Software Testing, Verification and Reliability

Cover image for Vol. 26 Issue 5

August 2016

Volume 26, Issue 5

Pages 347–426

  1. Issue Information

    1. Top of page
    2. Issue Information
    3. Editorial
    4. Research Articles
    1. Issue Information (pages 347–349)

      Version of Record online: 15 JUL 2016 | DOI: 10.1002/stvr.1588

  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. A general modeling and analysis framework for software fault detection and correction process (pages 351–365)

      Yu Liu, Duo Li, Lujia Wang and Qingpei Hu

      Version of Record online: 7 MAR 2016 | DOI: 10.1002/stvr.1600

      Thumbnail image of graphical abstract

      In this paper, a framework of software reliability models containing both information from software fault detection process and correction process is studied. The proposed approach is based on Markov model other than a nonhomogeneous Poisson process model. Also, parameter estimation is carried out with weighted least-square estimation method, which emphasizes the influence of later data on the prediction. Two data sets from practical software development projects are applied with the proposed framework, which shows satisfactory performance with the results.

    2. You have full text access to this OnlineOpen article
      Seeding strategies in search-based unit test generation (pages 366–401)

      José Miguel Rojas, Gordon Fraser and Andrea Arcuri

      Version of Record online: 7 MAR 2016 | DOI: 10.1002/stvr.1601

      Thumbnail image of graphical abstract

      This paper investigates different strategies to seed values (numerical and string constants derived statically and dynamically, type information, and previous solutions) during search-based unit test generation. Results of a large empirical analysis show with strong statistical confidence that the use of appropriate seeding strategies can further improve the code coverage achieved by unit test generation tools.

    3. Prioritizing test cases for early detection of refactoring faults (pages 402–426)

      Everton L. G. Alves, Patrícia D. L. Machado, Tiago Massoni and Miryung Kim

      Version of Record online: 21 MAR 2016 | DOI: 10.1002/stvr.1603

      Thumbnail image of graphical abstract

      This article's main contributions are as follows:

      • A test case prioritization technique (RBA) centred on detecting refactoring edits faults earlier.
      • An evaluation of the technique by means of two empirical studies. The studies provide statistical evidence that RBA fosters early detection of refactoring-related behavioural changes.
      • A new metric for evaluating how spread is the fault-revealing test cases throughout a prioritized test sequence.

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