Bugs that persist into releases of video games can have negative impacts on both developers and users, but particular aspects of testing in game development can lead to difficulties in effectively catching these missed bugs. It has become common practice for developers to apply updates to games in order to fix missed bugs. These updates are often accompanied by notes that describe the changes to the game included in the update. However, some bugs reappear even after an update attempts to fix them. In this paper, we develop a taxonomy for bug types in games that is based on prior work. We examine 12,122 bug fixes from 723 updates for 30 popular games on the Steam platform. We label the bug fixes included in these updates to identify the frequency of these different bug types, the rate at which bug types recur over multiple updates, and which bug types are treated as more severe. Additionally, we survey game developers regarding their experience with different bug types and what aspects of game development they most strongly associate with bug appearance. We find that Information bugs appear the most frequently in updates, while Crash bugs recur the most frequently and are often treated as more severe than other bug types. Finally, we find that challenges in testing, code quality, and bug reproduction have a close association with bug persistence. These findings should help developers identify which aspects of game development could benefit from greater attention in order to prevent bugs. Researchers can use our results in devising tools and methods to better identify and address certain bug types.

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Conversational systems use spoken language to interact with their users. Although conversational systems, such as Amazon Alexa, are becoming common and can provide interesting functionalities, there is li le known about the issues users of these systems face.In this paper, we study user reviews of more than 2,800 Alexa skills to understand the characteristics of the reviews and the issues that they raise. Our results suggest that most skills receive fewer than 50 reviews. Our qualitative study of user reviews using open coding resulted in identifying 16 types of issues in the user reviews. Issues related to content, integration with online services and devices, errors, and regression are the top issues raised by the users. Our results also indicate di erences in volume and types of complaints by users when compared with more traditional mobile applications. We discuss the implication of our results for practitioners and researchers.

Internet of Things (IoT) systems are bundles of networked sensors and actuators that are deployed in an environment and act upon the sensory data that they receive. These systems, especially consumer electronics, have two main cooperating components: a device and a mobile app. The unique combination of hardware and software in IoT systems presents challenges that are lesser known to mainstream software developers.They might require innovative solutions to support the development and integration of such systems.In this paper, we analyze more than 90,000 reviews of ten IoT devices and their corresponding apps and extract the issues that users encountered while using these systems. Our results indicate that issues with connectivity, timing, and updates are particularly prevalent in the reviews. Our results call for a new softwarehardware development framework to assist the development of reliable IoT systems.

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