“Bounties on Technical Q&A Sites: A Case Study of Stack Overflow Bounties” accepted for publication in the EMSE journal!

Jiayuan’s paper “Bounties on Technical Q&A Sites: A Case Study of Stack Overflow Bounties” was accepted for publication in the EMSE journal! Super congrats Jiayuan!

Abstract:
Technical question and answer (Q&A) websites provide a platform for developers to communicate with each other by asking and answering questions. Stack Overflow is the most prominent of such websites. With the rapidly increasing number of questions on Stack Overflow, it is becoming difficult to get an answer to all questions and as a result, millions of questions on Stack Overflow remain unsolved. In an attempt to improve the visibility of unsolved questions, Stack Overflow introduced a bounty system to motivate users to solve such questions. In this bounty system, users can offer reputation points in an effort to encourage users to answer their question. In this paper, we study 129,202 bounty questions that were proposed by 61,824 bounty backers. We observe that bounty questions have a higher solving-likelihood than non-bounty questions. This is particularly true for long-standing unsolved questions. For example, questions that were unsolved for 100 days for which a bounty is proposed are more likely to be solved (55%) than those without bounties (1.7%). In addition, we studied the factors that are important for the solving-likelihood and solving-time of a bounty question. We found that: (1) Questions are likely to attract more traffic after receiving a bounty than non-bounty questions. (2) Bounties work particularly well in very large communities with a relatively low question solving-likelihood. (3) High-valued bounties are associated with a higher solving-likelihood, but we did not observe a likelihood for expedited solutions.

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“Identifying gameplay videos that exhibit bugs in computer games” accepted for publication in the EMSE journal!

Dayi’s paper “Identifying gameplay videos that exhibit bugs in computer games” was accepted for publication in the EMSE journal! Super congrats Dayi, and what a great way to wrap up your PhD!

Abstract:
With the rapid growing market and competition in the gaming industry, it is challenging to develop a successful game, making the quality of games very important. To improve the quality of games, developers commonly use gamer-submitted bug reports to locate bugs in games. Recently, gameplay videos have become popular in the gaming community. A few of these videos showcase a bug, offering developers a new opportunity to collect context-rich bug information. In this paper, we investigate whether videos that showcase a bug can automatically be identified from the metadata of gameplay videos that are readily available online. Such bug videos could then be used as a supplemental source of bug information for game developers. We studied the number of gameplay videos on the Steam platform, one of the most popular digital game distribution platforms, and the difficulty of identifying bug videos from these gameplay videos. We show that naive approaches such as using keywords to search for bug videos are time-consuming and imprecise. We propose an approach which uses a random forest classifier to rank gameplay videos based on their likelihood of being a bug video. Our proposed approach achieves a precision that is 43% higher than that of the naive keyword searching approach on a manually labelled dataset of 96 videos. In addition, by evaluating 1,400 videos that are identified by our approach as bug videos, we calculated that our approach has both a mean average precision at 10 and a mean average precision at 100 of 0.91. Our study demonstrates that it is feasible to automatically identify gameplay videos that showcase a bug.

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