Linking e-mails and source code artifacts

Bacchelli, Alberto; Lanza, Michele; Robbes, Romain

doi:10.1145/1806799.1806855

Cited by 148 publications

(117 citation statements)

References 23 publications

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“…Once the model is generated, the parsing infrastructure classifies the content of the documentation and the support channel: it identifies the codelike terms, the code snippets, and their probable kind (e.g., class, method, XML element, Java code snippet, XML code snippet). We relied on existing techniques described in the literature [7] and in our previous work [12] to implement the content classification step. A brief description of the classification process and the evaluation of its accuracy is presented in a technical report [13].…”

Section: Sectionmentioning

confidence: 99%

“…Bacchelli et al compared various techniques to identify code elements and code snippets in email messages and found that lightweight techniques based on regular expressions performed better than information retrieval techniques such as latent semantic indexing [7]. We implemented our documentation and support channel parsers with regular expressions based on the observations of this study.…”

Section: Related Workmentioning

confidence: 99%

“…For example, Hipikat links coarse-grained project artifacts such as code commits, emails, and bug reports based on bug numbers [6]. Bacchelli et al devised a technique that identifies source code (e.g., code snippets) in emails and that can link classes mentioned in the email to classes declared in a codebase [7], but the technique does not work at the sub-class level of granularity.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Recovering traceability links between an API and its learning resources

Dagenais¹,

Robillard²

2012

2012 34th International Conference on Software Engineering (ICSE)

116

110

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Abstract-Large frameworks and libraries require extensive developer learning resources, such as documentation and mailing lists, to be useful. Maintaining these learning resources is challenging partly because they are not explicitly linked to the frameworks' API, and changes in the API are not reflected in the learning resources. Automatically recovering traceability links between an API and learning resources is notoriously difficult due to the inherent ambiguity of unstructured natural language. Code elements mentioned in documents are rarely fully qualified, so readers need to understand the context in which a code element is mentioned. We propose a technique that identifies code-like terms in documents and links these terms to specific code elements in an API, such as methods. In an evaluation study with four open source systems, we found that our technique had an average recall and precision of 96%.

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Section: Sectionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Recovering traceability links between an API and its learning resources

Dagenais¹,

Robillard²

2012

2012 34th International Conference on Software Engineering (ICSE)

116

110

View full text Add to dashboard Cite

show abstract

“…Trace the related artifacts that contain the external links as prepared by bacchelli al. [8] to identify all the mail messages defined. …”

Section: Mining Unstructured Data In Software Repositories: Current Amentioning

confidence: 99%

A Review on Software Mining: Current Trends and Methodologies

Ubhi¹,

Sahiwal²

2017

IJERA

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With the evolution of Software Mining, it has enabled to play a crucial role in the day to day activities .By empowering the software with the data mining methods it aids all the software developers and the managers at a the managerial levels to use it as a tool for knowledge discovery from the relative software engineering data (in the form of code, design documents, bug reports) that would help in visualizing the project"s status of evolution and progress. To add on, further all the mining methods and algorithms help to device the models that would result in the development of any fault prone real time system prior to the testing phase or the evolution phase. This review paper will highlight the different methodologies for software mining along with its extension as a tool for fault tolerance and bibliographic reference with the special eminence on mining the software engineering information.

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“…For example, Bachelli et al [21], [22] investigated the interlinking of email data from mailing lists with commit or patch data. The area of traceability research (e.g., [23], [24]) addresses the interlinking of source code changes with documentation changes.…”

Section: Related Workmentioning

confidence: 99%

Discovering Loners and Phantoms in Commit and Issue Data

Schermann

Brandtner

Panichella

et al. 2015

2015 IEEE 23rd International Conference on Program Comprehension

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The interlinking of commit and issue data has become a de-facto standard in software development. Modern issue tracking systems, such as JIRA, automatically interlink commits and issues by the extraction of identifiers (e.g., issue key) from commit messages. However, the conventions for the use of interlinking methodologies vary between software projects. For example, some projects enforce the use of identifiers for every commit while others have less restrictive conventions. In this work, we introduce a model called PaLiMod to enable the analysis of interlinking characteristics in commit and issue data. We surveyed 15 Apache projects to investigate differences and commonalities between linked and non-linked commits and issues. Based on the gathered information, we created a set of heuristics to interlink the residual of non-linked commits and issues. We present the characteristics of Loners and Phantoms in commit and issue data. The results of our evaluation indicate that the proposed PaLiMod model and heuristics enable an automatic interlinking and can indeed reduce the residual of non-linked commits and issues in software projects. Abstract-The interlinking of commit and issue data has become a de-facto standard in software development. Modern issue tracking systems, such as JIRA, automatically interlink commits and issues by the extraction of identifiers (e.g., issue key) from commit messages. However, the conventions for the use of interlinking methodologies vary between software projects. For example, some projects enforce the use of identifiers for every commit while others have less restrictive conventions. In this work, we introduce a model called PaLiMod to enable the analysis of interlinking characteristics in commit and issue data. We surveyed 15 Apache projects to investigate differences and commonalities between linked and non-linked commits and issues. Based on the gathered information, we created a set of heuristics to interlink the residual of non-linked commits and issues. We present the characteristics of Loners and Phantoms in commit and issue data. The results of our evaluation indicate that the proposed PaLiMod model and heuristics enable an automatic interlinking and can indeed reduce the residual of non-linked commits and issues in software projects.

show abstract

Linking e-mails and source code artifacts

Cited by 148 publications

References 23 publications

Recovering traceability links between an API and its learning resources

Recovering traceability links between an API and its learning resources

A Review on Software Mining: Current Trends and Methodologies

Discovering Loners and Phantoms in Commit and Issue Data

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