Pub Date : 2009-05-16DOI: 10.1109/MSR.2009.5069492
Lile Hattori, Michele Lanza
When software repositories are mined, two distinct sources of information are usually explored: the history log and snapshots of the system. Results of analyses derived from these two sources are biased by the frequency with which developers commit their changes. We argue that the usage of mainstream SCM systems influences the way that developers work. For example, since it is tedious to resolve conflicts due to parallel commits, developers tend to minimize conflicts by not contemporarily modifying the same file. This however defeats one of the purposes of such systems. We mine repositories created by our Syde tool, which records every change by every developer in multi-developer projects. This new source of information can augment the accuracy of analyses and breaks new ground in terms of how such information can assist developers. In this paper we illustrate how the information we mine can help to provide a refined notion of code ownership. As a case study, we analyze the developers' activities of the development of a commercial system.
{"title":"Mining the history of synchronous changes to refine code ownership","authors":"Lile Hattori, Michele Lanza","doi":"10.1109/MSR.2009.5069492","DOIUrl":"https://doi.org/10.1109/MSR.2009.5069492","url":null,"abstract":"When software repositories are mined, two distinct sources of information are usually explored: the history log and snapshots of the system. Results of analyses derived from these two sources are biased by the frequency with which developers commit their changes. We argue that the usage of mainstream SCM systems influences the way that developers work. For example, since it is tedious to resolve conflicts due to parallel commits, developers tend to minimize conflicts by not contemporarily modifying the same file. This however defeats one of the purposes of such systems. We mine repositories created by our Syde tool, which records every change by every developer in multi-developer projects. This new source of information can augment the accuracy of analyses and breaks new ground in terms of how such information can assist developers. In this paper we illustrate how the information we mine can help to provide a refined notion of code ownership. As a case study, we analyze the developers' activities of the development of a commercial system.","PeriodicalId":413721,"journal":{"name":"2009 6th IEEE International Working Conference on Mining Software Repositories","volume":"193 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115268177","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}
Pub Date : 2009-05-16DOI: 10.1109/MSR.2009.5069483
D. Germán, M. D. Penta, Yann-Gaël Guéhéneuc, G. Antoniol
Source code cloning does not happen within a single system only. It can also occur between one system and another. We use the term code sibling to refer to a code clone that evolves in a different system than the code from which it originates. Code siblings can only occur when the source code copyright owner allows it and when the conditions imposed by such license are not incompatible with the license of the destination system. In some situations copying of source code fragments are allowed—legally—in one direction, but not in the other. In this paper, we use clone detection, license mining and classification, and change history techniques to understand how code siblings—under different licenses—flow in one direction or the other between Linux and two BSD Unixes, FreeBSD and OpenBSD. Our results show that, in most cases, this migration appears to happen according to the terms of the license of the original code being copied, favoring always copying from less restrictive licenses towards more restrictive ones. We also discovered that sometimes code is inserted to the kernels from an outside source.
{"title":"Code siblings: Technical and legal implications of copying code between applications","authors":"D. Germán, M. D. Penta, Yann-Gaël Guéhéneuc, G. Antoniol","doi":"10.1109/MSR.2009.5069483","DOIUrl":"https://doi.org/10.1109/MSR.2009.5069483","url":null,"abstract":"Source code cloning does not happen within a single system only. It can also occur between one system and another. We use the term code sibling to refer to a code clone that evolves in a different system than the code from which it originates. Code siblings can only occur when the source code copyright owner allows it and when the conditions imposed by such license are not incompatible with the license of the destination system. In some situations copying of source code fragments are allowed—legally—in one direction, but not in the other. In this paper, we use clone detection, license mining and classification, and change history techniques to understand how code siblings—under different licenses—flow in one direction or the other between Linux and two BSD Unixes, FreeBSD and OpenBSD. Our results show that, in most cases, this migration appears to happen according to the terms of the license of the original code being copied, favoring always copying from less restrictive licenses towards more restrictive ones. We also discovered that sometimes code is inserted to the kernels from an outside source.","PeriodicalId":413721,"journal":{"name":"2009 6th IEEE International Working Conference on Mining Software Repositories","volume":"150 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123394546","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}
Pub Date : 2009-05-16DOI: 10.1109/MSR.2009.5069490
W. Maalej, Hans-Jörg Happel
Developers take notes about their work sessions, either to remember the work status and share it with collaborators, or because employers explicitly require this for project management matters. We report on an exploratory study which aims at understanding how software developers describe their work. We analyzed more than 750,000 work descriptions of about 2,000 professionals taken over 8 years in three settings. We observed several similarities in the content and time meta-data of work descriptions. Most frequent terms, such as top-30 performed activities, are used consistently. Particular templates such as “ACTION concerning ARTIFACT because of CAUSE” occur frequently. Developers described sessions that last 30–120 min. 4–16 times a day. Maintaining diaries seems to consume between 3–6% of the total work time, and in 10% of the sessions, developers did not describe their work in sufficient detail. We argue that our results make the first step towards automatically generating work diaries for software developers.
{"title":"From work to word: How do software developers describe their work?","authors":"W. Maalej, Hans-Jörg Happel","doi":"10.1109/MSR.2009.5069490","DOIUrl":"https://doi.org/10.1109/MSR.2009.5069490","url":null,"abstract":"Developers take notes about their work sessions, either to remember the work status and share it with collaborators, or because employers explicitly require this for project management matters. We report on an exploratory study which aims at understanding how software developers describe their work. We analyzed more than 750,000 work descriptions of about 2,000 professionals taken over 8 years in three settings. We observed several similarities in the content and time meta-data of work descriptions. Most frequent terms, such as top-30 performed activities, are used consistently. Particular templates such as “ACTION concerning ARTIFACT because of CAUSE” occur frequently. Developers described sessions that last 30–120 min. 4–16 times a day. Maintaining diaries seems to consume between 3–6% of the total work time, and in 10% of the sessions, developers did not describe their work in sufficient detail. We argue that our results make the first step towards automatically generating work diaries for software developers.","PeriodicalId":413721,"journal":{"name":"2009 6th IEEE International Working Conference on Mining Software Repositories","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125552171","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}
Pub Date : 2009-05-16DOI: 10.1109/MSR.2009.5069480
J. Ekanayake, Jonas Tappolet, H. Gall, A. Bernstein
Defect prediction is an important task in the mining of software repositories, but the quality of predictions varies strongly within and across software projects. In this paper we investigate the reasons why the prediction quality is so fluctuating due to the altering nature of the bug (or defect) fixing process. Therefore, we adopt the notion of a concept drift, which denotes that the defect prediction model has become unsuitable as set of influencing features has changed - usually due to a change in the underlying bug generation process (i.e., the concept). We explore four open source projects (Eclipse, OpenOffice, Netbeans and Mozilla) and construct file-level and project-level features for each of them from their respective CVS and Bugzilla repositories. We then use this data to build defect prediction models and visualize the prediction quality along the time axis. These visualizations allow us to identify concept drifts and - as a consequence - phases of stability and instability expressed in the level of defect prediction quality. Further, we identify those project features, which are influencing the defect prediction quality using both a tree induction-algorithm and a linear regression model. Our experiments uncover that software systems are subject to considerable concept drifts in their evolution history. Specifically, we observe that the change in number of authors editing a file and the number of defects fixed by them contribute to a project's concept drift and therefore influence the defect prediction quality. Our findings suggest that project managers using defect prediction models for decision making should be aware of the actual phase of stability or instability due to a potential concept drift.
{"title":"Tracking concept drift of software projects using defect prediction quality","authors":"J. Ekanayake, Jonas Tappolet, H. Gall, A. Bernstein","doi":"10.1109/MSR.2009.5069480","DOIUrl":"https://doi.org/10.1109/MSR.2009.5069480","url":null,"abstract":"Defect prediction is an important task in the mining of software repositories, but the quality of predictions varies strongly within and across software projects. In this paper we investigate the reasons why the prediction quality is so fluctuating due to the altering nature of the bug (or defect) fixing process. Therefore, we adopt the notion of a concept drift, which denotes that the defect prediction model has become unsuitable as set of influencing features has changed - usually due to a change in the underlying bug generation process (i.e., the concept). We explore four open source projects (Eclipse, OpenOffice, Netbeans and Mozilla) and construct file-level and project-level features for each of them from their respective CVS and Bugzilla repositories. We then use this data to build defect prediction models and visualize the prediction quality along the time axis. These visualizations allow us to identify concept drifts and - as a consequence - phases of stability and instability expressed in the level of defect prediction quality. Further, we identify those project features, which are influencing the defect prediction quality using both a tree induction-algorithm and a linear regression model. Our experiments uncover that software systems are subject to considerable concept drifts in their evolution history. Specifically, we observe that the change in number of authors editing a file and the number of defects fixed by them contribute to a project's concept drift and therefore influence the defect prediction quality. Our findings suggest that project managers using defect prediction models for decision making should be aware of the actual phase of stability or instability due to a potential concept drift.","PeriodicalId":413721,"journal":{"name":"2009 6th IEEE International Working Conference on Mining Software Repositories","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114314067","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}
Pub Date : 2009-05-16DOI: 10.1109/MSR.2009.5069497
G. Robles, Jesus M. Gonzalez-Barahona, I. Herraiz
In many libre (free, open source) software projects, most of the development is performed by a relatively small number of persons, the “core team”. The stability and permanence of this group of most active developers is of great importance for the evolution and sustainability of the project. In this position paper we propose a quantitative methodology to study the evolution of core teams by analyzing information from source code management repositories. The most active developers in different periods are identified, and their activity is calculated over time, looking for core team evolution patterns.
在许多自由(free, open source)软件项目中，大部分的开发都是由相对较少的人完成的，即“核心团队”。这群最活跃的开发人员的稳定性和持久性对项目的发展和可持续性至关重要。在本文中，我们提出了一种定量方法，通过分析来自源代码管理存储库的信息来研究核心团队的演变。确定不同时期最活跃的开发人员，并随着时间的推移计算他们的活动，寻找核心团队的演变模式。
{"title":"Evolution of the core team of developers in libre software projects","authors":"G. Robles, Jesus M. Gonzalez-Barahona, I. Herraiz","doi":"10.1109/MSR.2009.5069497","DOIUrl":"https://doi.org/10.1109/MSR.2009.5069497","url":null,"abstract":"In many libre (free, open source) software projects, most of the development is performed by a relatively small number of persons, the “core team”. The stability and permanence of this group of most active developers is of great importance for the evolution and sustainability of the project. In this position paper we propose a quantitative methodology to study the evolution of core teams by analyzing information from source code management repositories. The most active developers in different periods are identified, and their activity is calculated over time, looking for core team evolution patterns.","PeriodicalId":413721,"journal":{"name":"2009 6th IEEE International Working Conference on Mining Software Repositories","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128112862","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}
Pub Date : 2009-05-16DOI: 10.1109/MSR.2009.5069499
Adrian Kuhn
As more and more open-source software components become available on the internet we need automatic ways to label and compare them. For example, a developer who searches for reusable software must be able to quickly gain an understanding of retrieved components. This understanding cannot be gained at the level of source code due to the semantic gap between source code and the domain model. In this paper we present a lexical approach that uses the log-likelihood ratios of word frequencies to automatically provide labels for software components. We present a prototype implementation of our labeling/comparison algorithm and provide examples of its application. In particular, we apply the approach to detect trends in the evolution of a software system.
{"title":"Automatic labeling of software components and their evolution using log-likelihood ratio of word frequencies in source code","authors":"Adrian Kuhn","doi":"10.1109/MSR.2009.5069499","DOIUrl":"https://doi.org/10.1109/MSR.2009.5069499","url":null,"abstract":"As more and more open-source software components become available on the internet we need automatic ways to label and compare them. For example, a developer who searches for reusable software must be able to quickly gain an understanding of retrieved components. This understanding cannot be gained at the level of source code due to the semantic gap between source code and the domain model. In this paper we present a lexical approach that uses the log-likelihood ratios of word frequencies to automatically provide labels for software components. We present a prototype implementation of our labeling/comparison algorithm and provide examples of its application. In particular, we apply the approach to detect trends in the evolution of a software system.","PeriodicalId":413721,"journal":{"name":"2009 6th IEEE International Working Conference on Mining Software Repositories","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133852894","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}
Pub Date : 2009-05-16DOI: 10.1109/MSR.2009.5069485
Holger Schackmann, H. Lichter
The lifecycle of defects reports and enhancement requests collected in the Bugzilla database of the GNOME project provides valuable information on the evolution of the change request process and for the assessment of process quality in the GNOME sub projects. We present a quality model for the analysis of quality characteristics that is based on evaluating metrics on the Bugzilla database, and illustrate it with a comparative evaluation for 25 of the largest products within GNOME.
{"title":"Evaluating process quality in GNOME based on change request data","authors":"Holger Schackmann, H. Lichter","doi":"10.1109/MSR.2009.5069485","DOIUrl":"https://doi.org/10.1109/MSR.2009.5069485","url":null,"abstract":"The lifecycle of defects reports and enhancement requests collected in the Bugzilla database of the GNOME project provides valuable information on the evolution of the change request process and for the assessment of process quality in the GNOME sub projects. We present a quality model for the analysis of quality characteristics that is based on evaluating metrics on the Bugzilla database, and illustrate it with a comparative evaluation for 25 of the largest products within GNOME.","PeriodicalId":413721,"journal":{"name":"2009 6th IEEE International Working Conference on Mining Software Repositories","volume":"161 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123504146","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}
Pub Date : 2009-05-16DOI: 10.1109/MSR.2009.5069482
Eric Enslen, Emily Hill, L. Pollock, K. Vijay-Shanker
Automated software engineering tools (e.g., program search, concern location, code reuse, quality assessment, etc.) increasingly rely on natural language information from comments and identifiers in code. The first step in analyzing words from identifiers requires splitting identifiers into their constituent words. Unlike natural languages, where space and punctuation are used to delineate words, identifiers cannot contain spaces. One common way to split identifiers is to follow programming language naming conventions. For example, Java programmers often use camel case, where words are delineated by uppercase letters or non-alphabetic characters. However, programmers also create identifiers by concatenating sequences of words together with no discernible delineation, which poses challenges to automatic identifier splitting. In this paper, we present an algorithm to automatically split identifiers into sequences of words by mining word frequencies in source code. With these word frequencies, our identifier splitter uses a scoring technique to automatically select the most appropriate partitioning for an identifier. In an evaluation of over 8000 identifiers from open source Java programs, our Samurai approach outperforms the existing state of the art techniques.
{"title":"Mining source code to automatically split identifiers for software analysis","authors":"Eric Enslen, Emily Hill, L. Pollock, K. Vijay-Shanker","doi":"10.1109/MSR.2009.5069482","DOIUrl":"https://doi.org/10.1109/MSR.2009.5069482","url":null,"abstract":"Automated software engineering tools (e.g., program search, concern location, code reuse, quality assessment, etc.) increasingly rely on natural language information from comments and identifiers in code. The first step in analyzing words from identifiers requires splitting identifiers into their constituent words. Unlike natural languages, where space and punctuation are used to delineate words, identifiers cannot contain spaces. One common way to split identifiers is to follow programming language naming conventions. For example, Java programmers often use camel case, where words are delineated by uppercase letters or non-alphabetic characters. However, programmers also create identifiers by concatenating sequences of words together with no discernible delineation, which poses challenges to automatic identifier splitting. In this paper, we present an algorithm to automatically split identifiers into sequences of words by mining word frequencies in source code. With these word frequencies, our identifier splitter uses a scoring technique to automatically select the most appropriate partitioning for an identifier. In an evaluation of over 8000 identifiers from open source Java programs, our Samurai approach outperforms the existing state of the art techniques.","PeriodicalId":413721,"journal":{"name":"2009 6th IEEE International Working Conference on Mining Software Repositories","volume":"312 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123678433","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}
Pub Date : 2009-05-16DOI: 10.1109/MSR.2009.5069486
Erik J. Linstead, P. Baldi
We adapt Latent Dirichlet Allocation to the problem of mining bug reports in order to define a new information-theoretic measure of coherence. We then apply our technique to a snapshot of the GNOME Bugzilla database consisting of 431,863 bug reports for multiple software projects. In addition to providing an unsupervised means for modeling report content, our results indicate substantial promise in applying statistical text mining algorithms for estimating bug report quality. Complete results are available from our supplementary materials website at http://sourcerer.ics.uci.edu/msr2009/gnome_coherence.html.
{"title":"Mining the coherence of GNOME bug reports with statistical topic models","authors":"Erik J. Linstead, P. Baldi","doi":"10.1109/MSR.2009.5069486","DOIUrl":"https://doi.org/10.1109/MSR.2009.5069486","url":null,"abstract":"We adapt Latent Dirichlet Allocation to the problem of mining bug reports in order to define a new information-theoretic measure of coherence. We then apply our technique to a snapshot of the GNOME Bugzilla database consisting of 431,863 bug reports for multiple software projects. In addition to providing an unsupervised means for modeling report content, our results indicate substantial promise in applying statistical text mining algorithms for estimating bug report quality. Complete results are available from our supplementary materials website at http://sourcerer.ics.uci.edu/msr2009/gnome_coherence.html.","PeriodicalId":413721,"journal":{"name":"2009 6th IEEE International Working Conference on Mining Software Repositories","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127411680","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}
Pub Date : 2009-05-16DOI: 10.1109/MSR.2009.5069501
Joel Ossher, S. Bajracharya, Erik J. Linstead, P. Baldi, C. Lopes
Abstract The open source movement has made vast quantities of source code available online for free, providing an extremely large dataset for empirical study and potential resuse. A major difficulty in exploiting this potential fully is that the data are currently scattered between competing source code repositories, none of which are structured for empirical analysis and cross-project comparison. As a result, software researchers and developers are left to compile their own datasets, resulting in duplicated effort and limited results. To address this challenge, we built SourcererDB, an aggregated repository of statically analyzed and cross-linked open source Java projects. SourcererDB contains local snapshots of 2,852 Java projects taken from Sourceforge, Apache and Java.net. These projects are statically analyzed to extract rich structural information, which is then stored in a relational database. References to entities in the 16,058 external jars are resolved and grouped, allowing for cross-project usage information to be accessed easily. This paper describes: (a) the mechanism for resolving and grouping these cross-project references, (b) the structure of and the metamodel for the SourcererDB repository, and (d) end-user dataset access mechanisms. Our goal in building SourcererDB is to provide a rich dataset of source code to facilitate the sharing of extracted data and to encourage reuse and repeatability of experiments.
{"title":"SourcererDB: An aggregated repository of statically analyzed and cross-linked open source Java projects","authors":"Joel Ossher, S. Bajracharya, Erik J. Linstead, P. Baldi, C. Lopes","doi":"10.1109/MSR.2009.5069501","DOIUrl":"https://doi.org/10.1109/MSR.2009.5069501","url":null,"abstract":"Abstract The open source movement has made vast quantities of source code available online for free, providing an extremely large dataset for empirical study and potential resuse. A major difficulty in exploiting this potential fully is that the data are currently scattered between competing source code repositories, none of which are structured for empirical analysis and cross-project comparison. As a result, software researchers and developers are left to compile their own datasets, resulting in duplicated effort and limited results. To address this challenge, we built SourcererDB, an aggregated repository of statically analyzed and cross-linked open source Java projects. SourcererDB contains local snapshots of 2,852 Java projects taken from Sourceforge, Apache and Java.net. These projects are statically analyzed to extract rich structural information, which is then stored in a relational database. References to entities in the 16,058 external jars are resolved and grouped, allowing for cross-project usage information to be accessed easily. This paper describes: (a) the mechanism for resolving and grouping these cross-project references, (b) the structure of and the metamodel for the SourcererDB repository, and (d) end-user dataset access mechanisms. Our goal in building SourcererDB is to provide a rich dataset of source code to facilitate the sharing of extracted data and to encourage reuse and repeatability of experiments.","PeriodicalId":413721,"journal":{"name":"2009 6th IEEE International Working Conference on Mining Software Repositories","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129439086","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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