Software Defect Prediction Based on Elman Neural Network and Cuckoo Search Algorithm

Kun Song,Die Hu,Peng He,Shengkai Lv,Yun-Wen Feng

doi:10.1155/2021/5954432

Abstract

In software engineering, defect prediction is significantly important and challenging. The main task is to predict the defect proneness of the modules. It helps developers find bugs effectively and prioritize their testing efforts. At present, a lot of valuable researches have been done on this topic. However, few studies take into account the impact of time factors on the prediction results. Therefore, in this paper, we propose an improved Elman neural network model to enhance the adaptability of the defect prediction model to the time-varying characteristics. Specifically, we optimized the initial weights and thresholds of the Elman neural network by incorporating adaptive step size in the Cuckoo Search (CS) algorithm. We evaluated the proposed model on 7 projects collected from public PROMISE repositories. The results suggest that the contribution of the improved CS algorithm to Elman neural network model is prominent, and the prediction performance of our method is better than that of 5 baselines in terms of F-measure and Cliff’s Delta values. The F-measure values are generally increased with a maximum growth rate of 49.5% for the POI project.

Highlights

With the increasing complexity of software and people’s continuous demand for low cost, high quality, and maintainability of software in daily life, it is almost impossible to develop a software without any defects
Previous research efforts to build accurate prediction models have been in either of the two following directions. e first one is the manual design of a set of specific software features to determine the defects, such as Halstead metrics [1] based on operand and operator counts, CK metrics [2] connected with function and inheritance counts, etc. e second one is code churn features [3] that contain the number of lines that are added/removed and the modified code, etc
To consider the time characteristics of software defect, we proposed a novel predictor based on Elman neural network optimized by an improved Cuckoo Search (CS) algorithm

Summary

Introduction

With the increasing complexity of software and people’s continuous demand for low cost, high quality, and maintainability of software in daily life, it is almost impossible to develop a software without any defects. It is essential to improve software quality before deployment, reduce system maintenance work, and detect and eliminate software defects early. Defect prediction techniques are often based on building models based on software metrics collected from similar projects or past releases. Such prediction models are used to classify the current project as defective or not defective. E first one is the manual design of a set of specific software features to determine the defects, such as Halstead metrics [1] based on operand and operator counts, CK metrics [2] connected with function and inheritance counts, etc. With the increasing size of the codes, early manual investigations reduce the efficiency and accuracy of the review, while increasing the review cost

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