Stack Overflow has accumulated millions of answers. Informative comments can strengthen their associated answers (e.g., providing additional information). Currently, Stack Overflow hides comments that are ranked beyond the top 5. Stack Overflow aims to display more informative comments (i.e., the ones with higher scores) and hide less informative ones using this mechanism. As a result, 4.4 million comments are hidden under their answer threads. Therefore, it is very important to understand how well the current comment hiding mechanism works. In this study, we investigate whether the mechanism can effectively deliver informative comments while hiding uninformative comments. We find that: 1) Hidden comments are as informative as displayed comments; more than half of the comments (both hidden and displayed) are informative (e.g., providing alternative answers, or pointing out flaws in their associated answers). 2) The current comment hiding mechanism tends to rank and hide comments based on their creation time instead of their score in most cases due to the large amount of tie-scored comments (e.g., 87 of the comments have 0-score). 3) In 97.3 of answers that have hidden comments, at least one comment is hidden while there is another comment with the same score is displayed (i.e., we refer to such cases as unfairly hidden comments). Among such unfairly hidden comments, the longest unfairly hidden comment is more likely to be informative than the shortest unfairly displayed comments. Our findings suggest that Stack Overflow should consider adjusting their current comment hiding mechanism, e.g., displaying longer unfairly hidden comments to replace shorter unfairly displayed comments. We also recommend that users examine all comments, in case they would miss informative details such as software obsolescence, code error reports, or notices of security vulnerability in hidden comments.
To improve the quality of software systems, the software engineering community has proposed many approaches to leverage user's feedback. Since the feedback is based on first-hand experience from end-users, valuable insights can be mined to help developers identify patterns from issues and bugs. studied user reviews from iOS apps and analyzed how user complaints, such as functional errors and app crashes, negatively affect app ratings in the Apple iOS App Store @cite_14 . analyzed negative app reviews due to app updates in the Google Play Store, and found that updates with feature removal and user interface issues lead to the highest increase of negative reviews ratio @cite_12 . studied customer reviews on Amazon.com and identified the value of review extremity, review depth, and product type in helping customers make purchasing decisions @cite_23 . Poch 'e et al found that around 30 Similar to previous studies that leverage user feedback in improving existing software artifacts, our study of comments under Stack Overflow answers investigates how comments add value to their associated answers. Furthermore, we examine whether informative comments are effectively presented using the current comment hiding mechanism.
@cite_14 @cite_23 @cite_12
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App Stores, such as Google Play or the Apple Store, allow users to provide feedback on apps by posting review comments and giving star ratings. These platforms constitute a useful electronic mean in which application developers and users can productively exchange information about apps. Previous research showed that users feedback contains usage scenarios, bug reports and feature requests, that can help app developers to accomplish software maintenance and evolution tasks. However, in the case of the most popular apps, the large amount of received feedback, its unstructured nature and varying quality can make the identification of useful user feedback a very challenging task. In this paper we present a taxonomy to classify app reviews into categories relevant to software maintenance and evolution, as well as an approach that merges three techniques: (1) Natural Language Processing, (2) Text Analysis and (3) Sentiment Analysis to automatically classify app reviews into the proposed categories. We show that the combined use of these techniques allows to achieve better results (a precision of 75 and a recall of 74 ) than results obtained using each technique individually (precision of 70 and a recall of 67 ). Developers always focus on delivering high-quality updates to improve, or maintain the rating of their apps. Prior work has studied user reviews by analyzing all reviews of an app. However, this app-level analysis misses the point that users post reviews to provide their feedback on a certain update. For example, two bad updates of an app with a history of good updates would not be spotted using app-level analysis. In this paper, we examine reviews at the update-level to better understand how users perceive bad updates. We focus our study on the top 250 bad updates (i.e., updates with the highest increase in the percentage of negative reviews relative to the prior updates of the app) from 26,726 updates of 2,526 top free-to-download apps in the Google Play Store. We find that feature removal and UI issues have the highest increase in the percentage of negative reviews. Bad updates with crashes and functional issues are the most likely to be fixed by a later update. However, developers often do not mention these fixes in the release notes. Our work demonstrates the necessity of an update-level analysis of reviews to capture the feelings of an app's user-base about a particular update. Mobile-app quality is becoming an increasingly important issue. These apps are generally delivered through app stores that let users post reviews. These reviews provide a rich data source you can leverage to understand user-reported issues. Researchers qualitatively studied 6,390 low-rated user reviews for 20 free-to-download iOS apps. They uncovered 12 types of user complaints. The most frequent complaints were functional errors, feature requests, and app crashes. Complaints about privacy and ethical issues and hidden app costs most negatively affected ratings. In 11 percent of the reviews, users attributed their complaints to a recent app update. This study provides insight into the user-reported issues of iOS apps, along with their frequency and impact, which can help developers better prioritize their limited quality assurance resources. In the mobile-app ecosystem, user ratings of apps (a measure of user perception) are extremely important because they correlate strongly with downloads and hence revenue. A case study examined the relationship between ratings (and the associated review comments) and static-analysis warnings (collected using FindBugs) for 10,000 free-to-download Android apps. Three warning categories--bad practice, internationalization, and performance--were more frequent in low-rated apps and corresponded to the review comment complaints. Thus, these categories were closely related to the user experience. These results suggest that app developers could use static-analysis tools to identify the bugs behind the issues that users complain about, before releasing an app.