Impact of feature selection methods on the predictive performance of software defect prediction models: an extensive empirical study
Feature selection (FS) is a feasible solution for mitigating high dimensionality problem, and
many FS methods have been proposed in the context of software defect prediction (SDP) …
many FS methods have been proposed in the context of software defect prediction (SDP) …
Performance analysis of feature selection methods in software defect prediction: a search method approach
Software Defect Prediction (SDP) models are built using software metrics derived from
software systems. The quality of SDP models depends largely on the quality of software …
software systems. The quality of SDP models depends largely on the quality of software …
An adaptive rank aggregation-based ensemble multi-filter feature selection method in software defect prediction
Feature selection is known to be an applicable solution to address the problem of high
dimensionality in software defect prediction (SDP). However, choosing an appropriate filter …
dimensionality in software defect prediction (SDP). However, choosing an appropriate filter …
Search-based wrapper feature selection methods in software defect prediction: an empirical analysis
High dimensionality is a data quality problem that negatively influences the predictive
capabilities of prediction models in software defect prediction (SDP). As a viable solution …
capabilities of prediction models in software defect prediction (SDP). As a viable solution …
Software defect prediction using wrapper feature selection based on dynamic re-ranking strategy
Finding defects early in a software system is a crucial task, as it creates adequate time for
fixing such defects using available resources. Strategies such as symmetric testing have …
fixing such defects using available resources. Strategies such as symmetric testing have …
A cluster-based hybrid feature selection method for defect prediction
F Wang, J Ai, Z Zou - 2019 IEEE 19th International Conference …, 2019 - ieeexplore.ieee.org
Machine learning is an effective method for software defect prediction. The performance of
learning models can be affected by irrelative and redundant features. Feature selection …
learning models can be affected by irrelative and redundant features. Feature selection …
An empirical study on pareto based multi-objective feature selection for software defect prediction
The performance of software defect prediction (SDP) models depend on the quality of
considered software features. Redundant features and irrelevant features may reduce the …
considered software features. Redundant features and irrelevant features may reduce the …
Empirical analysis of rank aggregation-based multi-filter feature selection methods in software defect prediction
Selecting the most suitable filter method that will produce a subset of features with the best
performance remains an open problem that is known as filter rank selection problem. A …
performance remains an open problem that is known as filter rank selection problem. A …
Empirical investigation of hyperparameter optimization for software defect count prediction
M Nevendra, P Singh - Expert Systems with Applications, 2022 - Elsevier
Prior identification of defects in software modules can help testers to allocate limited
resources efficiently. Defect prediction techniques are helpful for this situation because they …
resources efficiently. Defect prediction techniques are helpful for this situation because they …
A comparative study of iterative and non-iterative feature selection techniques for software defect prediction
TM Khoshgoftaar, K Gao, A Napolitano… - Information Systems …, 2014 - Springer
Two important problems which can affect the performance of classification models are high-
dimensionality (an overabundance of independent features in the dataset) and imbalanced …
dimensionality (an overabundance of independent features in the dataset) and imbalanced …