Giger, Emanuel; Pinzger, Martin; Gall, Harald C (2012). Can we predict types of code changes? An empirical analysis. In: 9th Working Conference on Mining Software Repositories, Zurich, Switzerland, 2 June 2012 - 3 June 2012. IEEE, 217-226.
Abstract
There exist many approaches that help in pointing developers to the change-prone parts of a software system. Although beneficial, they mostly fall short in providing details of these changes. Fine-grained source code changes (SCC) capture such detailed code changes and their semantics on the statement level. These SCC can be condition changes, interface modifications, inserts or deletions of methods and attributes, or other kinds of statement changes. In this paper, we explore prediction models for whether a source file will be affected by a certain type of SCC. These predictions are computed on the static source code dependency graph and use social network centrality measures and object-oriented metrics. For that, we use change data of the Eclipse platform and the Azureus 3 project. The results show that Neural Network models can predict categories of SCC types. Furthermore, our models can output a list of the potentially change-prone files ranked according to their change-proneness, overall and per change type category.
Item Type: | Conference or Workshop Item (Paper), refereed, original work |
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Communities & Collections: | 03 Faculty of Economics > Department of Informatics |
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Dewey Decimal Classification: | 000 Computer science, knowledge & systems |
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Scopus Subject Areas: | Physical Sciences > Computer Science Applications
Physical Sciences > Software |
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Scope: | Discipline-based scholarship (basic research) |
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Language: | English |
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Event End Date: | 3 June 2012 |
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Deposited On: | 29 Jan 2013 09:44 |
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Last Modified: | 06 Mar 2024 14:12 |
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Publisher: | IEEE |
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Series Name: | IEEE International Working Conference on Mining Software Repositories |
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ISSN: | 2160-1852 |
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ISBN: | 978-1-4673-1760-3 |
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Additional Information: | © 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
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OA Status: | Green |
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Publisher DOI: | https://doi.org/10.1109/MSR.2012.6224284 |
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Other Identification Number: | merlin-id:7101 |
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