Mining version histories to guide software changes
We apply data mining to version histories in order to guide programmers along related changes: "Programmers who changed these functions also changed..." Given a set of existing changes, the mined association rules 1) suggest and predict likely further changes, 2) show up item coupling that...
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| Published in: | IEEE transactions on software engineering Vol. 31; no. 6; pp. 429 - 445 |
|---|---|
| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
New York
IEEE
01.06.2005
IEEE Computer Society |
| Subjects: | |
| ISSN: | 0098-5589, 1939-3520 |
| Online Access: | Get full text |
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| Abstract | We apply data mining to version histories in order to guide programmers along related changes: "Programmers who changed these functions also changed..." Given a set of existing changes, the mined association rules 1) suggest and predict likely further changes, 2) show up item coupling that is undetectable by program analysis, and 3) can prevent errors due to incomplete changes. After an initial change, our ROSE prototype can correctly predict further locations to be changed; the best predictive power is obtained for changes to existing software. In our evaluation based on the history of eight popular open source projects, ROSE's topmost three suggestions contained a correct location with a likelihood of more than 70 percent. |
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| AbstractList | We apply data mining to version histories in order to guide programmers along related changes: "Programmers who changed these functions also changed..." Given a set of existing changes, the mined association rules 1) suggest and predict likely further changes, 2) show up item coupling that is undetectable by program analysis, and 3) can prevent errors due to incomplete changes. After an initial change, our ROSE prototype can correctly predict further locations to be changed; the best predictive power is obtained for changes to existing software. In our evaluation based on the history of eight popular open source projects, ROSE's topmost three suggestions contained a correct location with a likelihood of more than 70 percent. We apply data mining to version histories in order to guide programmers along related changes: "Programmers who changed these functions also changed..." Given a set of existing changes, the mined association rules 1) suggest and predict likely further changes, 2) show up item coupling that is undetectable by program analysis, and 3) can prevent errors due to incomplete changes. After an initial change, our ROSE prototype can correctly predict further locations to be changed; the best predictive power is obtained for changes to existing software. In our evaluation based on the history of eight popular open source projects, ROSE's topmost three suggestions contained a correct location with a likelihood of more than 70 percent. [PUBLICATION ABSTRACT] |
| Author | Weissgerber, P. Zeller, A. Zimmermann, T. Diehl, S. |
| Author_xml | – sequence: 1 givenname: T. surname: Zimmermann fullname: Zimmermann, T. email: zimmerth@cs.uni-sb.de organization: Dept. of Comput. Sci., Saarlandes Univ., Saarbrucken, Germany – sequence: 2 givenname: A. surname: Zeller fullname: Zeller, A. email: zeller@acm.org organization: Dept. of Comput. Sci., Saarlandes Univ., Saarbrucken, Germany – sequence: 3 givenname: P. surname: Weissgerber fullname: Weissgerber, P. email: peter.weissgerber@ku-eichstaett.de – sequence: 4 givenname: S. surname: Diehl fullname: Diehl, S. email: diehl@acm.org |
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| References | ref13 ref35 ref12 ref15 Zimmermann (ref34) 2004 ref14 ref36 Ball (ref3) Srikant (ref31) ref11 ref33 ref10 ref2 Berliner (ref4) ref17 ref16 ref19 ref18 Xing (ref32) Fogel (ref9) 2002 ref24 ref23 Rijsbergen (ref25) 1979 ref20 Srikant (ref30) ref22 ref21 Agrawal (ref1) ref27 ref8 ref7 Sliwerski (ref29) ref6 ref5 Sayyad-Shirabad (ref28) Sayyad-Shirabad (ref26) |
| References_xml | – ident: ref6 doi: 10.1145/1056018.1056024 – ident: ref22 doi: 10.1109/ICSM.2000.883028 – volume-title: Master’s thesis, Universität Passau, Germany year: 2004 ident: ref34 article-title: Mining Version Archives to Guide Software Changes – volume-title: Proc. 25th Int’l Conf. Software Eng. (ICSE) ident: ref17 – year: 2002 ident: ref9 article-title: cvs2cl.pl: CVS-Log-Message-to-ChangeLog Conversion Script – ident: ref21 doi: 10.1145/337180.337200 – ident: ref7 doi: 10.1109/ICSM.2001.972749 – ident: ref10 doi: 10.1109/ICSM.1998.738508 – ident: ref23 doi: 10.1109/ICSE.2003.1201207 – volume-title: Proc. Int’l Workshop Mining Software Repositories (MSR) ident: ref29 article-title: When Do Changes Induce Fixes? On Fridays – volume-title: Proc. ICSE Workshop Process Modelling and Empirical Studies of Software Eng. ident: ref3 article-title: If Your Version Control System Could Talk – start-page: 487 volume-title: Proc. 20th Very Large Data Bases Conf. (VLDB) ident: ref1 article-title: Fast Algorithms for Mining Association Rules – ident: ref5 doi: 10.1109/WPC.2003.1199188 – ident: ref8 doi: 10.1109/ICSE.2003.1201219 – ident: ref13 doi: 10.1109/WPC.2005.18 – ident: ref15 doi: 10.1109/IWPSE.2003.1231214 – ident: ref36 doi: 10.1049/ic:20040466 – volume-title: Proc. Third Int’l Conf. KDD and Data Mining (KDD ’97) ident: ref31 article-title: Mining Association Rules with Item Constraints – ident: ref2 doi: 10.1007/BFb0053886 – ident: ref27 doi: 10.1109/ICSM.2003.1235410 – start-page: 341 volume-title: Proc. Winter 1990 USENIX Conf. ident: ref4 article-title: CVS II: Parallelizing Software Development – ident: ref14 doi: 10.1109/32.859533 – ident: ref35 doi: 10.1109/IWPSE.2003.1231213 – ident: ref16 doi: 10.1109/ICSM.2004.1357812 – start-page: 22 volume-title: Proc. Int’l Conf. Software Methods ident: ref26 article-title: Supporting Maintainance of Legacy Software with Data Mining Techniques – ident: ref18 doi: 10.1109/icsm.2001.972697 – ident: ref24 doi: 10.1109/icse.2004.1317529 – volume-title: Information Retrieval year: 1979 ident: ref25 – start-page: 407 volume-title: Proc. 21st Very Large Data Bases Conf. (VLDB) ident: ref30 article-title: Mining Generalized Association Rules – volume-title: Proc. 16th Int’l Conf. Software Eng. and Knowledge Eng. (SEKE ’04) ident: ref32 article-title: Data-Mining in Support of Detecting Class Co-Evolution – ident: ref33 doi: 10.1109/TSE.2004.52 – start-page: 53 volume-title: Proc. Int’l Workshop Mining Software Repositories (MSR 2004) ident: ref28 article-title: Mining the Software Change Repository of a Legacy Telephony System – ident: ref12 doi: 10.1109/IWPSE.2003.1231205 – ident: ref11 doi: 10.1109/ICSM.1997.624242 – volume-title: Proc. Int’l Workshop Principles of Software Evolution (IWPSE 2003) ident: ref19 – ident: ref20 doi: 10.1109/ASE.1999.802089 |
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| Snippet | We apply data mining to version histories in order to guide programmers along related changes: "Programmers who changed these functions also changed..." Given... |
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| SubjectTerms | Archives & records Association rules classification clustering Computer programming Computer programs Computer science Computer Society configuration management Data mining distribution enhancement Environmental management Error analysis Freeware History Index Terms- Programming environments/construction tools Java Joining maintenance Open source software Predictions Programmers Programming profession Prototypes Revisions Software Software engineering Software prototyping Studies |
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| Title | Mining version histories to guide software changes |
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