Software Testing, Verification and Reliability

Cover image for Vol. 24 Issue 6

September 2014

Volume 24, Issue 6

Pages i–ii, 413–495

  1. Issue Information

    1. Top of page
    2. Issue Information
    3. Editorial
    4. Research Articles
    1. You have free access to this content
      Issue Information (pages i–ii)

      Article first published online: 12 AUG 2014 | DOI: 10.1002/stvr.1515

  2. Editorial

    1. Top of page
    2. Issue Information
    3. Editorial
    4. Research Articles
    1. You have free access to this content
  3. Research Articles

    1. Top of page
    2. Issue Information
    3. Editorial
    4. Research Articles
    1. An improved Pareto distribution for modelling the fault data of open source software (pages 416–437)

      Shao-Pu Luan and Chin-Yu Huang

      Article first published online: 23 JUL 2013 | DOI: 10.1002/stvr.1504

      Thumbnail image of graphical abstract

      In this paper, a Single-Change-Point 2-Parameter Generalized Pareto Distribution (SCP-2GPD) model is proposed to model and analyze the fault distribution of Open Source Software. Experimental results show that proposed SCP-2GPD model has a flexible structure and can be used to model a wide spectrum of software development environments.

    2. Extending model checkers for hybrid system verification: the case study of SPIN (pages 438–471)

      María-del-Mar Gallardo and Laura Panizo

      Article first published online: 26 JUL 2013 | DOI: 10.1002/stvr.1505

      Thumbnail image of graphical abstract

      This paper presents a new methodology to extend explicit model checkers for hybrid systems analysis. The explicit model checker is integrated, in a non-intrusive way, with some external structures and existing abstraction libraries, which store and manipulate the abstraction of the continuous behaviour irrespective of the underlying model checker. The methodology is applied to SPIN using Parma Polyhedra Library.

    3. Search-based testing using constraint-based mutation (pages 472–495)

      Jan Malburg and Gordon Fraser

      Article first published online: 30 AUG 2013 | DOI: 10.1002/stvr.1508

      Thumbnail image of graphical abstract

      Many modern automated test generators are based on either metaheuristic search techniques or use constraint solvers. Both approaches have their advantages, but they also have specific drawbacks. This paper describes a method that integrates both techniques and delivers the best of both worlds. Experiments on 20 case study programs show an increase in coverage and a reduction in test set size over traditional techniques.

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