Abstract
In this paper, we propose an approach to assess the ability of developers based on their behavior data from OSS. Specifically, we classify developers' ability into code ability, project management ability, and social ability. Code efficiency is related to the developer's commit record and the pull-request record. The developer's project management ability is achieved by tracking the developer's commit record. We use regular matching to map the commit behavior to the project management behavior and calculate the developer's project management ability according to the proportion of different behaviors. The social ability of developers is related to the data that developers interact with in the open-source community. We dug for developer reviews on commit, issue, and gist fragments. By calculating the proportion of positive emotions in developer reviews and the proportion of developers interacting with others in the reviews, the social ability of developers is obtained. We get behavioral data from 50 random developers. Twitter's data is used to test the effect of different machine learning algorithms on the accuracy of developer comment polarity judgments. It is found that the combination of SVM, xgboost and random forest have the highest prediction accuracy. Finally, we select 5 students to use Likert scale to score the results. Our score shows that the results are basically in line with expectations.
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