SrcLog Valuation Methodology

How We Value Open Source Projects

Every repository on SrcLog displays two financial estimates: the cost to build it and its market value. Here is exactly how we calculate them.

These are estimates for educational and illustrative purposes only. They are not financial advice, not a formal appraisal, and should not be used for investment or acquisition decisions.

Overview

We compute two metrics for each repository:

Cost to Build — the estimated labour cost to recreate the codebase from scratch, plus accumulated maintenance.
Market Value — the creation cost scaled by the project's real-world popularity and recent activity.

Both are computed entirely from publicly available GitHub metadata: repository size, primary language, creation date, stars, forks, watchers, and last push date.

Cost to Build

Based on a modified COCOMO II (COnstructive COst MOdel) approach, adapted for open-source repositories where only aggregate metadata is available.

Step 1 — Estimate Lines of Code

GitHub reports repository size in kilobytes (all files combined). We apply two factors to isolate source code:

effective_kb = min(size_kb, 500 000) // cap at 500 MB to exclude data repos

code_kb = effective_kb × code_density(lang) // strip assets, docs, binaries

loc = code_kb × lines_per_kb(lang)

Language group	Code density	Lines / KB
C, C++, Go, Rust, Swift	55 %	22
Java, Kotlin, Scala, C#	55 %	20
JavaScript, TypeScript	55 %	28
Python, Ruby, Elixir	55 %	35
PHP, Perl, Lua	55 %	30
HTML, CSS, SCSS	45 %	40
Jupyter Notebook, R Markdown	20 %	12
Shell, Bash	55 %	25
Other / unknown	55 %	28

Step 2 — Development Hours

Each language has a characteristic productivity rate (hours per 1 000 lines), reflecting tooling maturity, verbosity, and cognitive load. A non-linear complexity overhead is then applied — larger codebases require disproportionately more effort (Brooks' Law).

dev_hours = (loc / 1000) × hours_per_kloc(lang) × complexity_factor(loc)

Language	Hours / KLOC
Assembly	320
C	260
C++	230
Rust	210
Java / Kotlin / Scala	160
C# / Swift	150
Go	140
JavaScript / TypeScript	105
Python	85
Ruby / Elixir / Clojure	75
PHP / Perl / Lua	80
Shell / Bash	90
HTML / CSS / SCSS	55
Jupyter Notebook / R	70
Other	120

Codebase size	Complexity ×
< 10 K LOC	1.00
10 K – 100 K LOC	1.12
100 K – 500 K LOC	1.28
500 K – 2 M LOC	1.48
> 2 M LOC	1.70

Step 3 — Labour Cost + Maintenance

We use $75 / hour as the blended global rate for a mid-level software developer (a reasonable average across North America, Western Europe, and remote workers).

base_cost = dev_hours × $75

years = (today − github_created_at) in years

cost_to_build = base_cost × (1 + years × 0.15)

The 0.15 factor represents a 15 % annual maintenance overhead — the industry-standard estimate for keeping software alive (bug fixes, dependency updates, documentation, security patches). This accumulates each year the project has existed.

Market Value

Market value reflects what the community actually thinks of a project, not just how much it cost to write. A small but universally-loved tool may be worth far more than a sprawling unmaintained codebase.

Formula

popularity_score = log₁₀(stars+1) × 3.0

+ log₁₀(forks+1) × 1.5

+ log₁₀(watchers+1) × 0.5

popularity_mult = 1 + popularity_score × 3 // ~1× … 60×

market_value = cost_to_build × popularity_mult × activity_mult

Popularity multiplier rationale

We use logarithmic scaling because the difference in value between 100 and 1 000 stars is not linear. The weights reflect that stars are a primary signal, forks indicate active adoption, and watchers indicate passive interest.

Stars	Approx. multiplier	Tier
0 – 10	~1×	Experimental
100	~7×	Growing
1 000	~10×	Established
10 000	~13×	Popular
100 000+	~16×	Iconic

Activity multiplier

A project that has not been touched in years carries more risk — a discount is applied. Actively maintained projects receive a premium.

Last push	Multiplier
≤ 7 days ago	1.3×
≤ 30 days	1.2×
≤ 90 days	1.1×
≤ 180 days	1.0×
≤ 1 year	0.9×
≤ 2 years	0.75×
> 2 years	0.6×

Worked Example

Imagine a TypeScript project with:

Repository size: 5 000 KB
Stars: 12 000 · Forks: 1 500 · Watchers: 200
Created: 4 years ago
Last push: 3 weeks ago

// Step 1 — LOC

code_kb = 5000 × 0.55 = 2 750 KB

loc = 2750 × 28 = 77 000 lines

// Step 2 — Hours

hours = (77 000 / 1000) × 105 × 1.12 = 9 058 h

// Step 3 — Cost

base = 9 058 × $75 = $679 350

total = $679 350 × (1 + 4 × 0.15) = $1 087 960 ≈ $1.09 M

// Market value

pop = log₁₀(12001)×3 + log₁₀(1501)×1.5 + log₁₀(201)×0.5

= 12.27 + 4.72 + 1.15 = 18.14

mult = 1 + 18.14 × 3 = 55.4× → activity 1.2×

market = $1 087 960 × 55.4 × 1.2 ≈ $72.3 M

Limitations & Known Biases

⚠ Repository size includes all files (images, datasets, compiled artifacts). The code-density factor mitigates this but cannot eliminate it for unusual repos.
⚠ Stars ≠ users. A viral tweet can generate thousands of stars overnight without reflecting real adoption.
⚠ Monorepos may have inflated size because they contain multiple independent projects.
⚠ Hourly rate is a single global average. Enterprise projects or niche language specialists may cost 2–5× more.
⚠ Volunteer work in open source is often not priced by market rates. The model estimates commercial replacement cost, not actual spending.
⚠ Network effects and ecosystem lock-in (e.g. linux, react) create value that no formula can capture.

Methodology v1.0 · Published by SrcLog.com