2016 IEEE/ACM 13th Working Conference on Mining Software Repositories (MSR)最新文献_第10页

Mining energy-greedy API usage patterns in Android apps: an empirical study 在Android应用中挖掘能源贪婪的API使用模式:一项实证研究

2016 IEEE/ACM 13th Working Conference on Mining Software Repositories (MSR) Pub Date : 2014-05-31 DOI: 10.1145/2597073.2597085

M. Vásquez, G. Bavota, Carlos Bernal-Cárdenas, R. Oliveto, M. D. Penta, D. Poshyvanyk

{"title":"Mining energy-greedy API usage patterns in Android apps: an empirical study","authors":"M. Vásquez, G. Bavota, Carlos Bernal-Cárdenas, R. Oliveto, M. D. Penta, D. Poshyvanyk","doi":"10.1145/2597073.2597085","DOIUrl":"https://doi.org/10.1145/2597073.2597085","url":null,"abstract":"Energy consumption of mobile applications is nowadays a hot topic, given the widespread use of mobile devices. The high demand for features and improved user experience, given the available powerful hardware, tend to increase the apps’ energy consumption. However, excessive energy consumption in mobile apps could also be a consequence of energy greedy hardware, bad programming practices, or particular API usage patterns. We present the largest to date quantitative and qualitative empirical investigation into the categories of API calls and usage patterns that—in the context of the Android development framework—exhibit particularly high energy consumption profiles. By using a hardware power monitor, we measure energy consumption of method calls when executing typical usage scenarios in 55 mobile apps from different domains. Based on the collected data, we mine and analyze energy-greedy APIs and usage patterns. We zoom in and discuss the cases where either the anomalous energy consumption is unavoidable or where it is due to suboptimal usage or choice of APIs. Finally, we synthesize our findings into actionable knowledge and recipes for developers on how to reduce energy consumption while using certain categories of Android APIs and patterns","PeriodicalId":6621,"journal":{"name":"2016 IEEE/ACM 13th Working Conference on Mining Software Repositories (MSR)","volume":"48 10 1","pages":"2-11"},"PeriodicalIF":0.0,"publicationDate":"2014-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77307654","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}

引用次数: 227

Improving the accuracy of duplicate bug report detection using textual similarity measures 使用文本相似性度量提高重复错误报告检测的准确性

2016 IEEE/ACM 13th Working Conference on Mining Software Repositories (MSR) Pub Date : 2014-05-31 DOI: 10.1145/2597073.2597088

A. Lazar, Sarah Ritchey, Bonita Sharif

引用次数: 71

The bug catalog of the maven ecosystem maven生态系统的bug目录

2016 IEEE/ACM 13th Working Conference on Mining Software Repositories (MSR) Pub Date : 2014-05-31 DOI: 10.1145/2597073.2597123

Dimitris Mitropoulos, Vassilios Karakoidas, P. Louridas, Georgios Gousios, D. Spinellis

引用次数: 32

Modern code reviews in open-source projects: which problems do they fix? 开源项目中的现代代码审查:它们修复了哪些问题?

2016 IEEE/ACM 13th Working Conference on Mining Software Repositories (MSR) Pub Date : 2014-05-31 DOI: 10.1145/2597073.2597082

M. Beller, Alberto Bacchelli, A. Zaidman, E. Juergens

{"title":"Modern code reviews in open-source projects: which problems do they fix?","authors":"M. Beller, Alberto Bacchelli, A. Zaidman, E. Juergens","doi":"10.1145/2597073.2597082","DOIUrl":"https://doi.org/10.1145/2597073.2597082","url":null,"abstract":"Code review is the manual assessment of source code by humans, mainly intended to identify defects and quality problems. Modern Code Review (MCR), a lightweight variant of the code inspections investigated since the 1970s, prevails today both in industry and open-source software (OSS) systems. The objective of this paper is to increase our understanding of the practical benefits that the MCR process produces on reviewed source code. To that end, we empirically explore the problems fixed through MCR in OSS systems. We manually classified over 1,400 changes taking place in reviewed code from two OSS projects into a validated categorization scheme. Surprisingly, results show that the types of changes due to the MCR process in OSS are strikingly similar to those in the industry and academic systems from literature, featuring the similar 75:25 ratio of maintainability-related to functional problems. We also reveal that 7–35% of review comments are discarded and that 10–22% of the changes are not triggered by an explicit review comment. Patterns emerged in the review data; we investigated them revealing the technical factors that influence the number of changes due to the MCR process. We found that bug-fixing tasks lead to fewer changes and tasks with more altered files and a higher code churn have more changes. Contrary to intuition, the person of the reviewer had no impact on the number of changes.","PeriodicalId":6621,"journal":{"name":"2016 IEEE/ACM 13th Working Conference on Mining Software Repositories (MSR)","volume":"1 1","pages":"202-211"},"PeriodicalIF":0.0,"publicationDate":"2014-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76218463","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}

引用次数: 203

A dictionary to translate change tasks to source code 将变更任务转换为源代码的字典

2016 IEEE/ACM 13th Working Conference on Mining Software Repositories (MSR) Pub Date : 2014-05-31 DOI: 10.1145/2597073.2597095

Katja Kevic, Thomas Fritz

引用次数: 17

Kataribe: a hosting service of historage repositories Kataribe:历史存储库的托管服务

2016 IEEE/ACM 13th Working Conference on Mining Software Repositories (MSR) Pub Date : 2014-05-31 DOI: 10.1145/2597073.2597125

Kenji Fujiwara, Hideaki Hata, Erina Makihara, Yusuke Fujihara, Naoki Nakayama, Hajimu Iida, Ken-ichi Matsumoto

引用次数: 18

Magnet or sticky? an OSS project-by-project typology 磁铁还是粘性?OSS项目对项目的类型学

2016 IEEE/ACM 13th Working Conference on Mining Software Repositories (MSR) Pub Date : 2014-05-31 DOI: 10.1145/2597073.2597116

Kazuhiro Yamashita, Shane McIntosh, Yasutaka Kamei, Naoyasu Ubayashi

引用次数: 30

Finding patterns in static analysis alerts: improving actionable alert ranking 在静态分析警报中发现模式:改进可操作警报排名

2016 IEEE/ACM 13th Working Conference on Mining Software Repositories (MSR) Pub Date : 2014-05-31 DOI: 10.1145/2597073.2597100

Quinn Hanam, Lin Tan, Reid Holmes, Patrick Lam

{"title":"Finding patterns in static analysis alerts: improving actionable alert ranking","authors":"Quinn Hanam, Lin Tan, Reid Holmes, Patrick Lam","doi":"10.1145/2597073.2597100","DOIUrl":"https://doi.org/10.1145/2597073.2597100","url":null,"abstract":"Static analysis (SA) tools that find bugs by inferring programmer beliefs (e.g., FindBugs) are commonplace in today's software industry. While they find a large number of actual defects, they are often plagued by high rates of alerts that a developer would not act on (unactionable alerts) because they are incorrect, do not significantly affect program execution, etc. High rates of unactionable alerts decrease the utility of static analysis tools in practice. \u0000 We present a method for differentiating actionable and unactionable alerts by finding alerts with similar code patterns. To do so, we create a feature vector based on code characteristics at the site of each SA alert. With these feature vectors, we use machine learning techniques to build an actionable alert prediction model that is able to classify new SA alerts. \u0000 We evaluate our technique on three subject programs using the FindBugs static analysis tool and the Faultbench benchmark methodology. For a developer inspecting the top 5% of all alerts for three sample projects, our approach is able to identify 57 of 211 actionable alerts, which is 38 more than the FindBugs priority measure. Combined with previous actionable alert identification techniques, our method finds 75 actionable alerts in the top 5%, which is four more actionable alerts (a 6% improvement) than previous actionable alert identification techniques.","PeriodicalId":6621,"journal":{"name":"2016 IEEE/ACM 13th Working Conference on Mining Software Repositories (MSR)","volume":"1 1","pages":"152-161"},"PeriodicalIF":0.0,"publicationDate":"2014-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89955436","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}

引用次数: 68

GreenMiner: a hardware based mining software repositories software energy consumption framework GreenMiner:一个基于硬件的挖矿软件库软件能耗框架

2016 IEEE/ACM 13th Working Conference on Mining Software Repositories (MSR) Pub Date : 2014-05-31 DOI: 10.1145/2597073.2597097

Abram Hindle, Alex Wilson, Kent Rasmussen, E. J. Barlow, Hazel Victoria Campbell, Stephen Romansky

{"title":"GreenMiner: a hardware based mining software repositories software energy consumption framework","authors":"Abram Hindle, Alex Wilson, Kent Rasmussen, E. J. Barlow, Hazel Victoria Campbell, Stephen Romansky","doi":"10.1145/2597073.2597097","DOIUrl":"https://doi.org/10.1145/2597073.2597097","url":null,"abstract":"Green Mining is a field of MSR that studies software energy consumption and relies on software performance data. Unfortunately there is a severe lack of publicly available software power use performance data. This means that green mining researchers must generate this data themselves by writing tests, building multiple revisions of a product, and then running these tests multiple times (10+) for each software revision while measuring power use. Then, they must aggregate these measurements to estimate the energy consumed by the tests for each software revision. This is time consuming and is made more difficult by the constraints of mobile devices and their OSes. In this paper we propose, implement, and demonstrate Green Miner: the first dedicated hardware mining software repositories testbed. The Green Miner physically measures the energy consumption of mobile devices (Android phones) and automates the testing of applications, and the reporting of measurements back to developers and researchers. The Green Miner has already produced valuable results for commercial Android application developers, and has been shown to replicate other power studies' results.","PeriodicalId":6621,"journal":{"name":"2016 IEEE/ACM 13th Working Conference on Mining Software Repositories (MSR)","volume":"66 1","pages":"12-21"},"PeriodicalIF":0.0,"publicationDate":"2014-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90291627","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}

引用次数: 149

New features for duplicate bug detection 重复错误检测的新特性

2016 IEEE/ACM 13th Working Conference on Mining Software Repositories (MSR) Pub Date : 2014-05-31 DOI: 10.1145/2597073.2597090

Nathan Klein, Christopher S. Corley, Nicholas A. Kraft

{"title":"New features for duplicate bug detection","authors":"Nathan Klein, Christopher S. Corley, Nicholas A. Kraft","doi":"10.1145/2597073.2597090","DOIUrl":"https://doi.org/10.1145/2597073.2597090","url":null,"abstract":"Issue tracking software of large software projects receive a large volume of issue reports each day. Each of these issues is typically triaged by hand, a time consuming and error prone task. Additionally, issue reporters lack the necessary understanding to know whether their issue has previously been reported. This leads to issue trackers containing a lot of duplicate reports, adding complexity to the triaging task. \u0000 Duplicate bug report detection is designed to aid developers by automatically grouping bug reports concerning identical issues. Previous work by Alipour et al. has shown that the textual, categorical, and contextual information of an issue report are effective measures in duplicate bug report detection. In our work, we extend previous work by introducing a range of metrics based on the topic distribution of the issue reports, relying only on data taken directly from bug reports. In particular, we introduce a novel metric that measures the first shared topic between two topic-document distributions. This paper details the evaluation of this group of pair-based metrics with a range of machine learning classifiers, using the same issues used by Alipour et al. We demonstrate that the proposed metrics show a significant improvement over previous work, and conclude that the simple metrics we propose should be considered in future studies on bug report deduplication, as well as for more general natural language processing applications.","PeriodicalId":6621,"journal":{"name":"2016 IEEE/ACM 13th Working Conference on Mining Software Repositories (MSR)","volume":"13 1","pages":"324-327"},"PeriodicalIF":0.0,"publicationDate":"2014-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87438778","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}

引用次数: 28