Best Places to Buy Source Code and Complete Software Projects in 2026

Building software from scratch is not always the best use of time or money.

For a new SaaS product, internal business application, mobile app, WordPress plugin, or niche online service, an existing codebase can remove weeks or even months from the initial development cycle.

The challenge is finding the right kind of source code — and understanding what you are actually buying.

A $30 script, a production-ready application, and the complete acquisition of an existing software project are three very different purchases.

This guide looks at the main options available in 2026 and when each approach makes sense.

1. CodeCanyon

CodeCanyon remains one of the best-known marketplaces for scripts, plugins, templates, and other reusable software components.

Its biggest advantage is selection. Buyers can browse a large catalogue covering PHP applications, WordPress plugins, JavaScript components, mobile applications, and many other categories.

For relatively small and standardized software products, that makes CodeCanyon an obvious place to start.

However, buyers should pay close attention to licensing, documentation, update history, and the quality of individual sellers. A large catalogue inevitably means significant differences between products.

Best for: plugins, scripts, components, templates, and relatively inexpensive reusable code.

2. Codester

Codester follows a similar marketplace model and offers scripts, themes, templates, plugins, app source code, and other web-development assets.

It is particularly useful when you cannot find the right product on the largest marketplaces or want to compare several implementations of the same idea.

As with any marketplace, buyers should evaluate each product individually rather than assuming that being listed automatically guarantees production quality.

Check the documentation, technology stack, update history, licensing terms, and available support before purchasing.

Best for: web scripts, app source code, themes, plugins, and niche developer products.

3. ESDecode

ESDecode takes a broader approach to software source code.

Alongside production-ready source code, scripts, templates, and software products, ESDecode also supports situations where a developer wants to sell an entire software project rather than simply sell multiple copies of a license.

This distinction matters.

A traditional source-code marketplace usually sells licenses to the same codebase multiple times. A complete project acquisition can instead involve transferring an agreed package of assets to one buyer.

Depending on the individual transaction, those assets may include:

• source code;
• domain names;
• brand assets;
• technical documentation;
• deployment materials;
• related intellectual property that the seller is legally able to transfer;
• and an agreed handover period.

This can make the model interesting for developers who have built technically complete projects that never developed a meaningful user base or revenue stream.

A pre-revenue project is not necessarily worthless.

A functioning application may still represent hundreds or thousands of development hours, an established architecture, working frontend and backend components, automated tests, deployment configuration, documentation, and a usable domain or brand.

For buyers, acquiring such a project can sometimes be considerably faster than commissioning an equivalent application from zero.

ESDecode also provides free tools for developers evaluating software assets, including a Software Project Valuation Calculator and a License Compatibility Checker.

Best for: production-ready source code and developers or buyers interested in complete software-project acquisitions.

4. GitHub and Open-Source Projects

GitHub is an enormous source of existing software, but it should not be confused with a commercial source-code marketplace.

Open-source software can provide an excellent foundation for a project. In many cases, it is also substantially better engineered than inexpensive commercial scripts.

But “available on GitHub” does not mean “free to use however you want.”

Before building a commercial product around an open-source repository, check its license carefully.

MIT, Apache, GPL, AGPL, and other licenses impose different requirements. Dependencies inside the project may also use licenses different from the main repository.

This becomes particularly important when you intend to redistribute the resulting software or sell its source code.

Best for: developers capable of reviewing, modifying, securing, and maintaining an existing codebase.

5. Gumroad and Direct Developer Sales

Another option is purchasing source code directly from its developer.

Platforms such as Gumroad make it easy for creators to sell downloadable digital products without operating a specialized code marketplace.

This gives developers significant control over pricing and distribution.

The trade-off is that the platform itself may perform little or no technical curation of the software.

The buyer therefore needs to perform more due diligence.

Before purchasing, determine exactly what is included, what license applies, whether updates are provided, and whether the seller offers support.

Best for: buyers who already know the developer or product they want.

6. Flippa and Business Marketplaces

At the other end of the spectrum are marketplaces focused primarily on businesses rather than reusable source-code products.

Flippa is a typical example.

Here the asset being sold may include a website or application together with its domain, users, traffic, revenue, operational accounts, and other business assets.

This is fundamentally different from purchasing a $50 script.

Business marketplaces make the most sense when the project already has commercial activity or other measurable traction.

If the project has no users and no revenue but contains a substantial working codebase, a source-code marketplace or software-project acquisition model may be a more natural fit.

Best for: operating websites, applications, SaaS businesses, and other projects with measurable commercial assets.

Source Code vs. Complete Project Acquisition

Before choosing a marketplace, decide what you actually want to buy.

Source-code license

You receive permission to use a codebase under specific license conditions.

The developer normally retains ownership and may sell the same product to many other customers.

This is appropriate when you need functionality rather than exclusivity.

Complete project acquisition

You acquire an agreed collection of project assets from the seller.

Depending on the transaction, this can include the codebase, domain, documentation, branding, deployment configuration, and transferable intellectual property.

The exact assets and rights should always be documented.

This model is more appropriate when you want to continue developing a particular product rather than simply reuse a generic script.

What to Check Before Buying Source Code

Regardless of where you purchase software, due diligence matters more than the marketplace logo.

Start with the technology stack.

Make sure your team can actually maintain the application. Buying an inexpensive project built with an unfamiliar or obsolete stack can become expensive very quickly.

Then examine the code and project structure where the seller makes that possible.

Look for documentation, tests, database migrations, deployment instructions, environment configuration, and evidence that the application can be reproduced outside the seller’s machine.

Licensing deserves particular attention.

A seller may own the code they wrote while relying on third-party libraries, commercial assets, datasets, APIs, fonts, images, or other components that cannot automatically be transferred to you.

For larger acquisitions, create an explicit asset list.

Do not rely on a vague statement that you are buying “the project.”

Document whether the transaction includes:

• source repositories;
• domains;
• trademarks or brand assets;
• databases;
• documentation;
• deployment configuration;
• cloud infrastructure;
• social accounts;
• customer data;
• third-party accounts;
• and post-sale support.

Not every asset will necessarily be transferable.

What About Pre-Revenue Software?

One of the more interesting categories is software that is technically developed but commercially unsuccessful — or simply never launched properly.

Developers abandon projects for many reasons.

The founder may have lost interest, moved to another business, lacked marketing resources, changed jobs, or discovered that operating the product required more time than expected.

None of those reasons automatically make the underlying software worthless.

For the right buyer, acquiring an existing codebase can provide a substantial head start.

The important distinction is between business value and software asset value.

A project with no customers cannot reasonably be valued as though it were an established SaaS business. But its codebase, architecture, tests, documentation, domain, design, and development effort can still have value.

That is why pre-revenue software should be evaluated differently from a revenue-producing company.

Which Option Should You Choose?

If you need a relatively inexpensive plugin, template, or common script, start with a traditional source-code marketplace such as CodeCanyon or Codester.

If you have identified a particular independent developer whose work you trust, direct purchase can make sense.

If you want an existing business with customers and revenue, look toward business-acquisition marketplaces.

If you are interested in production-ready code or potentially acquiring an entire software project — including projects that are technically complete but still pre-revenue — a marketplace such as ESDecode is worth considering.

And if you are evaluating an open-source foundation, GitHub can be invaluable, provided you understand the licensing and are prepared to maintain the software yourself.

Final Thoughts

Buying source code should not simply be a search for the cheapest ZIP file.

The real question is how much development time, technical risk, and uncertainty the purchase removes.

Sometimes a $30 script is exactly what a project needs.

Sometimes open source is the better answer.

And sometimes acquiring an existing software project — even one without revenue — can save months of development.

Evaluate the code, licensing, documentation, dependencies, transferable assets, and maintainability before evaluating the price.

The cheapest code is rarely the cheapest software to own.

Thank you for being a part of the community

Before you go:

• Be sure to clap and follow the writer ️👏️️
• Follow us: LinkedIn | Medium | GitHub 🐙
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Best Places to Buy Source Code and Complete Software Projects in 2026 was originally published in DevelopersGlobal on Medium, where people are continuing the conversation by highlighting and responding to this story.

Here’s a scene that plays out on a lot of agile teams. A user story lands in the sprint. Developers start coding. QA reads the same story and thinks, okay, how am I supposed to test this? The acceptance criteria are three bullet points, two of them vague, and there’s no code yet. So testing waits. And then it gets crammed into the last two days of the sprint.

Honestly, it doesn’t have to go that way. If you think about it, a user story is already written from the outside. It talks about what someone wants to happen, never about how the code makes it happen. That’s black box testing in software testing in a nutshell, so the story is a pretty great place to start. No code needed. What you do need is a couple of trusty techniques and the nerve to ask awkward questions before anyone’s started building. Let me show you with an example.

Why user stories and black box testing fit together

The whole idea of a black box test is that you don’t care what’s inside. Something goes in, something comes out, and you check whether what came out is what was promised. Whatever the code’s doing in between? Not your concern.

Stories work the same way. Somebody wants to do something, for some reason. There’s nothing in there about databases or functions. Just a person and what they’re after. So you can start designing black box test cases the minute a story’s ready, usually before a single line of code exists. That timing is the real win. Tests designed early shape the conversation instead of trailing behind it.

Our example story

Let’s use one almost everybody’s run into. A shopper wants to use a discount code during checkout and see the price drop. Pretty standard stuff.

The acceptance criteria say:

• A valid code reduces the order total by the code’s discount.
• An expired code shows an error and leaves the total unchanged.
• Only one code can be applied per order.
• Codes only work on carts of $50 or more.

Looks simple, right? Not quite. Let’s dig in.

Step one: pull out the rules and conditions

Before writing any test cases, I like to jot down everything the story depends on. Go through the criteria one at a time and ask yourself what has to be true, and what’s supposed to happen when it isn’t.

Our story hinges on a few things. First, what kind of code got typed in. Valid, expired, or something that doesn’t exist at all. Then there’s the cart total and which side of $50 it falls on. And whether the order’s already carrying a code. Every one of those is something your tests have to poke at. Getting them on paper first keeps you from testing the happy path, feeling good, and stopping there.

Step two: apply black box test design techniques

Okay, now the classic techniques get to shine.

Equivalence partitioning

The trick here is grouping inputs that ought to behave identically, then testing just one from each group. For the code field, I’d split things into a working code, an expired one, a made-up one, and the field left empty. That’s four tests, not a thousand random codes.

Boundary value analysis

Bugs love edges. That $50 minimum is an edge, so test right up against it. Try a cart sitting a penny under, one that’s dead on $50.00, and one a penny over. You’d be amazed how often somebody typed “greater than” when the rule really meant “greater than or equal to.” Boundaries are how that mistake gets caught.

Decision tables

When conditions start combining, a decision table keeps you honest. Lay out code validity, the cart threshold and whether a code’s already there as a grid, and every row becomes a test. Funny how often one combination, like a valid code on a cart that already has one, is the case nobody thought about.

State transitions

Orders change state. A code gets applied, then removed, then maybe applied again. What happens to the total each time? Does removing a code restore the original price exactly? Thinking in states turns up cases a simple list of inputs misses.

Step three: write the test cases from the requirement, not the code

Time to write the actual test cases. For each one, I want to know where the cart starts, what the shopper does, and what should happen next. And that last bit comes straight from the story. Never from whatever the code ends up doing.

IDStarting conditionInputExpected resultTC1Cart total $80, no codeValid 10% codeTotal drops to $72TC2Cart total $80, no codeExpired codeError shown, total stays $80TC3Cart total $49.99, no codeValid codeCode rejected, minimum not metTC4Cart total $50.00, no codeValid codeCode acceptedTC5Cart total $80, code appliedSecond valid codeSecond code rejectedTC6Cart total $80, code appliedRemove the codeTotal returns to $80

See how none of these mention a database or a function name? That’s on purpose. Even if the implementation gets rewritten next quarter, these tests still describe exactly what the story promised.

Step four: ask the questions the story didn’t answer

This is my favorite part, honestly. Designing black box tests from a story always turns up gaps. And finding them before coding starts is cheap. Finding them in production? Not so much.

Our discount story leaves plenty open. Are codes case-sensitive? What if someone pastes a code with a trailing space? Does the $50 minimum apply before or after tax? Can a code stack with an item that’s already on sale? None of that’s in the acceptance criteria, and every single one is a bug waiting to happen.

So grab the product owner, and a developer too if you can, for ten minutes before anybody starts building. Run through your questions. Whatever they decide either goes into the story as a new criterion or turns into one more test case. Either way, both the story and the tests come out better than they went in.

Step five: decide what to automate

Not every test case needs automating, but the ones guarding core rules usually do. Plenty of story-based checks work really well at the API level, by calling the checkout endpoint directly and checking what comes back, since that’s quicker and a lot steadier than clicking through the UI each time. I’d also tag each automated test with the story it came from. That way, the day the story changes, you’re not hunting around wondering which tests just went stale.

Covering what real users actually do

One honest limitation of story-based testing: stories describe what people planned for. Real users don’t always follow the plan. Someone pastes a code with an invisible character from an email. A mobile app sends the cart total in a slightly different format. A browser autofills the field with something odd.

That’s why many teams pair story-based black box tests with tests drawn from real usage. Recording actual requests that hit the system and replaying them as tests adds the cases nobody imagined, while the story-based tests keep checking the behavior everybody agreed on. Together they cover both what was intended and what really happens.

Conclusion

My take is that user stories are an underrated gift for black box testing in software testing. They describe behavior from the outside, they arrive before the code does, and working through them with a few classic techniques produces solid test cases while the sprint is still young. Better yet, the questions that come up along the way tend to fix the story itself.

Feel like giving it a go this sprint? Grab a story that just got accepted and write down what it depends on. Spend a few minutes on partitions and boundaries, maybe sketch a little decision table, and get five or six test cases written before coding kicks off. Then take whatever gaps you found to your next refinement meeting. I think you’ll find it’s a pretty different conversation.

Thank you for being a part of the community

Before you go:

• Be sure to clap and follow the writer ️👏️️
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Black Box Testing in Software Testing: Turning User Stories Into Test Cases was originally published in DevelopersGlobal on Medium, where people are continuing the conversation by highlighting and responding to this story.

A step-by-step flow for coding interviews, from clarifying the problem to testing, plus how to adapt it for codebase-style rounds.

Interviewers grade how you think as much as whether your code passes. A candidate who clarifies, explains a brute force, improves it and tests it out loud usually beats one who silently writes a perfect answer. A fixed flow also keeps you calm: when you get stuck, you always know the next step.

The flow at a glance

The whole flow fits in seven steps. Each one is covered in more detail below.

1. Clarify — Restate the problem; pin down inputs, outputs, constraints. “So I return the indices, not the values?”
2. Edge cases — List the awkward inputs and agree how to handle them. “What should happen on an empty array?”
3. Brute force — Give the simplest correct idea and its complexity. “Checking every pair is O(n²) time, O(1) space.”
4. Optimise — Find the bottleneck and the technique that removes it. “The inner loop is a lookup, so a hash map makes it O(1).”
5. Commit — Pick an approach, list data structures, outline steps, state complexity. “Given n up to 10⁵, I’ll go with the O(n) version.”
6. Code — Write clean code while narrating. “This map stores value to index.”
7. Test — Dry-run a normal case, then the edge cases. “Walking through [2, 7, 11] with target 9…”

1. Clarify the problem

Don’t touch the keyboard yet. Restate the problem in your own words, then ask:

• Inputs: types, sizes, can they be empty or None? Sorted? Unique values? Negative numbers?
• Outputs: return a value, modify in place, or print? Any order required? What if there’s no answer?
• Constraints: how large is n? The answer tells you the target complexity.

2. List edge cases early

Saying these out loud before coding shows you’re thinking about correctness:

• Empty input, or input of size 1
• All elements the same, or all different
• Duplicates
• Negative numbers or zeros
• Already in the final state (sorted, balanced, …)
• No valid answer: return -1, [], None, or raise?

Note them down, agree what each should return, and come back to them in step 7.

3. Start with brute force

Explain the simplest correct solution and its time and space complexity, even if it’s slow. It proves you understand the problem, gives you a fallback, and often points straight at the bottleneck. Walk through an example with it so you and the interviewer agree on the expected output.

“First, I can solve this by checking every pair, which is O(n²). Then I’ll look at what’s repeated.”

4. Optimise the bottleneck

Ask: which part of the brute force is doing repeated work?

• A repeated search → hash map/set, sorting + binary search, or a heap.
• Repeated range sums → prefix sums.
• Recomputed subproblems → memoisation / DP.
• A window that slides → two pointers or a sliding window.

If you’re stuck:

• Describe the solution in plain English as if doing it by hand, then translate.
• Go through data structures one by one and rule them out: “Would a stack help? A heap?”
• Try a smaller example, or a sorted version of the input.

Compare the options by time and space, then pick one:

“Considering n can be 10⁵, I’ll go with the hash map approach: O(n) time, O(n) space.”

5. Commit before coding

Before writing code, say briefly:

• Data structures you’ll use and why (e.g. a frequency map, a min-heap).
• The algorithm in a few steps: initialise, loop, update, return.
• Time and space complexity, with the reason: “O(n) because each element enters and leaves the window once.”

“If this makes sense, I’ll start coding.” Waiting for a nod catches misunderstandings before they cost you 15 minutes.

6. Code it

Now write the code you just outlined, keeping the interviewer with you as you go.

• Keep narrating: what each variable means, why each condition is there.
• Use clear names (left, right, seen, not i2, tmp).
• Handle the agreed edge cases explicitly, often as early returns.
• Re-read the code for syntax slips: missing colons, wrong indentation, off-by-one ranges.

7. Test it

Don’t wait for the interviewer to find bugs. Test your own code first:

• Dry-run the example from step 3, line by line, tracking variables.
• Run each edge case from step 2.
• Say: “That’s complete. Let’s run it against a test case.”

If the interviewer asks “can you make this faster?”, look for repeated work, a lookup that could be a hash map or set, a search that could be binary, or branches you could prune.

This is part of a larger set of free notes I made for SWE interview prep, covering DSA, CS fundamentals, LLD, and system design. The full version, with a codebase-round template and naming rules, is here: bigonotes

Thank you for being a part of the community

Before you go:

• Be sure to clap and follow the writer ️👏️️
• Follow us: LinkedIn | Medium | GitHub 🐙
• Join our Developers Global Community on Discord 🧑🏻‍💻

How to Run a Coding Interview was originally published in DevelopersGlobal on Medium, where people are continuing the conversation by highlighting and responding to this story.

A developer’s day mostly happens in the Terminal. Projects get built there, logs get read, scripts get run, bugs get chased. And that is also where the mess collects, quietly, a few megabytes at a time. Caches, build artifacts, temporary files, the odd leftover nobody remembers creating: together they make up the developer clutter on a Mac that eventually shows up as a full disk.

Until now, clearing it meant leaving the Terminal for a separate app. CleanMyMac CLI removes that step. It brings CleanMyMac’s cleanup to the command line, so Docker artifacts, Xcode caches, build outputs and package manager leftovers can be dealt with in the same window where they were created. It is an early developer preview, and the footer on every screen says beta.

Where developer clutter actually comes from

It helps to know what is being cleaned, because the names are not always self-explanatory.

Some of it is generated by the tools themselves. Package managers such as Homebrew, npm, Yarn, pip and Cargo keep download caches. Build tools leave output folders behind: node_modules for installed JavaScript dependencies, .next for Next.js builds, .turbo for Turborepo’s cache, .build for Swift packages and target for Rust.

Some of it comes from Xcode. DerivedData holds the build output and index for every project that was ever opened. Device Support keeps symbol files copied from connected devices, Module Cache stores compiled modules, and Documentation Cache keeps documentation that was fetched along the way.

And some of it is just logs and trash. All of it is regenerable, and none of it is anything anyone wants to browse by hand.

Getting it running

CLI installs through Homebrew and needs macOS 11 Big Sur or later:

brew install --cask macpaw/taps/cleanmymac-cli

All commands follow the same pattern. Each one shows what it plans to remove and asks for confirmation before it deletes anything. The list is interactive, with a checkbox on every row, and the footer spells out the keys: arrows to move, Space to toggle an item, A to deselect everything, O to reveal in Finder, Enter to confirm and Q to quit.

One pass over everything

The simplest start is a full cleanup, which covers system junk, developer-tool caches and trash in a single run:

cleanmymac clean

The screenshot below catches the scan midway: checkmarks where a category is done, a spinner on npm, hollow circles for what is still waiting. System Junk lists user logs, DerivedData, macOS Device Support, Archives and simulator runtimes. Dev Tools begins with Homebrew, npm, Docker and VS Code. Trash Bins and AI Junk, which currently names Claude Code and Codex, make up the other two of the four categories.

Caches left by developer tools

For a single category, clean takes a subcommand. This one targets the cached packages and build files that developer tools leave behind:

cleanmymac clean dev-tools

The list is long: Homebrew, npm, Yarn, pnpm, pip, Cargo, Go, CocoaPods, Docker, VS Code, JetBrains, Maven, Gradle, Poetry, uv, Bun, Deno and mise. In this screenshot Homebrew is the heavyweight at 1.4 GB, npm follows at 389 MB, and mise, pip and Bun add a bit more, which brings the selection to 1.97 GB.

Junk closer to the system

Another subcommand covers what lives nearer to the operating system: user logs, Xcode DerivedData, Device Support, Module Cache and Documentation Cache.

cleanmymac clean junk

The logs view is a good example of how the review works. Every entry shows its size and its full path, whether it is a Zoom folder, a log from a code editor or a launch service, so there is no guessing about what a name refers to.

Build artifacts that outlived their projects

The last command goes after something different: old build output scattered across project folders.

cleanmymac purge

It scans the usual places developers keep code (~/Projects, ~/Code, ~/dev, ~/GitHub and ~/Workspace) and lists the removable artifacts grouped by project. Grouping matters because a project name is far easier to judge than a path. Artifacts older than seven days are preselected, while anything newer is left alone, so work from this week is not swept up with a project that has been idle for months. In the sample scan, Rust target folders sit next to node_modules, .turbo, .next and .build, from six projects.

What a finished run looks like

At the end of a run the tool prints a summary per category. In this example: 4.41 GB of System Junk, 1.97 GB of Dev Tools, 19.9 MB of Trash Bins and 736 MB of AI Junk. That adds up to 7.1 GB across 42 items. These numbers come from one machine, so they show what the output looks like and not what any other Mac will recover.

The last two lines of that screen point to two extra commands. cleanmymac ignore <path> adds an item to a whitelist, so a folder that should never be touched stops being offered. cleanmymac clean — force skips the confirmation, which is meant for situations where nobody is around to press Enter.

Where it stands

The tool is still an early preview, and the footer on every screen says beta. The set of commands is small, and a couple of its conventions are worth keeping in mind: purge only looks in the usual project folders, so a project stored elsewhere will not show up, and the seven-day rule is a fixed cutoff. The full command list and the notes on privacy and safety are in the GitHub repository.

Thank you for being a part of the community

Before you go:

• Be sure to clap and follow the writer ️👏️️
• Follow us: LinkedIn | Medium | GitHub 🐙
• Join our Developers Global Community on Discord 🧑🏻‍💻

Cleaning developer clutter on a Mac from the Terminal was originally published in DevelopersGlobal on Medium, where people are continuing the conversation by highlighting and responding to this story.

How Much Is Your Software Project Worth? A Practical Valuation Approach for Pre-Revenue SaaS and Source Code

Selling a software project raises an obvious question:

How much is it actually worth?

For established SaaS businesses, valuation often starts with revenue, profit, growth, and recurring revenue multiples.

But many software projects do not fit that model.

A developer may have spent hundreds of hours building a production-ready SaaS application, plugin, internal tool, mobile app, or boilerplate without ever launching it commercially.

Revenue: $0.

That does not necessarily mean the software is worth $0.

The challenge is finding a reasonable way to estimate its value without pretending that a pre-revenue project should be valued like an established SaaS company.

Why Pre-Revenue Software Is Difficult to Value

Consider two projects.

Project A is a prototype built over a weekend.

Project B has:

- hundreds of development hours
- production-ready architecture
- documentation
- automated tests
- CI/CD
- a polished user interface
- a working deployment
- a domain and brand
- clean licensing
- no significant technical debt

Both may have exactly the same monthly revenue:

$0.

Clearly, they should not automatically have the same value.

This is where purely revenue-based valuation models become less useful.

Start With Replacement Cost

One useful starting point is development replacement cost.

The basic idea is simple:

Development hours × replacement developer rate

If a project required 500 hours of development and comparable work would cost $50 per hour, its theoretical replacement cost would be:

500 × $50 = $25,000

But this does not mean the project is automatically worth $25,000.

A buyer is acquiring an existing asset, not commissioning a new development project from scratch.

The condition and usability of that asset matter.

Software Quality Changes the Asset Value

Two codebases requiring the same number of development hours can have very different acquisition value.

Important factors include:

- code quality
- architecture
- documentation
- test coverage
- UI/UX quality
- deployment readiness

A clean, documented and deployable project reduces the amount of work a buyer must perform after acquisition.

A poorly structured project with significant technical debt can do the opposite.

This means replacement cost should normally be adjusted according to the actual quality and readiness of the software.

Revenue Still Matters When It Exists

Once a project generates revenue or profit, business performance becomes another important valuation signal.

Relevant factors may include:

- monthly revenue
- monthly profit
- recurring versus one-time revenue
- length of revenue history
- revenue trend

A SaaS product generating stable recurring revenue for twelve months is fundamentally different from a newly launched application that made a few one-time sales.

For this reason, a useful software valuation model should be capable of handling both:

pre-revenue asset valuation

and

revenue-generating business valuation.

Assets Can Add Value

The source code may not be the only transferable asset.

An acquisition can also include things such as:

- a domain
- existing users or customers
- brand and design assets
- documentation
- automated tests
- deployment configuration
- CI/CD infrastructure
- a live demo

Not every asset should simply receive an arbitrary monetary bonus.

Some are better treated as indicators of software quality and readiness, while others can represent genuinely transferable commercial value.

Risks Matter Just as Much

A technically impressive project can still become a poor acquisition if significant risks exist.

Three particularly important examples are:

Technical debt

A buyer may need substantial additional engineering work before the software can be maintained or extended safely.

Licensing problems

Third-party code, incompatible open-source licenses, unclear ownership, or non-transferable components can materially affect an acquisition.

Key-person dependency

If only the original developer understands how the system works, transferring the project becomes more difficult and expensive.

A realistic valuation should therefore account for risks rather than looking only at positive attributes.

A Practical Valuation Model

I wanted a quick way to combine these factors without pretending that software valuation can be reduced to one universal revenue multiple.

So I built the Software Project Valuation Calculator.

It is a free tool that estimates an indicative valuation range for:

- SaaS products
- web applications
- mobile applications
- plugins
- scripts
- boilerplates
- other software projects

It also works with projects that have zero revenue.

Try the free calculator:

https://esdecode.com/tools/software-project-valuation-calculator

What the Calculator Considers

The calculator uses four main groups of inputs.

1. Project characteristics

This includes development status, approximate development hours, and the replacement hourly rate.

2. Financial performance

If the project generates money, the model considers revenue, profit, revenue type, revenue history, and trend.

3. Software quality

Code quality, architecture, documentation, test coverage, UI/UX, and deployment readiness are considered.

4. Assets and risks

Transferable assets can strengthen the project while factors such as technical debt, licensing concerns, and key-person dependency can reduce the estimate.

Why It Produces a Range

Software acquisitions rarely have one objectively correct price.

One buyer might already have a distribution channel and engineering team.

Another may need to spend months integrating the same project.

The value of the exact same source code can therefore differ between buyers.

For that reason, the calculator produces an estimated range rather than presenting one number as absolute truth.

It also provides a confidence level and explains the main factors affecting the result.

No AI Black Box

The calculator deliberately does not use an AI model to invent a valuation.

The calculation is deterministic.

The same inputs produce the same result.

The valuation logic is also available as an open-source TypeScript project:

https://github.com/esdecode/software-project-valuation-calculator

This makes the assumptions inspectable rather than hiding the calculation behind an opaque API.

Valuation Is Not the Same as Sale Price

This distinction is important.

An estimated software value is not necessarily the price someone will pay.

Actual transaction prices depend on:

- buyer demand
- negotiation
- due diligence
- intellectual-property ownership
- licensing
- transferability of assets
- market conditions
- strategic value to a particular buyer
- how quickly the seller wants to close

A seller looking for an immediate liquidation may accept considerably less than someone willing to wait months for the right buyer.

Likewise, a strategic buyer may pay more because the software solves a specific problem they already have.

Use Valuation as a Starting Point

The purpose of a software valuation model should not be to tell a founder:

“Your project is worth exactly $47,382.”

That level of precision would be misleading.

A better question is:

Given the development effort, software quality, business performance, transferable assets, and risks, what appears to be a reasonable starting range?

That is the question the calculator is designed to help answer.

Try It

The Software Project Valuation Calculator is free to use:

http2s://esdecode.com/tools/software-project-valuation-calculator

The calculation runs locally in the browser, and the tool can also evaluate projects with no revenue.

For developers interested in the implementation and valuation model, the calculation engine is open source:

https://github.com/esdecode/software-project-valuation-calculator

Final Thoughts

Software has value before it generates revenue.

But development effort alone does not determine that value.

A useful valuation needs to consider what has actually been built, how maintainable and transferable it is, whether it produces revenue, what additional assets come with it, and what risks a buyer would inherit.

For pre-revenue software especially, thinking in terms of asset quality, replacement effort, transferability, and risk provides a much more useful starting point than simply applying a SaaS revenue multiple to zero.

The result still isn’t a guaranteed sale price.

But it gives developers and potential buyers something much better than a guess.

Thank you for being a part of the community

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How Much Is Your Software Project Worth? was originally published in DevelopersGlobal on Medium, where people are continuing the conversation by highlighting and responding to this story.

Pretty much every team using Playwright ends up with a big pile of end-to-end tests. Log in, click through checkout, update a profile, all of it. They’re great. They’re also slow. Run the full suite and you can go make coffee. And when one fails? Good luck figuring out right away whether it’s the UI or the backend.

Here’s what took me a while to notice. Every one of those E2E runs is quietly firing real API calls at your backend the whole time. Login hits auth. Checkout hits cart, pricing, payment. That traffic happens no matter what, and then it just vanishes when the run ends. Kind of a waste, honestly, because with a bit of effort it can turn into a fast, focused layer of Playwright API testing, built from flows you already trust.

Why E2E runs are an untapped source of API tests

Writing API tests from scratch is real work. Someone has to decide which endpoints to hit, what payloads to send, in what order. And a lot of it ends up being guesses about how the frontend actually uses the API.

E2E runs skip the guessing completely. The requests your frontend sends during a Playwright run are, well, the requests real users trigger. Real payloads. Real order. Even the weird edge cases, like a search with an empty filter or a cart with nothing in it, show up exactly the way the frontend sends them. So why invent API tests when you can just harvest them?

What Playwright already captures during a run

Good news: Playwright makes this easier than you’d think.

The simplest option is HAR recording. Basically you flip a switch on the browser context, and from then on it keeps a log of everything going in and out. That log is a HAR file. Nothing exotic, it’s JSON, the same format your browser’s dev tools export. Leave it on while the E2E suite runs and you’ll have a record of every flow sitting on disk afterwards.

Prefer filtering as you go? Playwright also fires request and response events on the page in real time. That’s handy if you only want calls to your own API domain and don’t care about the rest.

Either way, by the end of a run you’ve got a pretty detailed picture of how your frontend actually talks to your backend.

From captured traffic to Playwright API testing

Playwright ships with a request fixture for sending HTTP calls directly. No browser at all. These tests run in a tiny fraction of the time a browser test takes, and you still get Playwright’s runner, its reports, and its assertions, including a nice little check for whether a response came back OK.

Turning captured traffic into these tests is mostly about picking the right calls. Take a meaningful request from your HAR file, with its method, URL, headers and body, and that’s basically an API test: send the same thing, check what comes back.

The checking part needs a bit of judgment though. Assert on the entire response body and you’ll regret it fast, because timestamps and IDs change every run. I’d start with the status code. Then check that the response still looks roughly right, the fields are there and nothing’s suddenly a different type. Beyond that, only pin down values the flow really depends on. If it’s checkout, that’s probably the order total. Everything else can wobble.

Keeping flows in order

Some calls only make sense together. Take checkout. First a cart gets created, then something lands in it, and only after that does an order go through, with each call leaning on an ID the previous one handed back. Captured traffic keeps that order for you, so keep those calls together as one test. Split them up and they can’t really stand on their own.

Handling auth and test state

Auth is usually where people trip first. Tokens from an E2E run expire, so replaying them as-is works for about five minutes. Playwright’s storage state feature is a lifesaver here. Log in once at the start of the suite, save the session, and reuse it for the API tests too. Fresh token, no fuss.

Test data needs a little care as well. If a captured flow created a user with a specific email, replaying it might crash into that same user, who already exists now. Generating unique values per run, or cleaning up after each test, keeps things repeatable.

Filtering out the noise

Open a raw HAR file from an E2E run and you’ll find way more than your API in there. Images, fonts, scripts, analytics pings, calls to whatever third-party widgets the page loads. None of that belongs in API regression tests.

Filtering by your API’s domain or path prefix clears out most of it. I’d also drop calls that don’t really affect behavior, like health checks or telemetry. Keeps the suite lean and focused on stuff that matters.

Capturing what happens behind the API

Okay, there’s one blind spot here. The browser only sees what the frontend sends. It has no idea what your backend does next. Which database queries it runs, whether it calls a payment provider, an inventory service, whatever.

Why does that matter? Because if your shiny new API tests hit a backend that still talks to a live database and real third-party services, they’ll be slower and flakier, and they’ll fail any time a dependency has a rough day.

The fix is capturing on the backend side too. Record traffic at the network level while the E2E suite runs, and you pick up everything the backend sends and receives, database queries and downstream API calls included, without touching a line of application code. Replay those recorded interactions as mocks when the API tests run, and suddenly your Playwright API testing is fast, isolated and predictable. Same request, same downstream behavior, every time. So when a test fails, it’s pointing at a real change in your code, not at somebody else’s outage.

Keeping the API suite current

My favorite part? Keeping all this fresh is cheap. Your E2E suite already runs on a schedule anyway. When a feature changes, re-run those flows with recording on, regenerate the affected API tests and recordings, and look at the diff in a pull request.

That diff ends up being pretty useful by itself. A new field in a response, a status code that changed, it all jumps out, and reviewers get a chance to say “yep, meant to do that” before it ships.

Conclusion

The way I see it, E2E tests and API tests don’t need to be two separate projects. Your Playwright E2E runs are already poking the API exactly the way real users do. Hang on to that traffic, and to what the backend does behind it, and the regression layer pretty much builds itself out of real behavior. No guessing about payloads.

Curious? Start tiny. One E2E flow you already trust is plenty, and login’s a good candidate. Turn HAR recording on, run it, and pull a handful of its API calls into request-fixture tests. Then time both. Seeing the API version finish in seconds, while the browser one is still loading pages, tends to be pretty convincing.

Thank you for being a part of the community

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• Be sure to clap and follow the writer ️👏️️
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Playwright API Testing Built From Your E2E Runs was originally published in DevelopersGlobal on Medium, where people are continuing the conversation by highlighting and responding to this story.

1. Understanding the Problem Before Writing Code

AI can generate code quickly, but it does not automatically understand the real problem behind a request.
A developer who understands the requirements can ask better questions, identify missing information, and decide whether the proposed solution actually solves the problem.
For example, asking an AI tool to “build a login system” is easy.
But a developer still needs to think about authentication, authorization, password security, session management, error handling, and the type of users the system needs to support.
The better you understand the problem, the more useful AI becomes.

2. Code Review Is Becoming More Important
When AI generates code, accepting it without checking it can create new problems.
The code may look clean and still contain logical errors, inefficient approaches, outdated methods, or security weaknesses.

Developers therefore need strong code-reading skills.
You do not have to write every line manually to be a good developer. But you should be able to understand what the code is doing and recognize when something does not look right.
AI can generate the first version.
The developer still needs to judge the final version.

3. Debugging Still Matters
One of the biggest misconceptions about AI-assisted development is that debugging will disappear.
In reality, debugging can become even more important.
AI-generated code may fail because of an incorrect assumption, an unexpected input, a dependency issue, or a problem somewhere else in the application.
A developer who understands debugging can use AI as a partner instead of depending on it blindly.
Instead of simply saying “Fix this error,” a skilled developer can investigate the problem, provide useful context, test possible solutions, and verify whether the fix actually worked.

4. Knowing How Systems Work
Developers do not need to memorize everything.
But they need a strong understanding of the systems they are working with.
Knowing how APIs communicate, how databases store information, how authentication works, how applications are deployed, and how different services interact gives developers the ability to evaluate AI-generated solutions.
Without that foundation, it becomes easy to accept an answer simply because it sounds technical.
AI can explain a concept.
It cannot replace the developer’s responsibility to understand whether that explanation applies to the actual system.

5. Security Cannot Be

Delegated Completely to AI
Security is another area where human judgment remains essential.
AI can suggest security practices, identify possible vulnerabilities, and help developers understand security concepts. But developers still need to think carefully about authentication, permissions, sensitive information, dependencies, API keys, user data, and application behavior.
A small mistake can have serious consequences.
This is why developers should treat AI-generated code as something to review, not something to trust automatically.

6. Learning to Work With AI, Not Under AI
The strongest developers may not be the ones who use AI the most.
They may be the ones who know when to use it and when not to use it.
Sometimes AI can save hours by generating repetitive code or

explaining an unfamiliar concept.
Other times, manually thinking through a problem first can lead to a better solution.
AI should reduce unnecessary work, not replace thoughtful engineering.
A developer who uses AI for every decision may eventually lose confidence in solving problems independently.
The goal should be collaboration.

7. Communication Is Still a Technical Skill
Software development has never been only about code.
Developers communicate with designers, product managers, clients, testers, other developers, and users.
AI may help produce code, documentation, or technical explanations, but developers still need to explain decisions clearly.
Being able to describe a technical problem in simple language is

valuable because it helps teams make better decisions.
Good communication also improves AI interaction.
The clearer the developer’s requirements, constraints, and context are, the more useful the AI’s response is likely to be.

8. Curiosity Matters More Than Memorization
Technology changes quickly.
A tool that is popular today may be replaced by something else tomorrow.
This makes curiosity an important developer skill.
Instead of trying to memorize every new AI tool, framework, or platform, developers should learn how to understand new technologies quickly.
Ask questions.
Experiment.
Read documentation.

Build small projects.
Break things and figure out why they broke.
AI can accelerate this learning process, but curiosity still has to come from the developer.

AI Is Changing the Developer’s Role
AI is not simply removing programming tasks.
It is changing where developers spend their time.
Some repetitive coding tasks may become faster. Developers may spend more time reviewing solutions, designing systems, understanding requirements, testing applications, and making technical decisions.
That means the value of a developer is not limited to how quickly they can type code.
It is increasingly connected to how well they can think, evaluate, solve problems, and make responsible decisions.

Prompting is useful.
But prompting alone is not a complete developer skill set.
The developers who continue to grow in an AI-assisted world will be those who learn how to combine AI’s speed with their own understanding, judgment, creativity, and technical foundations.
AI can help write the code.
The developer still has to understand what that code should accomplish—and whether it is safe, reliable, and worth using.

Thank you for being a part of the community

Before you go:

• Be sure to clap and follow the writer ️👏️️
• Follow us: LinkedIn | Medium | GitHub 🐙
• Join our Developers Global Community on Discord 🧑🏻‍💻

The AI Skills Developers Need Beyond Prompting was originally published in DevelopersGlobal on Medium, where people are continuing the conversation by highlighting and responding to this story.