Explainable-ML-based Framework to Predict Software Defects in Real-Time Control Systems
2025 2nd International Conference on Computational Intelligence, Communication Technology and Networking (CICTN)(2025)
Department of Comp. Sci. & Engg.
Abstract
Machine Learning (ML) algorithms have significantly improved software engineering activities by examining software metrics and repositories, leading to more accurate predictions and improved software quality. Here, we have used five Machine learning models, which are Random Forest, Bootstrap aggregating, Extreme Gradient Boosting algorithm and K-Nearest Neighbours algorithm, to compare which performs the best in software fault prediction, specifically targeting real-time control systems where reliability and responsiveness are critical. Comparing the results of the model we infer that the boosting algorithm has the highest accuracy, while still maintaining other evaluation scores. After thorough evaluation we got the maximum accuracy of 0.78 with the bagging algorithm. We have also added a layer of transparency by using X-AI. For this, we have used Shapley Additive Explanation (SHAP) and Local Interpretable Model (LIME). The results from both techniques support each other, and the feature which has the maximum impact is the number of lines of code. X-AI enhances predictability by unveiling the black box and revealing what features have the most impact on classification, which is especially important in ensuring the stability and safety of real-time control systems.
MoreTranslated text
Key words
ML,X-AI,SHAP,LIME
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined