Software Defect Prediction Using Atomic Rule Mining and Random Forest

Thapa, Suroj; Alsadoon, Abeer; Prasad, P.W.C.; Al-Dala’in, Thair; Rashid, Tarik A.

doi:10.1109/citisia50690.2020.9371797

Cited by 1 publication

(1 citation statement)

References 13 publications

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“…To build these SDP models, researchers have utilized various regression [11] and classification techniques, such as support vector machine [12], the k-nearest neighbor algorithm [13], random forest algorithms [14], deep learning methods incorporating artificial neural networks [15], recurrent neural networks [16] and convolutional neural networks [17], ensemble methods [18], and the transfer learning framework [19], etc. Like these studies, our SDP models will be built using existing machine learning algorithms to support our research questions.…”

Section: Literature Reviewmentioning

confidence: 99%

Interpretable Software Defect Prediction from Project Effort and Static Code Metrics

Haldar,

Capretz

2024

Computers

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Software defect prediction models enable test managers to predict defect-prone modules and assist with delivering quality products. A test manager would be willing to identify the attributes that can influence defect prediction and should be able to trust the model outcomes. The objective of this research is to create software defect prediction models with a focus on interpretability. Additionally, it aims to investigate the impact of size, complexity, and other source code metrics on the prediction of software defects. This research also assesses the reliability of cross-project defect prediction. Well-known machine learning techniques, such as support vector machines, k-nearest neighbors, random forest classifiers, and artificial neural networks, were applied to publicly available PROMISE datasets. The interpretability of this approach was demonstrated by SHapley Additive exPlanations (SHAP) and local interpretable model-agnostic explanations (LIME) techniques. The developed interpretable software defect prediction models showed reliability on independent and cross-project data. Finally, the results demonstrate that static code metrics can contribute to the defect prediction models, and the inclusion of explainability assists in establishing trust in the developed models.

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