Aug 2026· International Conference on Automated Software Engineering· Vol 33· 0 citations· 38 references
TL;DR
DeepQual-Web is introduced, a single multimodal deep learning framework for comprehensive quality assessment and optimization of web applications that combines Gradient Boosting Regression and Bidirectional Long Short-Term Memory networks to interpret the performance and reliability attributes of execution logs and system metrics.
The engineering details of ADEPT are presented, a framework that integrates representative adequacy techniques, including neuron-coverage-based metrics, surprise adequacy, input distribution coverage, boundary coverage, and source- and model-level mutation score, under a consistent execution workflow.
Yidi Kao, Shawn Burnham, Tommi Rose Fahy et al.· 0 citations
An Explainable Artificial Intelligence (XAI) driven framework for developing composite, transparent software quality metrics that integrate predictive accuracy with multi-level interpretability and provides a scalable and extensible foundation for transparent AI-driven software engineering tools.
Abdulaziz Attaallah, Khalil Al Sulbi· IEEE Access· 0 citations
This research proposes an Explainable Machine Learning (XML)–based framework to assess software quality by integrating code metrics, defect datasets, and advanced interpretability methods such as SHAP, LIME, and permutation importance.
Nandhini Ravi· International Journal of Mac...· 0 citations
The significance of this work lies in demonstrating that a deployable, explainable detector can be assembled from compact components, and an edge-type ablation study, a cross-dataset evaluation, and a per-vulnerability analysis are reported to characterize the approach.
A. Elalfy, G. Ebrahim, M. B. Mansour· Computers· 0 citations
A novel An Intelligent Deep Learning Model for Automated Test Case Prioritization Systems using Neural Networks (Deep-TCP) has been proposed and the efficacy of the proposed Deep-TCP framework has been determined using evaluation metrics such as Average Percentage of Faults Detected (APFD).
S. S, Shashi Mehrotra· international journal of eng...· 0 citations
It is indicated that natural-language descriptions can support automated severity prediction, while also showing that model performance depends on project-specific data characteristics and data availability.
Paul Teye, Stephen Opoku Oppong, Dickson Keddy Wornyo et al.· Asian Journal of Research in...· 0 citations
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