Predicting Community Smells’ Occurrence on Individual Developers by Sentiments
Community smells appear in sub-optimal software development community structures, causing unforeseen additional project costs, e.g., lower productivity and more technical debt. Previous studies analyzed and predicted community smells in the granularity of community sub-groups using socio-technical factors. However, refactoring such smells requires the effort of developers individually. To eliminate them, supportive measures for every developer should be constructed according to their motifs and working states. Recent work revealed developers’ personalities could influence community smells’ variation, and their sentiments could impact productivity. Thus, sentiments could be evaluated to predict community smells’ occurrence on them. To this aim, this paper builds a developer-oriented and sentiment-aware community smell prediction model considering 3 smells such as Organizational Silo, Lone Wolf, and Bottleneck. Furthermore, it also predicts if a developer quitted the community after being affected by any smell. The proposed model achieves cross- and within-project prediction F-Measure ranging from 76% to 93%. Research also reveals 6 sentimental features having stronger predictive power compared with activeness metrics. Imperative and indicative expressions, politeness, and several emotions are the most powerful predictors. Finally, we test statistically the mean and distribution of sentimental features. Based on our findings, we suggest developers should communicate in a straightforward and polite way.
Wed 19 MayDisplayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change
02:40 - 03:10
|Exploiting the Unique Expression for Improved Sentiment Analysis in Software Engineering Text
Kexin Sun , Hui Gao Nanjing University, Hongyu Kuang Nanjing University, Xiaoxing Ma Nanjing University, Guoping Rong Nanjing University, Dong Shao Nanjing University, He Zhang Nanjing UniversityPre-print Media Attached
|Predicting Community Smells’ Occurrence on Individual Developers by Sentiments
Zijie Huang East China University of Science and Technology, Zhiqing Shao , Guisheng Fan , Jianhua Gao , Ziyi Zhou , Kang Yang , Xingguang YangPre-print Media Attached
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