The Tool Desk
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For privacy-sensitive batch comparisons, start with JPlag. For a managed workflow that needs source links and integrations, evaluate Codequiry. For an API pipeline involving proprietary repositories, evaluate Copyleaks. Free MOSS or Dolos runs can provide a useful peer-only baseline, but every similarity result still requires human review.
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What a GitHub code-plagiarism tool actually does
Code-plagiarism systems answer two different questions that are often mixed together:
- Peer comparison: Are two or more submitted programs unusually similar to one another?
- Source discovery: Does a submission resemble code published on GitHub, another website, a shared index or a private repository?
A peer-comparison engine normally receives a batch of submissions and produces pairwise matches. It may tokenize code, parse syntax, or compare program structure so that superficial edits do not hide copied work. A discovery service must search an external corpus, retain source links or repository identifiers, and deal with access controls, indexing gaps and changing content.
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Those are separate jobs. A tool can be excellent at finding overlap inside a class set while having no ability to search GitHub. Conversely, a web scanner can find a public source but may not be designed to establish whether one student copied from another student in the same assignment.
Side-by-side comparison
| Tool | Main scope | Deployment and privacy | Accuracy evidence available | Price evidence |
|---|---|---|---|---|
| MOSS | Peer similarity; no web or AI checks in the reviewed comparison | Hosted submission workflow; current Stanford terms should be checked before use | Codequiry’s 2026 vendor benchmark | Free for academic use according to the comparison; verify current terms |
| JPlag | Pairwise peer similarity with syntax and structure awareness | Runs locally; the project states that source and results remain local | Codequiry’s 2026 vendor benchmark | Free and open source |
| Dolos | Peer comparison | Online or local options described in the comparison | No independent figure captured | Free and open source |
| Codequiry | Peer comparison, GitHub/web discovery and AI-code checks | Web dashboard, desktop, API and CLI; documentation describes encrypted handling | Vendor benchmark reporting 95.0% F1 on its stated dataset | Grow: $28/month; Advanced: $99/month |
| Copyleaks | Online-source, shared-data and private-repository source-code API scans | Hosted API with webhook workflow and a private-repository hub | No comparable code-plagiarism figure captured in the documentation reviewed | Pricing was not stated in the available documentation |
Pricing, quotas, language support and program availability can change. Treat the figures above as the conditions documented for 2026, not permanent guarantees.
JPlag: the privacy-first peer comparator
JPlag is designed for comparing a set of programs against one another rather than searching the public internet. Its official project documentation states: “JPlag finds pairwise similarities among a set of multiple programs.” It also says that similarities are calculated locally and that the system is aware of programming-language syntax and program structure.
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Why structure awareness matters
Text-only comparison is easily disturbed by changing whitespace, comments or identifier names. A syntax- and structure-aware engine can still recognize corresponding control flow and operations after those cosmetic changes. That makes JPlag useful when students or contributors may have reformatted code or renamed variables.
When JPlag is the best fit
- You must keep source files and reports on an institution-controlled machine.
- You are comparing a defined batch of submissions for one assignment, interview or internal exercise.
- You want a free, open-source tool instead of a hosted service.
- You can perform the final investigation yourself after the report highlights suspicious pairs.
What JPlag does not establish
JPlag does not, by itself, prove that a submission was copied from GitHub or another public site. It also does not provide a universal accuracy guarantee across every language, refactoring style or assignment design. Its local operation protects data, but it means you must build your own process for collecting submissions, storing reports and documenting decisions.
MOSS: a free hosted baseline for peer similarity
MOSS is commonly used as a peer-only baseline: submit a set of programs, receive similarity results, then inspect the highlighted overlaps. In the comparison summarized here, it is treated as a hosted workflow without web-source or AI-code checking.
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Strengths
- No subscription price is listed for academic use in the comparison.
- It is straightforward for a one-off class or team batch where all relevant files are available.
- Its reports can quickly identify pairs that deserve a closer look.
Operational cautions
Because the workflow is hosted, confirm the current Stanford usage and retention terms before uploading sensitive or proprietary code. MOSS should be regarded as a triage system, not an automated misconduct verdict. Common assignment scaffolding, starter files and required libraries can generate legitimate overlap, so exclude shared boilerplate where the workflow permits and read the matched regions.
Dolos: an open-source peer-comparison option
Dolos is presented as a peer-comparison tool with online and local options. That combination can suit departments that want a graphical or hosted workflow for routine checks while retaining a local route for restricted assignments.
Where Dolos fits
Use Dolos when your central requirement is comparing submissions inside a known corpus and you prefer an open-source tool. It is not presented here as a GitHub search engine, and no independent accuracy figure is established in the available material. Plan to validate its behavior on your own labeled examples before relying on it for high-stakes decisions.
Codequiry: combined peer, GitHub/web and AI-code workflow
Codequiry covers more than pairwise class comparison. The comparison describes peer checks, GitHub and web source links, AI-code signals, and integrations through a web dashboard, desktop application, API and command-line interface. Its documentation also describes encrypted handling.
Plans documented for 2026
| Plan | Monthly price | Included allowance or scope stated |
|---|---|---|
| Grow | $28 per month | 10 web-plus-AI checks per month |
| Advanced | $99 per month | Plan price documented; the available material does not state an additional check quota |
These are monthly prices shown on the 2026 pricing page and may change. Check the current quota, supported languages, retention policy and integration limits before subscribing.
When Codequiry makes sense
- You need links to possible GitHub or web sources as well as peer matches.
- Your review process includes AI-generated-code signals in addition to similarity evidence.
- You want LMS, API or CLI integration instead of a manual upload-only process.
- You prefer a managed service and accept recurring cost and hosted-data considerations.
Codequiry’s own documentation warns that common code patterns can create similarity and recommends reviewing highlighted matches. Treat its AI signal as an investigative lead, not proof of authorship or misconduct.
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Copyleaks: API-oriented source-code scanning
Copyleaks documents source-code API scans against online sources, its Shared Data Hub and private repositories. The documented workflow includes webhooks, which can be useful when a build, submission portal or repository event should trigger an automated scan.
Private-repository use case
An organization with proprietary code can contribute or compare material through a private-repository hub rather than publishing it. Before implementation, establish which repositories are indexed, how authentication is handled, what data is retained and how deletion requests work.
Important AI limitation
The documentation states that AI-content detection is not supported for source-code files. Therefore, do not select Copyleaks expecting its general AI detector to classify programming files; its documented value here is source-code similarity and API-based discovery.
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One 2026 Codequiry benchmark used 2,400 Java assignments and reported the following results:
| Tool | F1 score | Recall | False-positive rate |
|---|---|---|---|
| Codequiry | 95.0% | 94.0% | 0.3% |
| MOSS | 83.5% | 87.0% | 2.1% |
| JPlag | 86.7% | 81.0% | 0.9% |
| Turnitin | 69.2% | 72.0% | 4.3% |
These numbers are vendor-published results, not an independent universal ranking. They describe the stated Java dataset and the study’s labeling and obfuscation conditions. The benchmark included 31 obfuscated positive cases and reported that detection varied by tool.
What the metrics mean operationally
- Recall measures how many known problematic cases were detected. Lower recall means more missed cases.
- False-positive rate measures how often acceptable code was flagged. A higher rate creates more unnecessary investigations.
- F1 balances precision and recall into one score, but it still depends on the dataset, labels and threshold used.
No source reviewed establishes universal performance across all programming languages, assignment types, generated code, copied libraries or sophisticated refactoring. If the decision affects grades, employment or discipline, test candidate tools on a labeled corpus that resembles your real submissions.
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A practical comparison workflow
1. Define the detection question
Write down whether you need peer comparison, public-source discovery, private-repository matching, AI-code indicators or a combination. This prevents buying a web scanner when the real problem is duplicated submissions, or choosing a peer comparator when source attribution is required.
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- Collect each submission in a separate, consistently named directory.
- Remove compiled binaries, dependency caches and generated files that can create meaningless matches.
- Record the programming language, assignment version and any starter code shared by everyone.
- Keep an untouched evidence copy so later exclusions are reproducible.
3. Establish a clean baseline
Run a peer-only tool such as JPlag, MOSS or Dolos on the normalized batch. Mark matches caused by instructor-provided scaffolding, standard library calls or required templates. This baseline tells you what normal overlap looks like before you add web or repository searches.
4. Add source discovery when needed
For GitHub or web attribution, use a workflow that explicitly supports those sources, such as Codequiry’s documented discovery features or Copyleaks’ documented online and private-repository API scans. Capture the source URL, repository revision or API record alongside the matched code; a similarity percentage without provenance is weak evidence.
5. Review highlighted regions manually
Check whether the matching code is distinctive, whether it is required by the assignment, and whether the surrounding logic supports the same conclusion. Compare timestamps, commit history, student explanations and legitimate collaboration records where policy allows. Do not convert a score directly into a finding.
6. Document the decision
Save the tool name and version, configuration, excluded paths, report date, matched files and reviewer reasoning. This makes an appeal possible and exposes systematic false positives that a single run can hide.
Privacy, deployment and integration trade-offs
| Priority | Most suitable starting point | Reason | Trade-off |
|---|---|---|---|
| Local processing and restricted data | JPlag | Local, structure-aware comparison | You manage collection, storage and review |
| Free peer-only baseline | MOSS or Dolos | No paid subscription is documented for the baseline use cases | No documented GitHub/web discovery in this comparison |
| Peer plus GitHub/web and AI signals | Codequiry | One managed workflow with dashboard, API and CLI options | Recurring cost and hosted-data review |
| Automated private-repository pipeline | Copyleaks | Source-code API, private-repository hub and webhooks | Pricing and independent accuracy figures are not stated here |
Common failure modes and fixes
A high score is treated as a verdict
Fix: Inspect the highlighted syntax and context. Common patterns can produce genuine similarity without copying.
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Starter code overwhelms the report
Fix: Identify instructor-supplied files and exclude or down-weight them where the selected tool supports that option. Keep a record of every exclusion.
The tool misses renamed or reformatted code
Fix: Prefer a syntax- or structure-aware comparator for peer batches, then validate it against examples with controlled renaming, formatting and refactoring.
Public-source results cannot be reproduced
Fix: Save the exact source link, repository revision or scan record at review time. Public repositories change, disappear or become private.
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Confidential code is uploaded without approval
Fix: Use a local tool when policy requires local processing, or obtain written approval after reviewing retention, encryption, access and deletion terms for a hosted service.
Which tool should you choose?
- Choose JPlag for local, privacy-sensitive comparison of multiple submissions where structure-aware matching matters.
- Choose MOSS or Dolos for a free peer-only baseline and manual inspection of highlighted pairs.
- Choose Codequiry when you need peer comparison plus GitHub/web links, AI-code signals and LMS, API or CLI integrations. Its published benchmark is promising but vendor-run, so validate it on your own data.
- Choose Copyleaks when an API-driven process must scan online sources, a shared data hub and private repositories. Confirm current pricing and contractual data terms first.
The most defensible process is often layered: run a free or local peer comparator to identify unusual pairs, add a source-discovery scan only when attribution is necessary, and have a trained reviewer examine the code and context before taking action.
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