International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 03 | Mar 2025
p-ISSN: 2395-0072
www.irjet.net
Software Bug Prediction: Ensuring Trustworthiness in Software Development: A Review Rishit Kantaria Assistant Professor, Department of Information Technology, Dr Subhash University, Gujarat, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract – Software Bug Prediction is an important
programming languages, feature engineering, and dimensionality reduction techniques on bug prediction accuracy. Finally, we discuss future research directions, emphasizing the need for hybrid models, explainable AI, and real-world applications of predictive analytics in software engineering.
research domain which is much useful for the Software Developers and IT Industries. Software Bugs increases the cost of whole Software Development Life Cycle (SDLC).
This review paper consist of comprehensive analysis of recent advancements in Software Bug Prediction in field of Machine Learning , Deep Learning and Applying Models on Live Workflow. As data pre-processing plays a vital role in any of the Machine Learning Models , this paper also discuss the role of various pre-processing techniques like Dimensionality Reduction , Feature Extraction and Feature Prediction to improve the accuracy of a Model.
The rest of this review paper is organized as follows: It provides an overview of traditional and modern software bug prediction techniques, including machine learning and deep learning approaches. Focuses and discusses key challenges in bug prediction, such as data imbalance, feature selection, and model interpretability. Additionally, it presents a comparative analysis of existing methodologies, evaluating their effectiveness and limitations. It also explores emerging trends, including explainable AI and just-in-time defect prediction. Finally, it also summarizes the findings and suggests future research directions for improving software bug prediction models."
Key Words: Bug Prediction, Machine Learning, Deep Learning, Dimensionality Reduction, Feature Extraction, Evaluation Metrics, Classification.
1. INTRODUCTION
2. OVERVIEW TRADITIONAL BUG PREDICTION TECHNIQUES
Software quality is a critical concern in modern software development, as defects and bugs can lead to significant financial losses, security vulnerabilities, and user dissatisfaction. To mitigate these risks, software bug prediction has emerged as a key research area, enabling early identification of potential defects before deployment. By leveraging historical code metrics, machine learning, deep learning, and statistical techniques, researchers have developed various predictive models to enhance software quality and reliability.
Traditional Bug Prediction Techniques primarily focuses on Software Metrics such as Lines of Code, Coupling, Cohesion, and Cyclomatic Complexity. Various Machine Learning Algorithms are also used for Bug Prediction like Logistic Regression, SVM, and Decision Trees. The underlying limitations in above Traditional Bug Prediction are the High False Positive and High False Negative Rates. The other limitation is the implementation of inappropriate Data Pre Processing Techniques.
Traditional bug prediction approaches rely on supervised learning algorithms such as decision trees, support vector machines, and artificial neural networks. More recent advancements include ensemble learning, hyper parameter optimization, and deep learning techniques that improve accuracy and scalability. Additionally, justin-time bug prediction models and explainable AI methods are gaining traction, offering real-time defect identification and interpretability.
Table -1: Comparison of Traditional and Modern Software Bug Prediction Techniques.
Despite significant progress, software bug prediction faces challenges such as imbalanced datasets, feature selection complexity, and model generalization across different projects. This review paper provides a comprehensive analysis of existing bug prediction methodologies, evaluating their strengths, limitations, and emerging trends. Furthermore, it highlights the impact of
© 2025, IRJET
|
Impact Factor value: 8.315
|
Feature
Traditional Bug Prediction Models
Modern Software Bug Prediction Models
Approach
Software Metrics , Code Metrics.
Deep Learning and Ensemble Learning Techniques.
Data Used
Historical Data
Real time Data
Algorithms
Logistic Regression, Decision Trees, SVM, Naïve
Neural Networks, Random Forest, XGBoost,
ISO 9001:2008 Certified Journal
|
Page 106