Keitaro Nakasai

Keitaro Nakasai, Hideaki Hata, Kenichi Matsumoto

Eclipse, an open source software project, acknowledges its donors by presenting donation badges in its issue tracking system Bugzilla. However, the rewarding effect of this strategy is currently unknown. We applied a framework of causal inference to investigate relative promptness of developer response to bug reports with donation badges compared with bug reports without the badges, and estimated that donation badges decreases developer response time by a median time of about two hours. The appearance of donation badges is appealing for both donors and organizers because of its practical, rewarding and yet inexpensive effect.

Yusuf Sulistyo Nugroho, Syful Islam, Keitaro Nakasai et al.

Although many software development projects have moved their developer discussion forums to generic platforms such as Stack Overflow, Eclipse has been steadfast in hosting their self-supported community forums. While recent studies show forums share similarities to generic communication channels, it is unknown how project-specific forums are utilized. In this paper, we analyze 832,058 forum threads and their linkages to four systems with 2,170 connected contributors to understand the participation, content and sentiment. Results show that Seniors are the most active participants to respond bug and non-bug-related threads in the forums (i.e., 66.1% and 45.5%), and sentiment among developers are inconsistent while knowledge sharing within Eclipse. We recommend the users to identify appropriate topics and ask in a positive procedural way when joining forums. For developers, preparing project-specific forums could be an option to bridge the communication between members. Irrespective of the popularity of Stack Overflow, we argue the benefits of using project-specific forum initiatives, such as GitHub Discussions, are needed to cultivate a community and its ecosystem.

Teruki Hayakawa, Masateru Tsunoda, Koji Toda et al.

Various software fault prediction models and techniques for building algorithms have been proposed. Many studies have compared and evaluated them to identify the most effective ones. However, in most cases, such models and techniques do not have the best performance on every dataset. This is because there is diversity of software development datasets, and therefore, there is a risk that the selected model or technique shows bad performance on a certain dataset. To avoid selecting a low accuracy model, we apply bandit algorithms to predict faults. Consider a case where player has 100 coins to bet on several slot machines. Ordinary usage of software fault prediction is analogous to the player betting all 100 coins in one slot machine. In contrast, bandit algorithms bet one coin on each machine (i.e., use prediction models) step-by-step to seek the best machine. In the experiment, we developed an artificial dataset that includes 100 modules, 15 of which include faults. Then, we developed various artificial fault prediction models and selected them dynamically using bandit algorithms. The Thomson sampling algorithm showed the best or second-best prediction performance compared with using only one prediction model.