Context: In-app advertising is the primary source of revenue for many mobile apps. The cost of advertising (ad cost) is non-negligible for app developers to ensure a good user experience and continuous profits. Previous studies mainly focus on addressing the hidden performance costs generated by ads, including consumption of memory, CPU, data traffic, and battery. However, there is no research on analyzing users’ perceptions of ads’ performance costs to our knowledge.

Objective: To fill this gap and better understand the effects of performance costs of in-app ads on user experience, we conduct a study on analyzing user concerns about ads’ performance costs.

Method: First, we propose RankMiner, an approach to quantify user concerns about specific app issues, including performance costs. Then, based on the usage traces of 20 subject apps, we measure the performance costs of ads. Finally, we conduct correlation analysis on the performance costs and quantified user concerns to explore whether users complain more for higher performance costs.

Results: Our findings include the following: (1) RankMiner can quantify users’ concerns better than baselines by an improvement of 214% and 2.5% in terms of Pearson correlation coefficient (a metric for computing correlations between two variables) and NDCG score (a metric for computing accuracy in prioritizing issues), respectively. (2) The performance costs of the with-ads versions are statistically significantly larger than those of no-ads versions with negligible effect size; (3) Users are more concerned about the battery costs of ads, and tend to be insensitive to ads’ data traffic costs.

Conclusion: Our study is complementary to previous work on in-app ads, and can encourage developers to pay more attention to alleviating the most user-concerned performance costs, such as battery cost.

上下文：应用内广告是许多移动应用的主要收入来源。广告费用（广告费用）对于应用开发者而言不可忽略，以确保良好的用户体验和持续的利润。先前的研究主要集中在解决广告所产生的隐藏性能成本，包括内存消耗，CPU，数据流量和电池消耗。但是，就我们所知，尚无研究分析用户对广告性能成本的看法。

目标：为填补这一空白，并更好地了解应用内广告的性能成本对用户体验的影响，我们进行了一项分析用户对广告性能成本的关注的研究。

方法：首先，我们提出RankMiner，一种量化用户对特定应用问题（包括性能成本）的关注的方法。然后，根据20个主题应用的使用情况跟踪，我们可以衡量广告的性能成本。最后，我们对性能成本和量化的用户顾虑进行相关分析，以探索用户是否抱怨性能成本更高。

结果：我们的发现包括以下内容：（1）RankMiner可以比Pearson相关系数（一种用于计算两个变量之间的相关性的度量）和NDCG分数（a）提高214％和2.5％，从而比基线更好地量化了用户的关注。计算优先级的准确性）。（2）统计上，有广告版本的性能成本明显高于无广告版本，效果大小可忽略不计；（3）用户更加关注广告的电池成本，并且往往对广告的数据流量成本不敏感。

结论：我们的研究是对先前在应用内广告方面的工作的补充，可以鼓励开发人员更加关注减轻用户最关注的性能成本，例如电池成本。