Software Testing, Verification and Reliability

Cover image for Vol. 26 Issue 1

Edited By: Jeff Offutt and Robert M. Hierons

Impact Factor: 1.348

ISI Journal Citation Reports © Ranking: 2014: 35/104 (Computer Science Software Engineering)

Online ISSN: 1099-1689

Associated Title(s): Journal of Software: Evolution and Process, Software Process: Improvement and Practice, Software: Practice and Experience

Recently Published Issues

See all

Enjoy the ICST Special Issues published by Software Testing Verification and Reliability

 ICST
To celebrate the ongoing collaboration between the ICST Conference and Software Testing, Verification & Reliability, we have brought all previous and current ICST Special Issues together in one collection.
 Read all the ICST Special Issue Papers

Recently Published Articles

  1. Using combinatorial testing to build navigation graphs for dynamic web applications

    Wenhua Wang, Sreedevi Sampath, Yu Lei, Raghu Kacker, Richard Kuhn and James Lawrence

    Article first published online: 2 FEB 2016 | DOI: 10.1002/stvr.1599

    Thumbnail image of graphical abstract

    We present an approach to model the navigation structure of dynamic web applications by applying combinatorial strategies for systematic coverage of the navigation structure. We implement our approach in a tool, Tansuo, and experimentally evaluate its effectiveness on seven web applications with exploration guided by t-way coverage, for t = 1,2,3. We find that 2-way coverage is most effective and can cover more than 80% of the underlying code.

  2. Exhaustive test sets for algebraic specifications

    Marc Aiguier, Agnès Arnould, Pascale Le Gall and Delphine Longuet

    Article first published online: 27 JAN 2016 | DOI: 10.1002/stvr.1598

    Thumbnail image of graphical abstract

    In this paper, we study the conditions to ensure the exhaustiveness property of the set of observable consequences for several algebraic formalisms and several test hypotheses. Such a property is essential in testing because it prevents from rejecting a correct program or dually to accept an incorrect program. Hence, exhaustive test sets, when they exist, are appropriate to start the process of selecting test sets of reasonable size.

  3. You have free access to this content
    The dreaded desk reject (page 3)

    Robert M. Hierons

    Article first published online: 15 DEC 2015 | DOI: 10.1002/stvr.1597

  4. Stochastic modelling and simulation approaches to analysing enhanced fault tolerance on service-based software systems

    Kuan-Li Peng and Chin-Yu Huang

    Article first published online: 9 DEC 2015 | DOI: 10.1002/stvr.1596

    Thumbnail image of graphical abstract

    Fault tolerance (FT) is a proven technique to provide continuous and reliable service delivery when failures occur. However, the reliability and performance of FT should be carefully analyzed because of the overhead of invoking redundant services and the potential single point of failure in the FT mechanism. To address these problems, this paper proposes two approaches, the stochastic modeling approach and the simulation approach, for analysing the reliability and performance attributes of generalized FT designs in service-based software systems (SBSSs).

  5. Predicting metamorphic relations for testing scientific software: a machine learning approach using graph kernels

    Upulee Kanewala, James M. Bieman and Asa Ben-Hur

    Article first published online: 16 NOV 2015 | DOI: 10.1002/stvr.1594

    Thumbnail image of graphical abstract

    Challenges in creating suitable oracles limit the ability to perform automated testing in scientific software. Metamorphic testing is a method for automating the testing process for programmes without test oracles. However, finding the metamorphic relations satisfied by a programme remains a labour-intensive task. Here, we propose a machine learning approach for predicting metamorphic relations that uses a graph-based representation of a programme. Our results show that a graph kernel that uses all paths in the graph has the best prediction accuracy.

SEARCH

SEARCH BY CITATION