Akademik Veri Yönetim Sistemi
7th International Conference on Computer Science and Engineering, UBMK 2022, Diyarbakır, Türkiye, 14 - 16 Eylül 2022, ss.110-115
© 2022 IEEE.In software projects, bug reports remain open and get updates from team members during the related bug's lifetime. It is an important task to predict when a bug would be resolved so that managers plan timeline and allocate team resources accordingly. Prior works show that reporter information is the most effective feature for predicting resolution time. Previous work only considers bug reporting activity to define reporter reputation and misses other activities. In this paper, we propose a new reputation calculation method that considers all activities of a reporter within a bug tracking system. We collected bug reports of Chromium and WebRTC projects and calculated reputations of each bug reporter within the dataset using our weighted, activity based calculation method as well as other methods from previous work in order to compare performance. We trained a Doc2Vec model to utilize textual information in bug reports to build a base model for comparing different reputation methods. Bug reports are classified into two categories as FAST and SLOW according to their resolution time. Stochastic Gradient Descent (SGD) and Extreme Gradient Boost (XGB) classifier algorithms are employed. XGB classifier resulted with 55 % - 72 % F -Scores for FAST and SLOW respectively. The results also show that data characteristics of project affects the effectiveness of the reputation calculation method on bug resolution time prediction.