Software Defect Density Prediction Using Deep Learning

Abstract

Delivering a reliable and high-quality software system to client is a big challenge in software development and evolution process. One of the software measures that confirm the quality of the system is the defect density. Practitioners usually need this measure during software development process or during a period of operation to indicate the reliability of software system. However, since predicting defect density before testing the modules is time consuming, managers need to build a prediction model that can help in detecting the defective modules. This process can reduce the testing cost and improve testing resources utilizations. The most intrinsic feature of software defect datasets is the data sparsity in the defect density which might bias the final prediction. Therefore, we use deep learning to build defect density prediction models and handle the inherit challenge of data sparsity in defect density. Deep learning has shown to be effective with sparse data. The constructed model has been evaluated against well-known machine learning methods over 28 public datasets. The obtained results confirmed that the deep learning model is generally more adequate than other machine models over the datasets with high and very high sparsity ratios, and competitive choice when the sparsity ratio is either medium or low INDEX TERMS Defect Density Prediction, Deep Learning, Data Sparsity, Machine Learning.

A soft computing approach for software defect density prediction

“…A normality test on the four metrics showed that the number of developers and code churn significantly affected defect density. Recently, Alghanim et al 38 proposed a deep learning model based on GRNN to predict the defect density.…”

Comparison of Feature Selection via Semi supervised denoising autoencoder and traditional approaches For Software Fault-prone Classification

“…However, various studies have indicated that DL techniques, which excel in learning semantic features, outperform ML techniques in software defect prediction (SDP) (Tong et al, 2018) Moreover, ML techniques rely on manual feature selection (Young et al, 2018). As a result, there has been a growing interest in exploring the application of autoencoders, a speci c type of neural network architecture, for software defect prediction in recent years (Eivazpour and Keyvanpour, 2019;Qasem et al, 2020;Alghanim et al, 2022;.…”

“…Overview of SDP studies based on machine learning and deeplearning techniques : This research proposes a defect prediction model based on deep learning with an attention mechanism. The study introduces an attention-based long short-term memory (LSTM) network to capture important features and patterns from software metrics Alghanim et al, 2022;…”