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.
Over the past decade, many test adequacy metrics have been proposed for deep learning that characterize test dataset adequacy from different perspectives, e.g., neuron activation behavior, latent feature coverage, decision-boundary exploration, etc. However, these metrics are typically released as independent research prototypes with substantially different installation and preprocessing requirements, execution workflows, and configuration mechanisms. These complications make them quite difficult to reproduce, compare, and adopt in research work and practical deployment alike. In this paper, we present the engineering details of ADEPT, 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. ADEPT provides a template-based metric interface with well-defined extension points for integrating new adequacy metrics. Furthermore, it provides YAML-based configuration management, preprocessing-cache reuse, and structured result reporting, making it easy to use in any research and development workflows. ADEPT is designed for researchers and practitioners who wish to reproduce and apply adequacy metrics without spending days or weeks implementing missing tooling or configuring disparate research prototypes. A demo video is available at https://aub.ie/ADEPT_video.
Yidi Kao, Shawn Burnham, Tommi Rose Fahy et al.· 0 citations
Accurate software quality prediction is critical for early defect identification and effective allocation of testing resources. Although machine learning (ML) and deep learning (DL) models have significantly improved defect prediction performance, their opaque nature limits transparency, interpretability, and practitioner trust. This study presents an Explainable Artificial Intelligence (XAI) driven framework for developing composite, transparent software quality metrics that integrate predictive accuracy with multi-level interpretability. The framework has been implemented and empirically validated using multiple real-world datasets. The framework combines static code metrics, process metrics, and developer activity metrics to construct robust defect prediction models using Random Forest, Gradient Boosted Trees (e.g., via the XGBoost implementation), Support Vector Machines (SVM), Logistic Regression, and attention-based Neural Networks. To enhance transparency, model agnostic explanation techniques, including SHAP and LIME, are integrated with in-model attention mechanisms to provide global and local explanations of predictions. Experiments conducted on PROMISE, open-source, and industrial datasets demonstrate that the proposed composite explainable metric achieves superior performance on the PROMISE (NASA-JM1) dataset, with the Gradient Boosted Trees model reaching an Accuracy of 0.85 and an AUC of 0.88, and consistently outperforms traditional and opaque baselines across the Eclipse JDT, Apache Commons, and Industrial datasets. Quantitative faithfulness analysis shows strong alignment between SHAP explanations and model behavior (Pearson’s r= 0.81). A controlled user study involving software developers reports a 14% improvement in decision accuracy and significantly higher confidence levels (p< 0.01) when using explainable metrics. The framework further incorporates interactive visual dashboards and textual summaries to support actionable decision-making during quality assurance processes. By bridging the gap between predictive performance and interpretability, this research demonstrates that explainability not only enhances trust but also improves the effectiveness of practical defect triage. The methodology provides a scalable and extensible foundation for transparent AI-driven software engineering tools.
Abdulaziz Attaallah, Khalil Al Sulbi· IEEE Access· 0 citations
Ensuring software quality is critical for the reliability, maintainability, and usability of modern software systems. Traditional software quality assessment techniques often rely on manual reviews, static analysis, or classical machine learning models that offer limited interpretability. 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. The study evaluates multiple ML models—Random Forest, Gradient Boosting, XGBoost, and Neural Networks—to predict software quality attributes including reliability, maintainability, and defect proneness. Explainability techniques are applied to interpret model decisions, identify key quality indicators, and provide insights useful for developers, testers, and project managers. Experimental results demonstrate that explainable ML improves both predictive performance and decision transparency, making it suitable for practical software engineering environments. This research highlights how combining ML with explainability techniques enhances trust, interpretability, and actionable insights in software quality assessment.
Nandhini Ravi· International Journal of Mac...· 0 citations
The deployment of deep-learning vulnerability detectors in production remains difficult. Models are large, false-positive rates are high, output is opaque, and a persistent gap separates benchmark performance from real-world utility. The objective of this work is to close part of that gap by combining a learned detector with interpretable rules so that accuracy, efficiency, and actionability are achieved together. A hybrid framework is therefore presented in which a lightweight edge-conditioned GNN of 71,810 parameters, named FastVulnGNN, trained in 96.2 s on a single CPU core, is paired with rule-based heuristic detection for six C/C++ vulnerability classes, namely buffer overflows, format-string defects, null-pointer dereferences, double-free errors, integer overflows, and race conditions. On the MegaVul dataset, an accuracy of 71.1%, an F1 score of 0.70, and an AUC-ROC of 0.77 are obtained by the GNN component. On a production codebase of 499 files and 312,758 lines of code, the full hybrid scan completes in 5.5 s, which corresponds to about 57,000 lines per second, without any GPU hardware. Per-file risk tiers and pattern-level explanations are produced, and these are suitable for continuous-integration use. 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.
Ahmed M. Elalfy, G. Ebrahim, M. B. Mansour· Computers· 0 citations
Test case prioritization is the process of ordering test cases based on their importance to improve regression testing efficiency. Test case prioritization approaches have been demonstrated to improve regression testing processes. But operating the whole regression test suite can be inconvenient and costly, particularly for large systems. To overcome this problem, a novel An Intelligent Deep Learning Model for Automated Test Case Prioritization Systems using Neural Networks (Deep-TCP) has been proposed. Test cases are collected from the source code repository and split into test steps. The test steps are preprocessed using Normalization, Tokenization and stemming to remove noise. After pre-processing, word embedding is computed and the embedded features are clustered using K-means clustering. The novelty of the proposed integration is where the effort is of word embedding, K-means clustering, Radial Basis Function Network (RBFN), and CNN-BiGRU to capture both semantic and sequential connections between test cases. Radial Basis Function Network (RBFN) is used for extracting the relevant features and Convolutional Neural Network-Bidirectional Gated Recurrent Unit (CNN-BiGRU) is used for testcase priority such as high priority, average priority and low priority. The proposed framework has been implemented using Python (PyTorch) and evaluated on a system with NVIDIA RTX 4090 GPU. The efficacy of the proposed Deep-TCP framework has been determined using evaluation metrics such as Average Percentage of Faults Detected (APFD). Average APFD of the proposed method is 94.5% which is higher than 71.25% at ATRL-TCP, 73.5% at QAOA-TCS and 81.5% at BootQA approaches.
S. S, Shashi Mehrotra· international journal of eng...· 0 citations
Software vulnerability assessment is essential for prioritising remediation, yet the manual assignment of Common Vulnerability Scoring System metrics is time-consuming, subjective, and prone to error. This study develops and evaluates a design science artefact for predicting vulnerability severity directly from natural-language descriptions. More than 15,000 confirmed vulnerability records were collected from ten open-source projects represented in the CVE-NVD repositories. The descriptions were cleaned through punctuation removal, tokenisation, stopword removal, and Porter stemming. Textual features were represented using n-grams, term frequency-inverse document frequency, and Word2Vec, while Information Gain was applied for feature selection. A deep neural network with three hidden layers was developed for binary classification of vulnerabilities as Severe or Non-Severe. Its performance was compared with Random Forest, Support Vector Machine, Decision Tree, k-nearest neighbours, and Naïve Bayes classifiers using stratified 10-fold cross-validation. The deep neural network achieved average accuracy, precision, and recall values of 71.2%, 68.5%, and 72.0%, respectively, and generally outperformed the conventional classifiers across the reported metrics. However, performance varied among projects, with AUC-ROC values ranging from 0.385 for Gentoo to 0.720 for Windows 7, and the Decision Tree exceeded the deep neural network on the Gentoo dataset. These findings indicate 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