M. Vásquez, G. Bavota, Carlos Bernal-Cárdenas, R. Oliveto, M. D. Penta, D. Poshyvanyk
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
{"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}
The paper describes an improved method for automatic duplicate bug report detection based on new textual similarity features and binary classification. Using a set of new textual features, inspired from recent text similarity research, we train several binary classification models. A case study was conducted on three open source systems: Eclipse, Open Office, and Mozilla to determine the effectiveness of the improved method. A comparison is also made with current state-of-the-art approaches highlighting similarities and differences. Results indicate that the accuracy of the proposed method is better than previously reported research with respect to all three systems.
{"title":"Improving the accuracy of duplicate bug report detection using textual similarity measures","authors":"A. Lazar, Sarah Ritchey, Bonita Sharif","doi":"10.1145/2597073.2597088","DOIUrl":"https://doi.org/10.1145/2597073.2597088","url":null,"abstract":"The paper describes an improved method for automatic duplicate bug report detection based on new textual similarity features and binary classification. Using a set of new textual features, inspired from recent text similarity research, we train several binary classification models. A case study was conducted on three open source systems: Eclipse, Open Office, and Mozilla to determine the effectiveness of the improved method. A comparison is also made with current state-of-the-art approaches highlighting similarities and differences. Results indicate that the accuracy of the proposed method is better than previously reported research with respect to all three systems.","PeriodicalId":6621,"journal":{"name":"2016 IEEE/ACM 13th Working Conference on Mining Software Repositories (MSR)","volume":"16 1 1","pages":"308-311"},"PeriodicalIF":0.0,"publicationDate":"2014-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82843201","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}
Dimitris Mitropoulos, Vassilios Karakoidas, P. Louridas, Georgios Gousios, D. Spinellis
Examining software ecosystems can provide the research community with data regarding artifacts, processes, and communities. We present a dataset obtained from the Maven central repository ecosystem (approximately 265GB of data) by statically analyzing the repository to detect potential software bugs. For our analysis we used FindBugs, a tool that examines Java bytecode to detect numerous types of bugs. The dataset contains the metrics results that FindBugs reports for every project version (a JAR) included in the ecosystem. For every version we also stored specific metadata such as the JAR's size, its dependencies and others. Our dataset can be used to produce interesting research results, as we show in specific examples.
{"title":"The bug catalog of the maven ecosystem","authors":"Dimitris Mitropoulos, Vassilios Karakoidas, P. Louridas, Georgios Gousios, D. Spinellis","doi":"10.1145/2597073.2597123","DOIUrl":"https://doi.org/10.1145/2597073.2597123","url":null,"abstract":"Examining software ecosystems can provide the research community with data regarding artifacts, processes, and communities. We present a dataset obtained from the Maven central repository ecosystem (approximately 265GB of data) by statically analyzing the repository to detect potential software bugs. For our analysis we used FindBugs, a tool that examines Java bytecode to detect numerous types of bugs. The dataset contains the metrics results that FindBugs reports for every project version (a JAR) included in the ecosystem. For every version we also stored specific metadata such as the JAR's size, its dependencies and others. Our dataset can be used to produce interesting research results, as we show in specific examples.","PeriodicalId":6621,"journal":{"name":"2016 IEEE/ACM 13th Working Conference on Mining Software Repositories (MSR)","volume":"21 1","pages":"372-375"},"PeriodicalIF":0.0,"publicationDate":"2014-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83673972","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}
M. Beller, Alberto Bacchelli, A. Zaidman, E. Juergens
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.
{"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}
At the beginning of a change task, software developers spend a substantial amount of their time searching and navigating to locate relevant parts in the source code. Current approaches to support developers in this initial code search predominantly use information retrieval techniques that leverage the similarity between task descriptions and the identifiers of code elements to recommend relevant elements. However, the vocabulary or language used in source code often differs from the one used for describing change tasks, especially since the people developing the code are not the same as the ones reporting bugs or defining new features to be implemented. In our work, we investigate the creation of a dictionary that maps the different vocabularies using information from change sets and interaction histories stored with previously completed tasks. In an empirical analysis on four open source projects, our approach substantially improved upon the results of traditional information retrieval techniques for recommending relevant code elements.
{"title":"A dictionary to translate change tasks to source code","authors":"Katja Kevic, Thomas Fritz","doi":"10.1145/2597073.2597095","DOIUrl":"https://doi.org/10.1145/2597073.2597095","url":null,"abstract":"At the beginning of a change task, software developers spend a substantial amount of their time searching and navigating to locate relevant parts in the source code. Current approaches to support developers in this initial code search predominantly use information retrieval techniques that leverage the similarity between task descriptions and the identifiers of code elements to recommend relevant elements. However, the vocabulary or language used in source code often differs from the one used for describing change tasks, especially since the people developing the code are not the same as the ones reporting bugs or defining new features to be implemented. In our work, we investigate the creation of a dictionary that maps the different vocabularies using information from change sets and interaction histories stored with previously completed tasks. In an empirical analysis on four open source projects, our approach substantially improved upon the results of traditional information retrieval techniques for recommending relevant code elements.","PeriodicalId":6621,"journal":{"name":"2016 IEEE/ACM 13th Working Conference on Mining Software Repositories (MSR)","volume":"28 1","pages":"320-323"},"PeriodicalIF":0.0,"publicationDate":"2014-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80044967","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}
In the research of Mining Software Repositories, code repository is one of the core source since it contains the product of software development. Code repository stores the versions of files, and makes it possible to browse the histories of files, such as modification dates, authors, messages, etc. Although such rich information of file histories is easily available, extracting the histories of methods, which are elements of source code files, is not easy from general code repositories. To tackle this difficulty, we have developed Historage, a fine-grained version control system. Historage repository is a Git repository which is built upon original Git repository. Therefore, similar mining techniques for general Git repositories are applicable to Historage repositories. Kataribe is a hosting service of Historage repositories, which enables researchers and developers to browse method histories on the web and clone Historage repositories to local. The Kataribe project aims to maintain and expand the datasets and features.
{"title":"Kataribe: a hosting service of historage repositories","authors":"Kenji Fujiwara, Hideaki Hata, Erina Makihara, Yusuke Fujihara, Naoki Nakayama, Hajimu Iida, Ken-ichi Matsumoto","doi":"10.1145/2597073.2597125","DOIUrl":"https://doi.org/10.1145/2597073.2597125","url":null,"abstract":"In the research of Mining Software Repositories, code repository is one of the core source since it contains the product of software development. Code repository stores the versions of files, and makes it possible to browse the histories of files, such as modification dates, authors, messages, etc. Although such rich information of file histories is easily available, extracting the histories of methods, which are elements of source code files, is not easy from general code repositories. To tackle this difficulty, we have developed Historage, a fine-grained version control system. Historage repository is a Git repository which is built upon original Git repository. Therefore, similar mining techniques for general Git repositories are applicable to Historage repositories. Kataribe is a hosting service of Historage repositories, which enables researchers and developers to browse method histories on the web and clone Historage repositories to local. The Kataribe project aims to maintain and expand the datasets and features.","PeriodicalId":6621,"journal":{"name":"2016 IEEE/ACM 13th Working Conference on Mining Software Repositories (MSR)","volume":"10 1","pages":"380-383"},"PeriodicalIF":0.0,"publicationDate":"2014-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84217050","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}
For Open Source Software (OSS) projects, retaining existing contributors and attracting new ones is a major concern. In this paper, we expand and adapt a pair of population migration metrics to analyze migration trends in a collection of open source projects. Namely, we study: (1) project stickiness, i.e., its tendency to retain existing contributors and (2) project magnetism, i.e., its tendency to attract new contributors. Using quadrant plots, we classify projects as attractive (highly magnetic and sticky), stagnant (highly sticky, weakly magnetic), fluctuating (highly magnetic, weakly sticky), or terminal (weakly magnetic and sticky). Through analysis of the MSR challenge dataset, we find that: (1) quadrant plots can effectively identify at-risk projects, (2) stickiness is often motivated by professional activity and (3) transitions among quadrants as a project ages often coincides with interesting events in the evolution history of a project.
{"title":"Magnet or sticky? an OSS project-by-project typology","authors":"Kazuhiro Yamashita, Shane McIntosh, Yasutaka Kamei, Naoyasu Ubayashi","doi":"10.1145/2597073.2597116","DOIUrl":"https://doi.org/10.1145/2597073.2597116","url":null,"abstract":"For Open Source Software (OSS) projects, retaining existing contributors and attracting new ones is a major concern. In this paper, we expand and adapt a pair of population migration metrics to analyze migration trends in a collection of open source projects. Namely, we study: (1) project stickiness, i.e., its tendency to retain existing contributors and (2) project magnetism, i.e., its tendency to attract new contributors. Using quadrant plots, we classify projects as attractive (highly magnetic and sticky), stagnant (highly sticky, weakly magnetic), fluctuating (highly magnetic, weakly sticky), or terminal (weakly magnetic and sticky). Through analysis of the MSR challenge dataset, we find that: (1) quadrant plots can effectively identify at-risk projects, (2) stickiness is often motivated by professional activity and (3) transitions among quadrants as a project ages often coincides with interesting events in the evolution history of a project.","PeriodicalId":6621,"journal":{"name":"2016 IEEE/ACM 13th Working Conference on Mining Software Repositories (MSR)","volume":"5 1","pages":"344-347"},"PeriodicalIF":0.0,"publicationDate":"2014-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88827157","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}
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. 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. 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.
{"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}
Abram Hindle, Alex Wilson, Kent Rasmussen, E. J. Barlow, Hazel Victoria Campbell, Stephen Romansky
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.
{"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}
Nathan Klein, Christopher S. Corley, Nicholas A. Kraft
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. 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.
{"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}
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