2020· International Journal of Machine Learning and Predictive Analytics· Vol 3, pp. 01-05· 0 citations
TL;DR
Experimental results demonstrate that the proposed Machine Learning-based risk assessment framework for SDLC can enhance risk prediction accuracy, support proactive mitigation strategies, and improve overall software project success rates.
Abstract
Risk assessment is a critical component of the Software Development Lifecycle (SDLC) to ensure timely delivery, maintain quality, and reduce project failures. Traditional risk assessment approaches rely heavily on expert judgment and manual analysis, which can be subjective and prone to errors. This paper proposes a Machine Learning (ML)-based risk assessment framework for SDLC, leveraging historical project data and software metrics to predict potential risks at different stages of development. Several ML algorithms, including Random Forest, Support Vector Machine, and Neural Networks, are evaluated for their effectiveness in identifying high-risk components. Experimental results demonstrate that the proposed approach can enhance risk prediction accuracy, support proactive mitigation strategies, and improve overall software project success rates. The framework provides a scalable and data-driven solution for early risk detection in modern software engineering practices.
The results demonstrate that ensemble methods provide superior performance in identifying reliability levels, and which classification method is most suitable for predicting software reliability based on code metrics such as Cyclomatic Complexity and Halstead Volume.
Nadir Subaşı, Ö. Özer· Osmaniye Korkut Ata Üniversi...· 0 citations
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.
Prasad Mathapati, S. G. Gollagi, Zebashireen Fahim Shaikh· International journal of res...· 0 citations
Information system development projects (ISDPs) frequently miss their planned schedules, and prior studies of ISDP success and failure have largely relied on surveys, interviews, and case analyses that limit objectivity and predictive use. This article proposes an audit-report-based early-warning framework and benchmar...
A review of deferent machine learning methods that are using for effort estimation like regression models, decision trees, random forest, neural networks, and then evaluate this models based on performance criteria such as MAE (Mean Absolute Error) and R 2 Score.
Montaser Fadulalla Ahmed Adam, Haroun Abdalla Eissa· 0 citations
The analysis indicates that combining predictive defect-risk scores with automated test selection can potentially reduce redundant testing, concentrate computational resources on high-risk software components, and improve feedback speed, but model reliability depends on historical defect data, feature quality, distribu...
Haruto Tanaka, Yuki Nakamura· Frontiers in Emerging Multid...· 0 citations
This paper presents a method using an uncertainty-aware heterogeneous ensemble of 100 bootstrap-trained base learners, which jointly produce point predictions and prediction intervals using a robust trimmed mean, with effort modeled on the log scale.
H. Sharif, Tara Nawzad Ahmad Al Attar, D. Rashid· UHD Journal of Science and T...· 0 citations
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