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Interpretable Software Defect Prediction from Project Effort and Static Code Metrics

https://doi.org/10.3390/computers13020052

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Proceedings of the 2nd International Conference on Emerging Technologies and Intelligent Systems

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Outlier Analysis

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Understanding machine learning software defect predictions

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Software defect prediction techniques using metrics based on neural network classifier

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A novel feature selection method for data mining tasks using hybrid Sine Cosine Algorithm and Genetic Algorithm

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A Hybrid Instance Selection Using Nearest-Neighbor for Cross-Project Defect Prediction

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Software defect prediction ensemble learning algorithm based on adaptive variable sparrow search algorithm

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Effective software defect prediction using support vector machines (SVMs)

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Software defect prediction via optimal trained convolutional neural network

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A three-stage transfer learning framework for multi-source cross-project software defect prediction

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Advances in neural information processing systems 7

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Random Forests

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Software defect prediction model based on LLE and SVM

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The misuse of the NASA Metrics Data Program data sets for automated software defect prediction

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Progress on approaches to software defect prediction

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Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)

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DeepCPDP: Deep Learning Based Cross-Project Defect Prediction

inferredcites →https://doi.org/10.1109/aitc.2019.8921374openalex-snapshot

Software Defect Prediction using Hybrid Approach

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Arrhythmia Classification Using Hybrid Feature Selection Approach and Ensemble Learning Technique

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Software Defect Prediction Using Atomic Rule Mining and Random Forest

inferredcites →https://doi.org/10.1109/icces48766.2020.9137899openalex-snapshot

Noise Filtering and Imbalance Class Distribution Removal for Optimizing Software Fault Prediction using Best Software Metrics Suite

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A Study of Hyper-Parameter Tuning in The Field of Software Analytics

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2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS)

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Explainable Software Defect Prediction from Cross Company Project Metrics using Machine Learning

inferredcites →https://doi.org/10.1109/icices.2013.6508369openalex-snapshot

Software defect prediction using software metrics - A survey

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Software Defect Prediction Using SMOTE and Artificial Neural Network

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Software defect prediction using neural networks

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Proceedings of 3rd International Conference on Reliability, Infocom Technologies and Optimization

inferredcites →https://doi.org/10.1109/icrse.2015.7366475openalex-snapshot

Software defect prediction via transfer learning based neural network

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2015 First International Conference on Reliability Systems Engineering (ICRSE)

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Hyperparameter optimization to improve bug prediction accuracy

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Correlation based Feature Selection impact on the classification of breast cancer patients response to neoadjuvant chemotherapy

inferredcites →https://doi.org/10.1109/msr.2017.18openalex-snapshot

A Large-Scale Study of the Impact of Feature Selection Techniques on Defect Classification Models

inferredcites →https://doi.org/10.1109/msr45733.2019openalex-snapshot

2019 IEEE/ACM 16th International Conference on Mining Software Repositories (MSR)

inferredcites →https://doi.org/10.1109/msr52588.2021.00055openalex-snapshot

Practitioners’ Perceptions of the Goals and Visual Explanations of Defect Prediction Models

inferredcites →https://doi.org/10.1109/nafips.2007.383813openalex-snapshot

Applying Novel Resampling Strategies To Software Defect Prediction

inferredcites →https://doi.org/10.1109/seatuc.2018.8788881openalex-snapshot

Software Defect Prediction Using Random Forest Algorithm

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IEEE Transactions on Software Engineering

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Nearest neighbor pattern classification

inferredcites →https://doi.org/10.1109/tr.2018.2847353openalex-snapshot

Ridge and Lasso Regression Models for Cross-Version Defect Prediction

inferredcites →https://doi.org/10.1109/tse.2007.256941openalex-snapshot

Data Mining Static Code Attributes to Learn Defect Predictors

inferredcites →https://doi.org/10.1109/tse.2013.11openalex-snapshot

Data Quality: Some Comments on the NASA Software Defect Datasets

inferredcites →https://doi.org/10.1109/tse.2020.2982385openalex-snapshot

An Empirical Study of Model-Agnostic Techniques for Defect Prediction Models

inferredcites →https://doi.org/10.1109/tse.2021.3079841openalex-snapshot

On the Value of Oversampling for Deep Learning in Software Defect Prediction

inferredcites →https://doi.org/10.1109/ubmk50275.2020.9219531openalex-snapshot

Performance Evaluation of Some Machine Learning Algorithms in NASA Defect Prediction Data Sets

inferredcites →https://doi.org/10.1109/ubmk52708.2021openalex-snapshot

2021 6th International Conference on Computer Science and Engineering (UBMK)

inferredcites →https://doi.org/10.1109/ubmk55850.2022.9919490openalex-snapshot

Explainable AI for Software Defect Prediction with Gradient Boosting Classifier

inferredcites →https://doi.org/10.1145/2939672.2939778openalex-snapshot

"Why Should I Trust You?"

inferredcites →https://doi.org/10.1145/3474124.3474127openalex-snapshot

Applying Cognitive and Neural Network Approach over Control Flow Graph for Software Defect Prediction

inferredcites →https://doi.org/10.1155/2019/6230953openalex-snapshot

Software Defect Prediction via Attention-Based Recurrent Neural Network

inferredcites →https://doi.org/10.1201/9780429027192openalex-snapshot

Explanatory Model Analysis

inferredcites →https://doi.org/10.1201/9780429027192-11openalex-snapshot

Local Interpretable Model-agnostic Explanations (LIME)

inferredcites →https://doi.org/10.1613/jair.953openalex-snapshot

SMOTE: Synthetic Minority Over-sampling Technique

inferredcites →https://doi.org/10.3390/electronics10020179openalex-snapshot

Empirical Analysis of Rank Aggregation-Based Multi-Filter Feature Selection Methods in Software Defect Prediction

inferredcites →https://doi.org/10.48550/arxiv.1201.0490openalex-snapshot

Scikit-learn: Machine Learning in Python

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Feature Selection: A Data Perspective

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Benchmarking Machine Learning Techniques for Software Defect Detection

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Explainable-AI-assisted feature selection for software change-proneness prediction

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A comparative study of Kolmogorov-Arnold networks and multilayer perceptrons for software defect prediction with SHAP analysis

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Enhanced software defect prediction using krill herd algorithm with stacked LSTM with attention mechanism

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Explainable deep learning for software defect prediction: a residual-shuffle network approach with SHAP and LIME

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Software defect prediction via contrastive similarity learning and inter-sample attention

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QualiGuard: An AI-driven risk-based quality gate for defect prediction and code smell detection in DevOps pipelines

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Exploring Consistent Feature Selection for Software Fault Prediction: An XAI-Based Model-Agnostic Approach

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Feature Importance in the Context of Traditional and Just-In-Time Software Defect Prediction Models

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Prediction of Software Defects for Quality Assurance and Improvement Based on Machine Learning Methods

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Enhancing Cross-Project Defect Prediction for Automated Software Fault Detection

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A Bibliometric Analysis of AI-Driven Adaptive and Predictive Testing in Agile Software Quality Assurance

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Leveraging CatBoost and XAI for Enhanced Software Defect Prediction

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Machine Learning-Powered Defect Prediction Models for Scalable Software Testing Development

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Hybrid Learning-to-Rank Regression Framework for Intelligent Software Fault Prediction

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Research on Domain Adaptation Methods of Interpretable Machine Learning in Cross-Project Software Defect Prediction

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Enhancing Software Defect Prediction through Explainable AI: Integrating SHAP and LIME in a Voting Classifier Framework

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Software Defect Propagation Analysis and Impact Domain Prediction Based on Code Knowledge Graph

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Dynamics of Software Defect Prediction: A Systematic Literature Review on Within-Project and Cross-Project Approaches

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FQDA-ML: A Hierarchical Machine Learning-Powered Data Quality Framework for Evaluating Agile Sprint Performance: A Case Study in Community Engagement Projects

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An Empirical Study of Cross-Project and Within-Project Performance in Software Defect Prediction Models Using Tree-Based and Boosting Classifiers

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Multilayer Perceptron of Software Complexity Metrics for Explainable Multicollinearity Mitigation and Defect Localization

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