INTELLIGENT BUG PREDICTION SYSTEM

Automated software debugging and bug prediction through the use of machine learning and deep learning

The results indicate that traditional ML models, especially random forest and extra trees, are still very effective for metric-based defect prediction, while DL and multi-modal approaches need to be fed with richer software artifacts to reach their full potential.

Precision Software Defect Prediction Using Novel Machine Learning Approaches

Abstract - Software defect detection is a crucial area in software engineering focused on identifying issues within software systems. To achieve software success, it is essential to bridge the gap between software engineering and data mining. Various methods, including clustering, statistical techniques, matrix-based neural networks, black box & white box testing, & machine learning, remain employed toward predict software defects. These methods are applied to enhance accuracy in defect prediction through machine learning. Objective of this study is toward improving software defect prediction five publicly available NASA datasets: CM1, JM1, KC2, KC1, and PC1. Feature assortment performances are cohesive through ML methods, such as Random Forest to achieve higher defect prediction accuracy compared to methods without feature selection (WOFS) and to get matrix problem we use Classification Network. A defect is defined as an imperfection caused by an error, fault, or failure in the software development process. In this context, an "error" refers to human actions leading to undesirable outcomes, while a "defect" denotes a decision resulting in incorrect outcomes when attempting to resolve a problem. Software defect prediction has gained prominence in recent years due to its direct impact on software quality. Defective software modules can affect product quality, leading to cost overruns, delays in project timelines, and increased maintenance expenses. Key Words:  software defect prediction five publicly available NASA datasets: CM1, JM1, KC2, KC1, and PC1.

Software Failure Prediction and Efficiency Optimization Using AI/ML Techniques

Software reliability remains a major concern in modern software engineering due to the increasing complexity of software systems and the rapid pace of development. Software Failure Prediction (SFP) aims to identify fault-prone modules before deployment, enabling organizations to reduce maintenance costs and improve system quality. Artificial Intelligence (AI) and Machine Learning (ML) techniques provide data-driven approaches for analyzing software metrics, defect repositories, and execution logs to predict failures. This review examines AI/ML-based approaches for software failure prediction and efficiency optimization, with explicit attention to benchmark datasets, dataset quality, preprocessing, class imbalance, feature selection, model families, validation strategies, and evaluation measures. Representative NASA and PROMISE/Jureczko datasets are characterized in terms of software-unit type, metric families, binary defect labels, and imbalance. The review emphasizes Precision, Recall, F1-score, ROC-AUC, and MCC in addition to accuracy and compares traditional ML, ensemble, and deep-learning approaches. It further discusses missing data, concept drift, and cross-project prediction as key factors affecting real-world generalization. The analysis indicates that no single model is universally optimal; robust software failure prediction requires dataset-aware preprocessing, leakage-safe validation, imbalance-aware evaluation, and an explicit trade-off among predictive performance, computational efficiency, and interpretability.

Cross Project Software Defect Prediction Using Machine Learning with Optimized Feature Selection

In smart city software systems, where interconnected services demand high reliability, Software Defect Prediction (SDP) plays a vital role and reducing maintenance costs by identifying defect-prone modules early in the Software Development Life Cycle (SDLC). Cross-Project Defect Prediction (CPDP) enables defect data from source projects to predict defects in target projects with scarce labels; However, its effectiveness is hindered by feature redundancy, heterogeneous data distributions, and severe class imbalance. To address these challenges, this study proposes an integrated CPDP framework that combines Particle Swarm Optimization with Domain Knowledge (PSO+DK) for feature selection and Adaptive Synthetic Sampling (ADASYN) for class imbalance handling. Experiments were conducted on five widely used NASA datasets; CM1, PC1,PC2, PC3, and PC4 —using Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) classifiers. Results show that PSO+DK enhanced the discriminative power of the models, with the framework achieving the highest accuracy of 0.9785 on PC2 dataset. Furthermore, ADASYN show minimal differences over SMOTE in improving classifier robustness. Highlights promising directions for deploying reliable cross-project prediction in smart city software development

An Explainable Feature Selection and Stacking Ensemble Framework for Software Fault Prediction

Overall, the findings indicate that integrating principled feature selection with a boosting-based stacking ensemble can improve software fault prediction performance while providing greater transparency for software quality management.

March 2026: Top 10 Cited Articles in Software Engineering & Applications