Journal of Software: Evolution and Process

Cover image for Vol. 26 Issue 8

Special Issue: The 16th European Conference on Software Maintenance and Reengineering (CSMR 2012)

August 2014

Volume 26, Issue 8

Pages i–iii, 729–769

Issue edited by: T. Mens, A. Cleve

  1. Issue Information

    1. Top of page
    2. Issue Information
    3. Editorial
    4. Special Issue Papers
    1. Issue Information (pages i–iii)

      Article first published online: 19 AUG 2014 | DOI: 10.1002/smr.1629

  2. Editorial

    1. Top of page
    2. Issue Information
    3. Editorial
    4. Special Issue Papers
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  3. Special Issue Papers

    1. Top of page
    2. Issue Information
    3. Editorial
    4. Special Issue Papers
    1. The Linux kernel: a case study of build system variability (pages 730–746)

      Sarah Nadi and Ric Holt

      Article first published online: 18 APR 2013 | DOI: 10.1002/smr.1595

      Thumbnail image of graphical abstract

      Variability in the Linux kernel is implemented through three distinct artifacts: source code, Kconfig (configuration) files, and Kbuild files (Makefiles). Any inconsistencies between these three can lead to undesirable anomalies that can lead to increased maintenance efforts or decreased reliability. This paper focuses on analyzing the role and effect of Kbuild variability on the consistency of Linux. We provide a quantitative analysis of variability in Kbuild and then study how variability constraints in Kbuild affect variability anomalies detected in Linux.

    2. Large-scale inter-system clone detection using suffix trees and hashing (pages 747–769)

      Rainer Koschke

      Article first published online: 10 FEB 2013 | DOI: 10.1002/smr.1592

      Thumbnail image of graphical abstract

      Similar code between two systems can effectively and efficiently be found by a combination of hash-based filtering and sequence matching based on a suffix tree. Precision can effecetively be improved by filtering matches using a decission tree based on software metrics, in particular, parameter similarity. The decission tree can be automatically learned from a validated sample.

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