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

Alhumam, Abdulaziz

doi:10.3390/s21217401

Cited by 4 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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Model Checking Leveraged Error Localization for Complex RTL Designs

Srinivasan

Vemuri

2022

2022 IEEE 40th International Conference on Computer Design (ICCD)

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

Cited by 4 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

Model Checking Leveraged Error Localization for Complex RTL Designs

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