On the public citation graph
Software Security Vulnerability Prediction Modeling for PHP Systems
0 signed · 18 inferred · view as graph
Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated CiteStamped
badge you can embed.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-3-319-09940-8_19openalex-extracted
Less is More: Temporal Fault Predictive Performance over Multiple Hadoop Releases
inferredcites →https://doi.org/10.1016/0092-8674(89)90888-xopenalex-extracted
Regulation of gene expression during plant embryogenesis
inferredcites →https://doi.org/10.1016/j.simpat.2019.101951openalex-extracted
Optimum checkpoints for programs with loops
inferredcites →https://doi.org/10.1016/j.swevo.2016.10.002openalex-extracted
On the application of search-based techniques for software engineering predictive modeling: A systematic review and future directions
inferredcites →https://doi.org/10.1049/sfw2.12006openalex-extracted
Empirical studies on the impact of filter‐based ranking feature selection on security vulnerability prediction
inferredcites →https://doi.org/10.1109/issre.2014.32openalex-extracted
Predicting Vulnerable Components: Software Metrics vs Text Mining
inferredcites →https://doi.org/10.1109/scam.2016.15openalex-extracted
Vulnerability Prediction Models: A Case Study on the Linux Kernel
inferredcites →https://doi.org/10.1109/tr.2019.2924932openalex-extracted
Large-Scale Empirical Studies on Effort-Aware Security Vulnerability Prediction Methods
inferredcites →https://doi.org/10.3390/e24050651openalex-extracted
Examining the Capacity of Text Mining and Software Metrics in Vulnerability Prediction
inferredcites →https://doi.org/10.37190/e-inf230102openalex-extracted
The Effect of Dual Hyperparameter Optimization on Software Vulnerability Prediction Models
inferredcites →https://doi.org/10.48550/arxiv.2203.05181openalex-extracted
LineVD: Statement-level Vulnerability Detection using Graph Neural Networks
inferredcites →https://doi.org/10.5120/14492-2622openalex-extracted
Severity Assessment of Software Defect Reports using Text Classification
inferredcites →https://doi.org/10.5281/zenodo.1315961openalex-extracted
Hybrid Approach For Software Defect Prediction Using Machine Learning With Optimization Technique
inferredcites →https://doi.org/10.48620/88855openalex-snapshot
Vudenc: Vulnerability Detection with Deep Learning on a Natural Codebase for Python - Summary
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.7753/ijcatr1402.1002openalex-snapshot
Leveraging Artificial Intelligence to Enhance Process Control and Improve Efficiency in Manufacturing Industries
inferred← citeshttps://doi.org/10.7753/ijcatr1402.1003openalex-snapshot
Socioeconomic Disparities in Breast Cancer Care: Addressing Global Challenges in Oncology Outcomes
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.