Bug Detection and Assignment for Mobile Apps via Mining Users' Reviews
Subject Areas : electrical and computer engineeringMaryam Younesi 1 , Abbas Heydarnoori 2 * , F. Ghanadi 3
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Keywords: Opinion miningapp storesbug assignmentsoftware maintenancemachine learning,
Abstract :
Increasing the popularity of smart phones and the great ovation of users of mobile apps has turned the app stores to massive software repositories. Therefore, using these repositories can be useful for improving the quality of the program. Since the bridge between users and developers of mobile apps is the comments that users write in app stores, special attention to these comments from developers can make a dramatic improvement in the final quality of mobile apps. Hence, in recent years, numerous studies have been conducted around the topic of opinion mining, whose intention was to extract and exert important information from user's reviews. One of the shortcomings of these studies is the inability to use the information contained in user comments to expedite and improve the process of fixing the software error. Hence, this paper provides an approach based on users’ feedback for assigning program bugs to developers. This approach builds on the history of a program using its commit data, as well as developers' ability in fixing a program’s errors using the bugs that developers have already resolved in the app. Then, by combining these two criteria, each developer will get a score for her appropriation for considering each review. Next, a list of developers who are appropriate for each bug are provided. The evaluations show that the proposed method would be able to identify the right developer to address the comments with a precision of 74%.
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