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APFEL
A Preprocessing Framework For Eclipse

Lehrstuhl für Softwaretechnik (Prof. Zeller)
Universität des Saarlandes – Informatik
Informatik Campus des Saarlandes
Campus E9 1 (CISPA)
66123 Saarbrücken
E-mail: zeller @ cs.uni-saarland.de
Telefon: +49 681 302-70970

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The Software Evolution project at the Software Engineering Chair, Saarland University, analyzes version and bug databases to predict failure-prone modules, related changes, and future development activities.

Ready-to-go preprocessing of CVS

APFEL is an open source framework for the ECLIPSE programming environment that facilitates the analysis of CVS archives. Currently, APFEL provides the following preprocessing techniques:
  • extract CVS log information into a database;
  • group checkins to transactions using time windows;
  • detect added, modified, or removed entities;
  • automatic data cleaning for imports and for large transactions; and
  • manual data cleaning for merge transactions.
Additional preprocessing steps can easily be integrated into APFEL via the ECLIPSE plug-in mechanism. Plug-ins that work on the same kind of data can be grouped together in order to avoid expensive CVS operations.

APFEL serves as a platform for new analyses of version archives and enables the development of prototypes that demonstrate the benefit of an analysis for ECLIPSE users. One example of such a tool is the eROSE plug-in that guides programmers along related changes.

Software

  • The APFEL project is currently put on hold. Disappointed? Check out Jennifer Bevan’s Kenyon tool.
  • Subscribe to the APFEL mailing list (see below) and you will be notified of the first release.

Papers

  • Preprocessing CVS Data for Fine-Grained Analysis. T. Zimmermann, P. Weißgerber. Proc. 1st International Workshop on Mining Software Repositories (MSR), Edinburgh, UK, May 2004. [PDF]
    Abstract. All analyses of version archives have one phase in common: the preprocessing of data. Preprocessing has a direct impact on the quality of the results returned by an analysis. In this paper we discuss four essential preprocessing tasks necessary for a fine-grained analysis of CVS archives: data extraction, transaction recovery, mapping of changes to fine-grained entities, and data cleaning. We formalize the concept of sliding time windows and show how commit mails can relate revisions to transactions. We also present two approaches that map changes to the affected building blocks of a file, e.g. functions or sections.

Discuss APFEL @ Google

People

See also

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<webmaster@st.cs.uni-saarland.de> · http://www.st.cs.uni-saarland.de//softevo/apfel/?lang=en · Stand: 2018-04-05 13:41