Defect prediction is a very meaningful topic, particularly at change-level. Change-level defect prediction, which is also referred as just-in-time defect prediction, could not only ensure software quality in the development process, but also make the developers check and fix the defects in time. Nowadays, deep learning is a hot topic in the machine learning literature. Whether deep learning can be used to improve the performance of just-in-time defect prediction is still uninvestigated. In this paper, to bridge this research gap, we propose an approach Deeper which leverages deep learning techniques to predict defect-prone changes. We first build a set of expressive features from a set of initial change features by leveraging a deep belief network algorithm. Next, a machine learning classifier is built on the selected features. To evaluate the performance of our approach, we use datasets from six large open source projects, i.e., Bugzilla, Columba, JDT, Platform, Mozilla, and PostgreSQL, containing a total of 137,417 changes. We compare our approach with the approach proposed by Kamei et al. The experimental results show that on average across the 6 projects, Deeper could discover 32.22% more bugs than Kamei et al's approach (51.04% versus 18.82% on average). In addition, Deeper can achieve F1-scores of 0.22-0.63, which are statistically significantly higher than those of Kamei et al.'s approach on 4 out of the 6 projects.

Similar bugs are bugs that require handling of many common code files. Developers can often fix similar bugs with a shorter time and a higher quality since they can focus on fewer code files. Therefore, similar bug recommendation is a meaningful task which can improve development efficiency. Rocha et al. propose the first similar bug recommendation system named NextBug. Although NextBug performs better than a start-of-the-art duplicated bug detection technique REP, its performance is not optimal and thus more work is needed to improve its effectiveness. Technically, it is also rather simple as it relies only upon a standard information retrieval technique, i.e., cosine similarity. In the paper, we propose a novel approach to recommend similar bugs. The approach combines a traditional information retrieval technique and a word embedding technique, and takes bug titles and descriptions as well as bug product and component information into consideration. To evaluate the approach, we use datasets from two popular open-source projects, i.e., Eclipse and Mozilla, each of which contains bug reports whose bug ids range from [1,400000]. The results show that our approach improves the performance of NextBug statistically significantly and substantially for both projects.

Alzheimer’s Disease is a neurodegenerative disorder, and one of its common and prominent early symptoms is language impairment. Therefore, early diagnosis of Alzheimer’s Disease through speech and text information is of significant importance. However, the multimodal data is often complex and inconsistent, which leads to inadequate feature extraction. To address the problem, We propose a model for early diagnosis of Alzheimer’s Disease based on multimodal attention(EDAMM). Specifically, we first evaluate and select three optimal feature extraction methods, Wav2Vec2.0, TF-IDF and Word2Vec, to extract acoustic and linguistic features. Next, by leveraging self-attention mechanism and cross-modal attention mechanisms, we generate fused features to enhance and capture the inter-modal correlation information. Finally, we concatenate the multimodal features into a composite feature vector and employ a Neural Network(NN) classifier to diagnose Alzheimer’s Disease. To evaluate EDAMM, we perform experiments on two public datasets, i.e., NCMMSC2021 and ADReSSo. The results show that EDAMM improves the performance of Alzheimer’s Disease diagnosis over state-of-the-art baseline approaches on both datasets.