Jul 2026· international journal of engineering trends and technology· Vol 74, pp. 294-308· 0 citations
TL;DR
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).
Abstract
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.
Continuous integration (CI) environments are increasingly challenged by the rapid growth in test cases and code changes, rendering exhaustive regression testing impractical. In our previous work [1], [2], we introduced TCP-Net and TCP-Net++, deep neural network models for test case prioritization that showed promising improvements in fault detection. However, these models struggled to fully capture the sequential dependencies inherent in evolving CI systems. In this paper, we present TE-TCP-Net, a novel transformer-based model that leverages parallel transformer encoders to dynamically learn from historical test execution data, source code modifications, and test case characteristics. By effectively modeling sequential relationships across multiple CI builds, TE-TCP-Net achieves superior performance in terms of Average Percentage of Faults Detected (APFD) and Normalized APFD (NAPFD) compared to state-of-the-art methods. The results show that TE-TCP-Net is very effective, indicating its potential for practical usage in real-world scenarios. We validate our approach on two industrial datasets, Calibre PERC and Calibre xRC, where TE-TCP-Net significantly improves early fault detection and overall testing efficiency, establishing a new benchmark for test case prioritization in dynamic CI environments.
Automated software fault localization has become a research focus with the growing software scale. Deep learning-based fault localization (DLFL) has been widely studied due to its excellent performance. Most existing DLFL methods use the execution traces of the test cases as features. However, the number of passing test cases is far more than that of failed test cases in the practical test suite. The severe class imbalance problem is ignored in existing DLFL methods. To alleviate the impact of the class imbalance on DLFL, we propose applying cost-sensitive learning to set different weights for each sample to optimize the performance of fault localization. We reduce the number of passing test cases and compute the weight for each sample. The sample weight is combined with a loss function to optimize the accuracy of the DLFL method. To evaluate our approach, we conduct within-project and cross-project fault localization experiments on the Defects4J benchmark. Experimental results show our approach outperforms the traditional DLFL and existing data augmentation methods in fault localization (e.g., localizing 133 faults within Top-5). The experimental results show that applying cost-sensitive learning can effectively alleviate the class imbalance problem in DLFL.
Yue Yan, Shujuan Jiang, Yanmei Zhang et al.· International journal of sof...· 0 citations
Test Case Prioritization (TCP) aims to reorder test cases to enable earlier fault detection in Continuous Integration pipelines. Machine learning-based TCP approaches have shown promising results but require substantial historical execution data, creating a cold-start challenge for new or rapidly evolving projects. This paper presents an empirical study of direct transfer learning, where models trained on data-rich source projects are applied to unseen targets without any retraining. A total of 578 experiments are conducted across 17 Defects4J projects (34 within-project baselines and 544 cross-project transfer scenarios) using 270,611 feature vectors. Results show that with appropriate source selection, cross-project transfer can match or surpass within-project baselines. A three-way feature ablation (HIS-only, COV-only, full 13-feature) finds that no feature subset consistently exceeds random ordering (E[APFD]≈0.5) on average, confirming that source selection is the critical variable. Analysis identifies failure-rate alignment and source dataset richness as key predictors of transfer quality, providing source-selection criteria that require no target-side data collection.
Michael Johnson, Khaled Slhoub· International Conference on...· 0 citations
: Open-source platforms and issue tracking systems such as GitHub and Jira generate large volumes of issue reports and code changes, making effective bug identification a challenging task. This study investigates software bug prediction by integrating various feature extraction methods, including Word2Vec, TF-IDF, FastText, GloVe, and Doc2Vec, with several lightweight ML algorithms. Hybrid feature sets are further enhanced using Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) and empirical results indicate that Word2Vec and Multi-Layer Perceptron (MLP) provide comparatively stronger performance. The study proposes a Hybrid Attention-Residual Multilayer Perceptron (HAR-MLP) model to automatically classify software issue reports as bugs and feature requests. The model is equipped with a feature set consisting of a combination of TF-IDF reweighted embedding averages derived from Word2Vec embedding vectors, mean-maximum-standard deviation pooling statistics, and DCT spectral coefficients. This feature fusion is then combined with context-sensitive representations obtained from a multi-headed self-attention mechanism and fed as input to the Residual MLP model as a classifier. While traditional MLP has direct forward connections between layers, Residual MLP reduces gradient decay and prevents information loss in deep layers by combining intermediate layer outputs with the input using the skip connection method. This structure increases the model’s learning capacity and provides higher accuracy, especially in hybrid structures where attention outputs and handcrafted features are processed together. Experimental results demonstrate that the HAR-MLP model achieves higher performance than traditional feature extraction and classification methods while remaining computationally competitive compared to pre-trained transformer-based approaches. Moreover, cross-platform evaluation on three independent Jira issue datasets further demonstrates the generalizability of the proposed architecture, achieving macro-F1 scores ranging from 86.61% to 97.48%. The source code of the proposed HAR-MLP model can be accessed via the following link: https://github.com/isilkarabeyaksakalli/HAR-MLP.
Isil Karabey Aksakalli· Computers, Materials & C...· 0 citations
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.
I. Alharbi· International Conference on...· 0 citations
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
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