Published April 27, 2025
| Version v2
Dataset
Open
Dataset of Optimization Methods for Model-Implemented Fault Injection in Cyber-Physical Systems: A Systematic Literature Review
Authors/Creators
- 1. University of Antwerp
-
2.
Flanders Make (Belgium)
-
3.
University of Luxembourg
Description
Data set for the paper entitled “Optimization Methods for Model-Implemented Fault Injection in Cyber-Physical Systems: a Systematic Literature Review”
In this repo, we have some pictures and Excel files.
- Pictures are screenshots from the Parsifal tool (https://parsif.al/) which we use for performing the SLR.
- Excel files are as follows:
| Excel’s file name | Description |
| Keyword_analysis | In this file, you can see the evolution of our keyword selection. |
| Articles_InclusionExclusion_QA | In this file, you can find all found papers until Feb. 27, 2025. In the last column of this excel file, we can see the status of each paper, if it has been included, or excluded by authors. For the included paper (their status is “Accepted”) you can see their quality score in the last column. |
| Extracted_data | In this file, we logged the result of data extraction from qualified paper. In the first sheet “Articles”, you can see a list of the read papers with corresponding data. Other sheets in this Excel file are driven from the “Article” sheet for data visualization. So, they are not important. |
If you have any questions, you can read the corresponding paper and contact the authors.
The paper can be found at https://doi.org/10.1016/j.jss.2026.112957
Files
data extraction 1.JPG
Files
(1.4 MB)
| Name | Size | |
|---|---|---|
|
md5:8b2b6beb188b2bc3c638bda5cf91d17b
|
964.8 kB | Download |
|
md5:d66e1e253331d40330eaa1b9aff2fea0
|
76.6 kB | Preview Download |
|
md5:69a61465cc19577c3b0511a21a4ce61a
|
74.1 kB | Preview Download |
|
md5:4c4596b7ec6f8cc0672c193c09c2889b
|
58.8 kB | Preview Download |
|
md5:d0565837383c8c5172114b7a5fc17bf0
|
137.8 kB | Download |
|
md5:fa4dea5b50b388ff8f0606aacc3a928e
|
27.0 kB | Download |
|
md5:312ca7b5b85d7fe69cbb6ac39599311e
|
96.8 kB | Preview Download |
|
md5:64ddbb2c82a07b08aae85d0baf5974bd
|
3.2 kB | Download |
