Dataset: A Fly in the Ointment: An Empirical Study on the Characteristics of Ethereum Smart Contracts Code Weaknesses and Vulnerabilities
Bibliographic record
Abstract
Dataset: A Fly in the Ointment: An Empirical Study on the Characteristics of Ethereum Smart Contracts Code Weaknesses and Vulnerabilities Majd Soud, Grischa Liebel, Mohammad Hamdaqa majd18@ru.is, grischal@ru.is, mhamdaqa@polymtl.ca. This Dataset includes the following: 1. "labeling.xml" files that represents the data for Categories of vulnerabilities in Smart Contracts for four data sources (i.e., Common Vulnerability and Exposure (CVE), Smart Contract Weakness Classification Registry (SWC), Stack Overflow, and GitHub) XML files structure: The XML files can be opened used any editor or any code editor (e.g. Visual Studio Code). 1. Each file has a root that is which contains all the cards we labeled. 2. Each card is represented by the and contains the following: - The tag marked by represents the keyword that was used to search and collect the card from StackOverflow. - The URL marked by of the URL link which contains all the information of the labeled vulnerability. - The other tages marked by that shows all the tags used in the post on Stack Overflow. - The expert labeling for the categories of vulnerabilities in each card is represented by - In more details, some records has the that represents the second expert labeling for the categories of vulnerabilities. - The used to calculate the inter-rater agreement between the two labelers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.016 |
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".