Software Fault Localization through Aggregation-Based Neural Ranking for Static and Dynamic Features Selection

Alhumam, Abdulaziz

doi:10.3390/s21217401

Cited by 5 publications

(2 citation statements)

References 49 publications

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“…When independent factors have varying effects on the class label, weighing the attributes to boost performance, and the in-class labeling is feasible. The normalized mutual information (NMI) [62] is used as the feature weight among each feature and class label. This dataset contains two parameters, α, and β, over the sample i, j concerning the class label c. Here, the class label of the instances is required to determine the normalized mutual information.…”

Section: Explainable Feature Weight Initialization and Normalizationmentioning

confidence: 99%

XAI Framework for Cardiovascular Disease Prediction Using Classification Techniques

Guleria¹,

Srinivasu

Ahmed

et al. 2022

Electronics

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Machine intelligence models are robust in classifying the datasets for data analytics and for predicting the insights that would assist in making clinical decisions. The models would assist in the disease prognosis and preliminary disease investigation, which is crucial for effective treatment. There is a massive demand for the interpretability and explainability of decision models in the present day. The models’ trustworthiness can be attained through deploying the ensemble classification models in the eXplainable Artificial Intelligence (XAI) framework. In the current study, the role of ensemble classifiers over the XAI framework for predicting heart disease from the cardiovascular datasets is carried out. There are 303 instances and 14 attributes in the cardiovascular dataset taken for the proposed work. The attribute characteristics in the dataset are categorical, integer, and real type and the associated task related to the dataset is classification. The classification techniques, such as the support vector machine (SVM), AdaBoost, K-nearest neighbor (KNN), bagging, logistic regression (LR), and naive Bayes, are considered for classification purposes. The experimental outcome of each of those algorithms is compared to each other and with the conventional way of implementing the classification models. The efficiency of the XAI-based classification models is reasonably fair, compared to the other state-of-the-art models, which are assessed using the various evaluation metrics, such as area under curve (AUC), receiver operating characteristic (ROC), sensitivity, specificity, and the F1-score. The performances of the XAI-driven SVM, LR, and naive Bayes are robust, with an accuracy of 89%, which is assumed to be reasonably fair, compared to the existing models.

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Section: Explainable Feature Weight Initialization and Normalizationmentioning

confidence: 99%

XAI Framework for Cardiovascular Disease Prediction Using Classification Techniques

Guleria¹,

Srinivasu

Ahmed

et al. 2022

Electronics

View full text Add to dashboard Cite

show abstract

“…Diverse classification models can Symmetry 2023, 15, 1562 2 of 21 be constructed using various machine intelligence techniques to conduct testing more efficiently. Different machine intelligence techniques have been explored to predict erroneous code statements in software modules to improve software quality and minimize software testing expenses [4]. The quantity and length of assessments administered significantly influence the efficacy of tests.…”

Section: Introductionmentioning

confidence: 99%

FEDRak: Federated Learning-Based Symmetric Code Statement Ranking Model for Software Fault Forecasting

Alhumam

2023

Symmetry

Self Cite

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Software Fault Forecasting (SFF) pertains to timely identifying sections in software projects that are prone to faults and may result in significant development expenses. Deep learning models have become widespread in software fault monitoring and management, and these models rely on the design metrics and the code pattern features for classifying the code as erroneous or safe. The proposed model works based on the collective formulation of the fault localization model, which acquires the model-specific metadata for building a global model that would perform software fault forecasting globally. The proposed model works by ranking the suspicious code blocks based on the symmetry of the semantic features of the erroneous code and the implementation code. The feature selection and scaling process is initially performed to precisely identify the features contributing to fault forecasting. The data extraction that is portrayed as the intermediate phase would assist in focusing on the code statements and ranking them based on the impact of the fault. A fine-tuned spectrum-based fault localization technique is used in ranking the statements. The FEDRak model facilitates ongoing adaptation in instances where there are adjustments in the feature contribution of data over time. The federated learning model would update the feature weights of the global model based on the weights synchronized by locally built fault forecasting approaches. FEDRak is statistically analyzed in relation to other contemporary techniques in fault localization in terms of metrics like sensitivity, specificity, accuracy, F1-score, and ROC curves. The proposed model’s performances are analyzed in terms of local and global models.

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Graph-based rank aggregation: a deep-learning approach

Keyhanipour

2024

IJWIS

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Purpose This study aims to introduce a novel rank aggregation algorithm that leverages graph theory and deep-learning to improve the accuracy and relevance of aggregated rankings in metasearch scenarios, particularly when faced with inconsistent and low-quality rank lists. By strategically selecting a subset of base rankers, the algorithm enhances the quality of the aggregated ranking while using only a subset of base rankers. Design/methodology/approach The proposed algorithm leverages a graph-based model to represent the interrelationships between base rankers. By applying Spectral clustering, the algorithm identifies a subset of top-performing base rankers based on their retrieval effectiveness. These selected rankers are then integrated into a sequential deep-learning model to estimate relevance labels for query-document pairs. Findings Empirical evaluation on the MQ2007-agg and MQ2008-agg data sets demonstrates the substantial performance gains achieved by the proposed algorithm compared to baseline methods, with an average improvement of 8.7% in MAP and 11.9% in NDCG@1. The algorithm’s effectiveness can be attributed to its ability to effectively integrate diverse perspectives from base rankers and capture complex relationships within the data. Originality/value This research presents a novel approach to rank aggregation that integrates graph theory and deep-learning. The author proposes a graph-based model to select the most effective subset for metasearch applications by constructing a similarity graph of base rankers. This innovative method addresses the challenges posed by inconsistent and low-quality rank lists, offering a unique solution to the problem.

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Software Fault Localization through Aggregation-Based Neural Ranking for Static and Dynamic Features Selection

Cited by 5 publications

References 49 publications

XAI Framework for Cardiovascular Disease Prediction Using Classification Techniques

XAI Framework for Cardiovascular Disease Prediction Using Classification Techniques

FEDRak: Federated Learning-Based Symmetric Code Statement Ranking Model for Software Fault Forecasting

Graph-based rank aggregation: a deep-learning approach

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