#Etal snagit hotkey pdf#
#Etal snagit hotkey software#
#Etal snagit hotkey professional#

ActivitySpace uses operating-system level instrumentation to track developer interactions with a wide range of applications in real work settings. First, we use our ActivitySpace framework to improve the generalizability of the data collection. In this paper, we propose an approach to address the above two challenges in collecting and analyzing developers’ behavior data. This abstraction is often performed manually or based on simple heuristics. Furthermore, 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.

#Etal snagit hotkey software#

However, instrumenting all software tools commonly used in real work settings is difficult and requires significant human effort. This is feasible for studies on development activities using specific software tools. Researchers usually instrument the software tools to log developers’ behavior for further studies. This creates significant challenges in collecting and analyzing developers’ behavior data.

#Etal snagit hotkey pdf#

In modern software development, developers frequently use different applications including IDEs, Web Browsers, documentation software (such as Office Word, Excel, and PDF applications), and other tools to complete their tasks. Studying developers’ behavior in software development tasks is crucial for designing effective techniques and tools to support developers’ daily work. These share datasets as well as the proposed dataset construction method help to promote research progress in SBR prediction research domain. Finally, we construct a large-scale SBR dataset with 191 SBRs and 88,472 NSBRs (non-security bug reports) from OpenStack and improve the quality of existing SBR dataset Chromium by identifying 64 new SBRs from previously labeled NSBRs and filtering out 173 noise bug reports from this dataset. The correctness of these labels are then manually reviewed by three experienced security testing members. By using this approach, we label over 80k bug reports from OpenStack and 40k bug reports from Chromium. 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).

#Etal snagit hotkey professional#

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.