Software Testing, Verification and Reliability

Cover image for Vol. 25 Issue 4

Special Issue: ICST 2013 Conference

June 2015

Volume 25, Issue 4

Pages i–ii, 333–459

Issue edited by: Benoit Baudry, Alessandro Orso

  1. Issue Information

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

      Article first published online: 7 MAY 2015 | DOI: 10.1002/stvr.1553

  2. Editorial

    1. Top of page
    2. Issue Information
    3. Editorial
    4. Special Issue Papers
    1. You have free access to this content
      Special issue for the ICST 2013 conference (page 333)

      Benoit Baudry and Alessandro Orso

      Article first published online: 7 MAY 2015 | DOI: 10.1002/stvr.1578

  3. Special Issue Papers

    1. Top of page
    2. Issue Information
    3. Editorial
    4. Special Issue Papers
    1. Are concurrency coverage metrics effective for testing: a comprehensive empirical investigation (pages 334–370)

      Shin Hong, Matt Staats, Jaemin Ahn, Moonzoo Kim and Gregg Rothermel

      Article first published online: 23 JUN 2014 | DOI: 10.1002/stvr.1539

      Thumbnail image of graphical abstract

      We explore the impact of concurrency coverage metrics on testing effectiveness and examine the relationship between coverage, fault detection, and test size. We study eight existing coverage metrics and six new metrics formed by combining complementary metrics. Our results indicate that the metrics are moderate to strong predictors of testing effectiveness and effective at providing test generation targets. Nevertheless, metric effectiveness varies across programs, even for the combinations of metrics. This result highlights the need for improving concurrency coverage metrics.

    2. Coverage-based regression test case selection, minimization and prioritization: a case study on an industrial system (pages 371–396)

      Daniel Di Nardo, Nadia Alshahwan, Lionel Briand and Yvan Labiche

      Article first published online: 7 APR 2015 | DOI: 10.1002/stvr.1572

      Thumbnail image of graphical abstract

      This paper presents a case study of coverage-based regression testing techniques on a real-world industrial system with real regression faults. The results show that prioritization techniques that are based on additional coverage with finer-grained coverage criteria perform significantly better in fault detection rates, test selection does not provide significant savings in execution cost (<2%), and test suite minimization using finer-grained coverage criteria could provide significant savings in execution cost (79.5%) while maintaining a fault detection capability level above 70%.

    3. A study and toolkit of CHECK-THEN-ACT idioms of Java concurrent collections (pages 397–425)

      Yu Lin and Danny Dig

      Article first published online: 9 FEB 2015 | DOI: 10.1002/stvr.1567

      Thumbnail image of graphical abstract

      A common usage of concurrent collections is composed of two operations where a check on the collection (e.g., collection contains an element) precedes an action (e.g., inserting an element). Unless the whole composition is atomic, the usage contains an atomicity violation bug. This paper presents an extensive empirical study of CHECK-THEN-ACT idioms of Java concurrent collections by analyzing 28 widely used open source Java projects, which shows that such idioms are commonly misused and can lead to bugs.

    4. Defect prediction as a multiobjective optimization problem (pages 426–459)

      Gerardo Canfora, Andrea De Lucia, Massimiliano Di Penta, Rocco Oliveto, Annibale Panichella and Sebastiano Panichella

      Article first published online: 8 MAR 2015 | DOI: 10.1002/stvr.1570

      Thumbnail image of graphical abstract

      When performing defect prediction, a software engineer often wants to achieve conflicting objectives, for example, maximizing the number of identified defect-prone classes and reducing the code inspection cost. In this paper, we formulate the defect-prediction problem as a multi-objective problem and propose an approach called Multi-objective Defect Prediction (MODEP), which uses a genetic algorithm to build defect predictor models. Results of an empirical study indicate that MODEP outperforms single-objective predictors, trivial baselines, and local prediction models.

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