Optimizing Software Error Proneness Prediction Using Bird Mating Algorithm

Designing, developing, and maintaining are the key phases of a software life cycle, and the most essential property of a software product is quality. The quality of a software product is dependent on various factors, e.g., reliability, security, and efficiency. But the most important aspect of a software quality is its proper function as described by functional requirements of the software. Errors often occur that are the mistakes which hamper the correct functionality of the software. Thus, to deliver high-quality software, errors must not occur, and if they do then these must be removed. In this chapter, we suggest that the process of identifying and removing errors can be optimized if prior information about the module’s possible errors is known. Error proneness prediction can be modeled using classification and prediction techniques. In this context, artificial neural network is a classification model which can be used to predict error proneness. However, the neural network with gradient descent algorithm, e.g., backpropagation algorithm, has the inherent issue of getting stuck into local minima while training. To solve this issue, evolutionary algorithms such as genetic algorithm and bird mating algorithm focus on training of artificial neural. When the prediction model is formalized, receiver operating characteristic curve and accuracy curve are used to analyze the performance of the model. In this chapter, we present an error proneness approach using bird mating algorithm.

[1]  Hiroshi Motoda,et al.  Feature Selection for Knowledge Discovery and Data Mining , 1998, The Springer International Series in Engineering and Computer Science.

[2]  Sabrina Ahmad,et al.  Neural Network Parameter Optimization Based on Genetic Algorithm for Software Defect Prediction , 2014 .

[3]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[4]  Urbano Nunes,et al.  Novel Maximum-Margin Training Algorithms for Supervised Neural Networks , 2010, IEEE Transactions on Neural Networks.

[5]  Alireza Rezazadeh,et al.  Artificial neural network training using a new efficient optimization algorithm , 2013, Appl. Soft Comput..

[6]  Hak-Keung Lam,et al.  Tuning of the structure and parameters of a neural network using an improved genetic algorithm , 2003, IEEE Trans. Neural Networks.

[7]  A. Sudha,et al.  Software Defect Prediction System using Multilayer Perceptron Neural Network with Data Mining , 2014 .

[8]  Fuhui Long,et al.  Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy , 2003, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[10]  Ömer Faruk Arar,et al.  Software defect prediction using cost-sensitive neural network , 2015, Appl. Soft Comput..

[11]  Parvinder S. Sandhu,et al.  A model for early prediction of faults in software systems , 2010, 2010 The 2nd International Conference on Computer and Automation Engineering (ICCAE).

[12]  Darrell Whitley,et al.  A genetic algorithm tutorial , 1994, Statistics and Computing.

[13]  Robert A. Jacobs,et al.  Increased rates of convergence through learning rate adaptation , 1987, Neural Networks.

[14]  Jun Zheng,et al.  Cost-sensitive boosting neural networks for software defect prediction , 2010, Expert Syst. Appl..

[15]  Rozaida Ghazali,et al.  An Approach to Improve Back-propagation algorithm by Using Adaptive Gain( SOFT COMPUTING METHODOLOGIES AND ITS APPLICATIONS) , 2010 .

[16]  Ruchika Malhotra,et al.  Software defect prediction using neural networks , 2014, Proceedings of 3rd International Conference on Reliability, Infocom Technologies and Optimization.

[17]  Adam A. Porter,et al.  Empirically guided software development using metric-based classification trees , 1990, IEEE Software.

[18]  Dalwinder Singh Salaria,et al.  Software Defect Prediction Tool based on Neural Network , 2013 .

[19]  Claus Nebauer,et al.  Evaluation of convolutional neural networks for visual recognition , 1998, IEEE Trans. Neural Networks.