Usage of Machine Learning Methods for Cause-Effect Graph Feasibility Prediction

Abstract

Cause-effect graphs (CEGs) are usually applied for black-box testing of complex industrial systems. The specification process is time-consuming and can result in many errors. In this work, machine learning methods were applied for predicting the feasibility of CEG elements. All information was extracted from graphs contained in CEGSet, a dataset of CEGs. The data was converted to two different formats. The Boolean features format represents relations as separate data rows, whereas the Term-Frequency times Inverse-Document-Frequency (TF-IDF) format represents graphs as data rows. Eight machine learning models were trained on this data. The results of testing by using the 80–20 holdout method indicate that important information is lost when converting the graphs to the TF-IDF format, whereas the Boolean feature format enables 100%-accurate predictions of ensemble methods. The achieved results indicate that pre-trained models can be used as help for domain experts during the CEG specification process.

Usage of cause‐effect graphs for reliability analysis of an embedded IoT alarm software system

“…Jang and Kim later developed a software tool 27 for automatically generating a cause-effect graph from the textual representation of a system, localized to the Korean language. We recently collected CEGSet, 28 a dataset containing 65 cause-effect graph specifications, and applied machine learning methods 29 in order to successfully train a model for detecting the feasibility of cause-effect graph elements.…”