![]() The accuracy of the prediction model is improved with an iterative voting strategy. It starts with a small group of ground-truth traing samples, which can be labeled with the help of authoritative vulnerability records hosted in CVE (Common Vulnerabilities and Exposures). To solve this issue, we propose an automated data labeling approach based on iterative voting classification. However, the effectiveness of the state-of-the-art machine learning models depend on high-quality datasets, while gathering large-scale datasets are expensive and tedious. ![]() ![]() Machine learning are promising ways for SBR prediction. Identifying SBRs (security bug reports) is crucial for eliminating security issues during software development. The proposed CRF model achieves better performance (i.e., 0.728 accuracy and 0.672 macro-averaged F1-score) than the other three baselines. We conduct an experiment with the collected data and a small number of initial human-labeled training data using the CRF model and the other three baselines (i.e., a heuristic-rules based method, a SVM classifier, and a random weighted classifier). To evaluate our proposed approach, we deploy the ActivitySpace framework in an industry partner's company and collect the real working data from ten professional developers' one-week work. Then, we propose a Condition Random Field (CRF) based approach to segment and label the developers' low-level actions into a set of basic, yet meaningful development activities. ![]() In this paper, to address these two challenges, we first use our ActivitySpace framework to improve the generalizability of the data collection. Second, the collected behavior data consist of low-level and fine-grained event sequences, which must be abstracted into high-level development activities for further analysis. First, instrumenting many software tools commonly used in real work settings (e.g., IDEs, web browsers) is difficult and requires significant resources. However, there are two challenges in collecting and analyzing developers' behavior data. Studying developers' behavior is crucial for designing effective techniques and tools to support developers' daily work. ![]()
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