Software Testing, Verification and Reliability
© John Wiley & Sons Ltd
Edited By: Jeff Offutt and Robert M. Hierons
Impact Factor: 1.082
ISI Journal Citation Reports © Ranking: 2015: 46/106 (Computer Science Software Engineering)
Online ISSN: 1099-1689
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Recently Published Articles
- Evaluating code-based test input generator tools
Lajos Cseppentő and Zoltán Micskei
Version of Record online: 6 FEB 2017 | DOI: 10.1002/stvr.1627
This paper presents a method and an open-source framework to evaluate and compare code-based test input generator tools. Based on core and challenging features of programming languages, 363 code snippets were defined that can serve as inputs for the tools. Five Java and one .NET-based tools were evaluated using different metrics and their strengths and weaknesses were highlighted.
- Test suite completeness and black box testing
Adilson Luiz Bonifácio and Arnaldo Vieira Moura
Version of Record online: 9 JAN 2017 | DOI: 10.1002/stvr.1626
This work allows for test cases to block giving rise to a natural variant of completeness called perfectness. Perfectness guarantees that non-compliance between a specification and an implementation will always be detected, even in the presence of blocking test cases. This study characterizes perfectness by isomorphisms, and establishes a relationship between the notion of completeness and perfectness. Further, a sharp upper bound is given on the number of states in implementations, beyond which no test suite can be complete.
- Causal inference based fault localization for numerical software with NUMFL
Zhuofu Bai, Gang Shu and Andy Podgurski
Version of Record online: 28 NOV 2016 | DOI: 10.1002/stvr.1613
This paper presents NUMFL, a value-based causal inference technique for localizing faults in numerical software. Given value profiles for an expression's variables, NUMFL uses generalized propensity scores (GPSs) or covariate balancing propensity scores (CBPSs) to reduce confounding bias caused by evaluation of other faulty expressions. It estimates the average failure-causing effect of an expression using statistical regression models. The empirical results indicate that NUMFL is more effective than competitive metrics, and NUMFL works surprisingly well with data from failing runs alone.
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