The popularity prediction of mobile apps provides substantial value to a broad range of applications, ranging from app development to targeted advertising. However, most previous studies do this work by establishing regression models for impact factors, or using clustering and classification algorithms. It does not fully investigate the process of popularity evolution and the reasons behind it. In this paper, we discuss and analyze the potential predictors, especially the impact of early evolutionary patterns on future popularity. To this end, we first explore six basic evolutionary patterns and six impact factors that are closely related to app popularity. After detailed analysis, we present CrowdPop, a popularity prediction model based on the Random Forest algorithm, to quantify patterns and factors as predictors of CrowdPop. The experiment results with a real-world dataset of 126 apps indicate that, compared with baseline methods, our CrowdPop performs better in mobile app popularity prediction.
{"title":"CrowdPop","authors":"Yixuan Zhang, Bin Guo, Ouyang Yi, Tong Guo, Zhu Wang, Zhiwen Yu","doi":"10.1145/3275219.3275235","DOIUrl":"https://doi.org/10.1145/3275219.3275235","url":null,"abstract":"The popularity prediction of mobile apps provides substantial value to a broad range of applications, ranging from app development to targeted advertising. However, most previous studies do this work by establishing regression models for impact factors, or using clustering and classification algorithms. It does not fully investigate the process of popularity evolution and the reasons behind it. In this paper, we discuss and analyze the potential predictors, especially the impact of early evolutionary patterns on future popularity. To this end, we first explore six basic evolutionary patterns and six impact factors that are closely related to app popularity. After detailed analysis, we present CrowdPop, a popularity prediction model based on the Random Forest algorithm, to quantify patterns and factors as predictors of CrowdPop. The experiment results with a real-world dataset of 126 apps indicate that, compared with baseline methods, our CrowdPop performs better in mobile app popularity prediction.","PeriodicalId":184857,"journal":{"name":"Proceedings of the Tenth Asia-Pacific Symposium on Internetware","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114726274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
It is a big challenge to pick up the best cloud configuration for recurring big data analytics jobs running in clouds. Prior efforts may get in a sub-optimal configuration due to a broad spectrum of cloud configurations with a few test runs, such as CherryPick. We present HW3C which is a heuristic based workload classification and cloud configuration system for big data analytics jobs, our insight is classifying a job by comparing its resource preference and usage informantion with other jobs, and then using heuristic rules to distinguish bad samples from good ones in Bayesian Optimization algorithm. Our experiments on HiBench and SparkBench in Aliyun ECS show that the performance of job had been improved by 53% in average comparing with CherryPick, meanwhile the resource cost had been reduced by 40% in average.
{"title":"HW3C","authors":"Yuewen Wu, Heng Wu, Wen-bo Zhang, Yuanjia Xu, Jun Wei, Hua Zhong","doi":"10.1145/3275219.3275224","DOIUrl":"https://doi.org/10.1145/3275219.3275224","url":null,"abstract":"It is a big challenge to pick up the best cloud configuration for recurring big data analytics jobs running in clouds. Prior efforts may get in a sub-optimal configuration due to a broad spectrum of cloud configurations with a few test runs, such as CherryPick. We present HW3C which is a heuristic based workload classification and cloud configuration system for big data analytics jobs, our insight is classifying a job by comparing its resource preference and usage informantion with other jobs, and then using heuristic rules to distinguish bad samples from good ones in Bayesian Optimization algorithm. Our experiments on HiBench and SparkBench in Aliyun ECS show that the performance of job had been improved by 53% in average comparing with CherryPick, meanwhile the resource cost had been reduced by 40% in average.","PeriodicalId":184857,"journal":{"name":"Proceedings of the Tenth Asia-Pacific Symposium on Internetware","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115612775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yu Wang, Fengjuan Gao, Lingyun Situ, Lingzhang Wang, Bihuan Chen, Yang Liu, Jianhua Zhao, Xuandong Li
Dangling pointers have become an important class of software bugs that can lead to use-after-free and double-free vulnerabilities. So far, only a few approaches have been proposed to protect against dangling pointers, while most of them suffer from high overhead. In this paper, we propose a lightweight approach, named DangDone, to eliminate dangling pointers at compile time. Built upon the root cause of a dangling pointer, i.e., a pointer and its aliases are not nullified but the memory area they point to is deallocated, DangDone realizes the protection by inserting an intermediate pointer between the pointers (i.e., a pointer and its aliases) and the memory area they point to. Hence, nullifying the intermediate pointer will nullify the pointer and its aliases, which mitigates the vulnerabilities caused by dangling pointers. Experimental results have demonstrated that DangDone can protect target programs (i.e., the SPEC CPU benchmarks and the programs with known CVEs) with negligible runtime overhead (i.e., around 1% on average).
{"title":"DangDone","authors":"Yu Wang, Fengjuan Gao, Lingyun Situ, Lingzhang Wang, Bihuan Chen, Yang Liu, Jianhua Zhao, Xuandong Li","doi":"10.1145/3275219.3275231","DOIUrl":"https://doi.org/10.1145/3275219.3275231","url":null,"abstract":"Dangling pointers have become an important class of software bugs that can lead to use-after-free and double-free vulnerabilities. So far, only a few approaches have been proposed to protect against dangling pointers, while most of them suffer from high overhead. In this paper, we propose a lightweight approach, named DangDone, to eliminate dangling pointers at compile time. Built upon the root cause of a dangling pointer, i.e., a pointer and its aliases are not nullified but the memory area they point to is deallocated, DangDone realizes the protection by inserting an intermediate pointer between the pointers (i.e., a pointer and its aliases) and the memory area they point to. Hence, nullifying the intermediate pointer will nullify the pointer and its aliases, which mitigates the vulnerabilities caused by dangling pointers. Experimental results have demonstrated that DangDone can protect target programs (i.e., the SPEC CPU benchmarks and the programs with known CVEs) with negligible runtime overhead (i.e., around 1% on average).","PeriodicalId":184857,"journal":{"name":"Proceedings of the Tenth Asia-Pacific Symposium on Internetware","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127859884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lingyun Situ, Linzhang Wang, Yang Liu, Bing Mao, Xuandong Li
{"title":"Vanguard","authors":"Lingyun Situ, Linzhang Wang, Yang Liu, Bing Mao, Xuandong Li","doi":"10.2307/j.ctv6q52nd.12","DOIUrl":"https://doi.org/10.2307/j.ctv6q52nd.12","url":null,"abstract":"","PeriodicalId":184857,"journal":{"name":"Proceedings of the Tenth Asia-Pacific Symposium on Internetware","volume":"326 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114453850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents an effective model-based GUI testing technique for Android apps. To avoid local and repetitive exploration, our approach groups equivalent widgets in a state and designs a novel feedback-based exploration strategy, which dynamically adjusts the priority of actions based on the execution result of those already triggered ones, and tends to select actions that can reach news states of apps. We implemented our technique in a tool, called CrawlDroid, and conducted empirical experiments. Our results show that the proposed technique is effective, and covers more code within a fixed testing budget.
{"title":"CrawlDroid","authors":"Yuzhong Cao, Guoquan Wu, Wei Chen, Jun Wei","doi":"10.1145/3275219.3275238","DOIUrl":"https://doi.org/10.1145/3275219.3275238","url":null,"abstract":"This paper presents an effective model-based GUI testing technique for Android apps. To avoid local and repetitive exploration, our approach groups equivalent widgets in a state and designs a novel feedback-based exploration strategy, which dynamically adjusts the priority of actions based on the execution result of those already triggered ones, and tends to select actions that can reach news states of apps. We implemented our technique in a tool, called CrawlDroid, and conducted empirical experiments. Our results show that the proposed technique is effective, and covers more code within a fixed testing budget.","PeriodicalId":184857,"journal":{"name":"Proceedings of the Tenth Asia-Pacific Symposium on Internetware","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117352315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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