Abstract Software defect prediction is an important software quality assurance technique. Nevertheless, the prediction performance of the constructed model is easily susceptible to irrelevant or redundant features in the software projects and is not predominant enough. To address these two issues, a novel defect prediction model called SSEPG based on Stacked Sparse Denoising AutoEncoders (SSDAE) and Extreme Learning Maching (ELM) optimised by Particle Swarm Optimisation (PSO) and another complementary Gravitational Search Algorithm (GSA) are proposed in this paper, which has two main merits: (1) employ a novel deep neural network – SSDAE to extract new combined features, which can effectively learn the robust deep semantic feature representation. (2) integrate strong exploitation capacity of PSO with strong exploration capability of GSA to optimise the input weights and hidden layer biases of ELM, and utilise the superior discriminability of the enhanced ELM to predict the defective modules. The SSDAE is compared with eleven state‐of‐the‐art feature extraction methods in effect and efficiency, and the SSEPG model is compared with multiple baseline models that contain five classic defect predictors and three variants across 24 software defect projects. The experimental results exhibit the superiority of the SSDAE and the SSEPG on six evaluation metrics.

Just‐in‐time defect prediction is an important and useful branch in software defect prediction. At present, deep learning is a research hotspot in the field of artificial intelligence, which can combine basic defect features into deep semantic features and make up for the shortcomings of machine learning algorithms. However, the mainstream deep learning techniques have not been applied yet in just‐in‐time defect prediction. Therefore, the authors propose a novel just‐in‐time defect prediction model named DAECNN‐JDP based on denoising autoencoder and convolutional neural network in this study, which has three main advantages: (i) Different weights for the position vector of each dimension feature are set, which can be automatically trained by adaptive trainable vector. (ii) Through the training of denoising autoencoder, the input features that are not contaminated by noise can be obtained, thus learning more robust feature representation. (iii) The authors leverage a powerful representation‐learning technique, convolution neural network, to construct the basic change features into the abstract deep semantic features. To evaluate the performance of the DAECNN‐JDP model, they conduct extensive within‐project and cross‐project defect prediction experiments on six large open source projects. The experimental results demonstrate that the superiority of DAECNN‐JDP on five evaluation metrics.