How are solidity smart contracts tested in open source projects?
Bibliographic record
Abstract
Smart contracts are self-executing programs that are stored on the blockchain. Once a smart contract is compiled and deployed on the blockchain, it cannot be modified. Therefore, having a bug-free smart contract is vital. To ensure a bug-free smart contract, it must be tested thoroughly. However, little is known about how developers test smart contracts in practice. Our study explores 139 open source smart contract projects that are written in Solidity to investigate the state of smart contract testing from three dimensions: (1) the developers working on the tests, (2) the used testing frameworks and testnets and (3) the type of tests that are conducted. We found that mostly core developers of a project are responsible for testing the contracts. Second, developers typically use only functional testing frameworks to test a smart contract, with Truffle being the most popular one. Finally, our results show that functional testing is conducted in most of the studied projects (93%), security testing is only performed in a few projects (9.4%) and traditional performance testing is conducted in none. In addition, we found 34 projects that mentioned or published external audit reports.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.069 | 0.381 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.016 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".