Cost-Sensitive Radial Basis Function Neural Network Classifier for Software Defect Prediction

Abstract

Effective prediction of software modules, those that are prone to defects, will enable software developers to achieve efficient allocation of resources and to concentrate on quality assurance activities. The process of software development life cycle basically includes design, analysis, implementation, testing, and release phases. Generally, software testing is a critical task in the software development process wherein it is to save time and budget by detecting defects at the earliest and deliver a product without defects to the customers. This testing phase should be carefully operated in an effective manner to release a defect-free (bug-free) software product to the customers. In order to improve the software testing process, fault prediction methods identify the software parts that are more noted to be defect-prone. This paper proposes a prediction approach based on conventional radial basis function neural network (RBFNN) and the novel adaptive dimensional biogeography based optimization (ADBBO) model. The developed ADBBO based RBFNN model is tested with five publicly available datasets from the NASA data program repository. The computed results prove the effectiveness of the proposed ADBBO-RBFNN classifier approach with respect to the considered metrics in comparison with that of the early predictors available in the literature for the same datasets.

Machine Learning Empowered Software Defect Prediction System

“…Results showed that the proposed technique outperformed other modern methods. The researchers in [25] proposed a conventional radial basis function-based technique integrated with a novel adaptive dimensional biogeography-based optimization model for software-defect predication. Five NASA datasets from the PROMISE repository were used for experimental analysis, the results of which showed that the proposed technique is effective compared to earlier proposed models.…”

Performance Analysis of Machine Learning Techniques on Software Defect Prediction using NASA Datasets

“…Results reflected that Decision Tree achieved 0.8 and 0.9 AUC scores for AR1 and AR6 respectively which were better than other used techniques. Researchers in [6] presented a software defect prediction technique using Conventional Radial Basis Function along with novel Adaptive Dimensional Biogeography-based optimization model. For experiment, five NASA datasets from PROMISE repository were used and the results showed the higher accuracy of proposed technique as compared to early used techniques.…”

Towards Predicting Software Defects with Clustering Techniques

“…Software defect prediction has attracted the attention of scholars in knowledge discovery and data mining fields. Many scholars have considered numerous machine learning algorithms to tackle the classification problems related to software defect prediction [9][10][11][12][13][14][15][16][17][18].…”

“…Researchers in a study by [14], detected faulty components by applying the radial basis function neural network with novel adaptive dimensional biogeography-based optimization model to investigate five NASA datasets from the PROMISE repository. Results were satisfactory compared to conventional models.…”

“…We used the D'' version, available for download at [20]. This cleaned version has already been adapted in the literature [10][11][12]14,17,18]. Many researchers agree on considering D'' superior to its predecessor dataset as it does not contain redundant records and has a lower complexity level of data.…”