Authors :
Atul Pandey; Srujana Maddula; Gaddam Prathik Kumar; Sarthak Kumar Shailendra; Karan Mudaliar
Volume/Issue :
Volume 8 - 2023, Issue 12 - December
Google Scholar :
http://tinyurl.com/yc5h9j4f
Scribd :
http://tinyurl.com/49t96bza
DOI :
https://doi.org/10.5281/zenodo.10464708
Abstract :
In the expansive domain of software
engineering, the persistent challenge of fault prediction has
garnered scholarly interest in machine learning
methodologies, aiming to refine decision-making and
enhance software quality. This study pioneers advanced
fault prediction models, intertwining product and process
metrics through machine learning classifiers and ensemble
design. The methodological framework involves metric
identification, experimentation with machine learning
classifiers, and evaluation, considering cost dynamics.
Empirically, 42 diverse projects from PROMISE, BUG,
and JIRA repositories are examined, revealing advanced
models with ensemble methods manifesting an accuracy of
(91.7%), showcasing heightened predictive capabilities and
nuanced cost sensitivity. Non-parametric tests affirm
statistical significance, portraying innovation beyond
conventional paradigms. Conclusively, these advanced
models navigate inter-project fault prediction with finesse,
signifying a convergence of novelty and performance.
Simultaneously, anticipating fault proneness in software
components is a pivotal focus in software testing. Software
coupling and complexity metrics are critical for evaluating
software quality. Object-oriented metrics, including
inheritance, polymorphism, and encapsulation, influence
software quality and offer avenues for estimating fault
proneness. This study contributes a comprehensive
taxonomy to the discourse, offering a holistic perspective
on the multifaceted landscape of object-oriented metrics in
fault prediction within the broader context of advancing
software quality.
Keywords :
Software Fault Prediction; Object-Oriented Testing; Object-Oriented Coupling; Machine Learning, Ensemble Design, Product, and Process Metrics.
In the expansive domain of software
engineering, the persistent challenge of fault prediction has
garnered scholarly interest in machine learning
methodologies, aiming to refine decision-making and
enhance software quality. This study pioneers advanced
fault prediction models, intertwining product and process
metrics through machine learning classifiers and ensemble
design. The methodological framework involves metric
identification, experimentation with machine learning
classifiers, and evaluation, considering cost dynamics.
Empirically, 42 diverse projects from PROMISE, BUG,
and JIRA repositories are examined, revealing advanced
models with ensemble methods manifesting an accuracy of
(91.7%), showcasing heightened predictive capabilities and
nuanced cost sensitivity. Non-parametric tests affirm
statistical significance, portraying innovation beyond
conventional paradigms. Conclusively, these advanced
models navigate inter-project fault prediction with finesse,
signifying a convergence of novelty and performance.
Simultaneously, anticipating fault proneness in software
components is a pivotal focus in software testing. Software
coupling and complexity metrics are critical for evaluating
software quality. Object-oriented metrics, including
inheritance, polymorphism, and encapsulation, influence
software quality and offer avenues for estimating fault
proneness. This study contributes a comprehensive
taxonomy to the discourse, offering a holistic perspective
on the multifaceted landscape of object-oriented metrics in
fault prediction within the broader context of advancing
software quality.
Keywords :
Software Fault Prediction; Object-Oriented Testing; Object-Oriented Coupling; Machine Learning, Ensemble Design, Product, and Process Metrics.
