Issues with SZZ: An empirical assessment of the state of practice of defect prediction data collection

Defect prediction research has a strong reliance on published data sets that are shared between researchers. The SZZ algorithm is the de facto standard for collecting defect labels for this kind of data and is used by most public data sets. Thus, problems with the SZZ algorithm may have a strong indirect impact on almost the complete state of the art of defect prediction. Recent research uncovered potential problems in different parts of the SZZ algorithm. Within this article, we provide an extensive empirical analysis of the defect labels created with the SZZ algorithm. We used a combination of manual validation and adopted or improved heuristics for the collection of defect data to establish ground truth data for bug fixing commits, improved the heuristic for the identification of inducing changes for defects, as well as the assignment of bugs to releases. We conducted an empirical study on 398 releases of 38 Apache projects and found that only half of the bug fixing commits determined by SZZ are actually bug fixing. Moreover, if a six month time frame is used in combination with SZZ to determine which bugs affect a release, one file is incorrectly labeled as defective for every file that is correctly labeled as defective. In addition, two defective files are missed. We also explored the impact of the relatively small set of features that are available in most defect prediction data sets, as there are multiple publications that indicate that, e.g., churn related features are important for defect prediction. We found that the difference of using more features is negligible.Comment: Submitted and under review. First three authors are equally contributin

A Novel Self-Supervised Learning-Based Anomaly Node Detection Method Based on an Autoencoder in Wireless Sensor Networks

Due to the issue that existing wireless sensor network (WSN)-based anomaly detection methods only consider and analyze temporal features, in this paper, a self-supervised learning-based anomaly node detection method based on an autoencoder is designed. This method integrates temporal WSN data flow feature extraction, spatial position feature extraction and intermodal WSN correlation feature extraction into the design of the autoencoder to make full use of the spatial and temporal information of the WSN for anomaly detection. First, a fully connected network is used to extract the temporal features of nodes by considering a single mode from a local spatial perspective. Second, a graph neural network (GNN) is used to introduce the WSN topology from a global spatial perspective for anomaly detection and extract the spatial and temporal features of the data flows of nodes and their neighbors by considering a single mode. Then, the adaptive fusion method involving weighted summation is used to extract the relevant features between different models. In addition, this paper introduces a gated recurrent unit (GRU) to solve the long-term dependence problem of the time dimension. Eventually, the reconstructed output of the decoder and the hidden layer representation of the autoencoder are fed into a fully connected network to calculate the anomaly probability of the current system. Since the spatial feature extraction operation is advanced, the designed method can be applied to the task of large-scale network anomaly detection by adding a clustering operation. Experiments show that the designed method outperforms the baselines, and the F1 score reaches 90.6%, which is 5.2% higher than those of the existing anomaly detection methods based on unsupervised reconstruction and prediction. Code and model are available at https://github.com/GuetYe/anomaly_detection/GLS

プログラムの解析、テスト、修復のための表現学習

学位の種別: 課程博士審査委員会委員 : （主査）東京大学特任准教授 松尾 豊, 東京大学教授 和泉 潔, 東京大学准教授 阿部 力也, 東京大学准教授 森 純一郎, 国立情報学研究所教授 蓮尾 一郎University of Tokyo(東京大学