scholarly journals An optimized feature selection using fuzzy mutual information based ant colony optimization for software defect prediction

2017 ◽  
Vol 7 (1.1) ◽  
pp. 456
Author(s):  
G Manivasagam ◽  
R Gunasundari
Keyword(s):  
Feature Selection ◽  
Software Engineering ◽  
Mutual Information ◽  
Ant Colony Optimization ◽  
Optimization Technique ◽  
Ant Colony ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Software Defects ◽  
Software Defect

In recent years, there is a significant notification focused towards the prediction of software defect in the field of software engineering. The prediction of software defects assist in reducing the cost of testing effort, improving the process of software testing and to concentrate only on the fault-prone software modules. Recently, software defect prediction is an important research topic in the software engineering field. One of the important factors which effect the software defect detection is the presence of noisy features in the dataset. The objective of this proposed work is to contribute an optimization technique for the selection of potential features to improve the prediction capability of software defects more accurately. The Fuzzy Mutual Information Ant Colony Optimization is used for searching the optimal feature set with the ability of Meta heuristic search. This proposed feature selection efficiency is evaluated using the datasets from NASA metric data repository. Simulation results have indicated that the proposed method makes an impressive enhancement in the prediction of routine for three different classifiers used in this work.

scholarly journals Towards Predicting Software Defects with Clustering Techniques

2021 ◽  
Vol 12 (1) ◽  
pp. 39-54
Author(s):  
Waheeda Almayyan
Keyword(s):  
Feature Selection ◽  
Predictive Model ◽  
Machine Learning Techniques ◽  
Defect Prediction ◽  
Grey Wolf Optimizer ◽  
Software Defect Prediction ◽  
Software Defects ◽  
Particle Swarm Optimizer ◽  
Clustering Techniques ◽  
Software Defect

The purpose of software defect prediction is to improve the quality of a software project by building a predictive model to decide whether a software module is or is not fault prone. In recent years, much research in using machine learning techniques in this topic has been performed. Our aim was to evaluate the performance of clustering techniques with feature selection schemes to address the problem of software defect prediction problem. We analysed the National Aeronautics and Space Administration (NASA) dataset benchmarks using three clustering algorithms: (1) Farthest First, (2) X-Means, and (3) selforganizing map (SOM). In order to evaluate different feature selection algorithms, this article presents a comparative analysis involving software defects prediction based on Bat, Cuckoo, Grey Wolf Optimizer (GWO), and particle swarm optimizer (PSO). The results obtained with the proposed clustering models enabled us to build an efficient predictive model with a satisfactory detection rate and acceptable number of features.

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