICSE, 2026)