Reference : ZoomIn: Discovering Failures by Detecting Wrong Assertions
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/19573
ZoomIn: Discovering Failures by Detecting Wrong Assertions
English
Pastore, Fabrizio mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Mariani, Leonardo mailto [Universita degli Studi di Milano - Bicocca > Department of Informatics Systems and COmmunication]
May-2015
Proceedings of the 37th International Conference on Software Engineering (ICSE)
Yes
International
37th International Conference on Software Engineering
from 16-05-2015 to 24-05-2015
[en] Test Oracles ; Software Testing ; Test Generation
[en] Automatic testing, although useful, is still quite ineffective against faults that do not cause crashes or uncaught exceptions. In the majority of the cases automatic tests do not include oracles, and only in some cases they incorporate assertions that encode the observed behavior instead of the intended behavior, that is if the application under test produces a wrong result, the synthesized assertions will encode wrong expectations that match the actual behavior of the application.
In this paper we present ZoomIn, a technique that extends the fault-revealing capability of test case generation techniques from crash-only faults to faults that require non-trivial oracles to be detected. ZoomIn exploits the knowledge encoded in the manual tests written by developers and the similarity between executions to automatically determine an extremely small set of suspicious assertions that are likely wrong and thus worth manual inspection.
Early empirical results show that ZoomIn has been able to detect 50% of the analyzed non-crashing faults in the Apache Commons Math library requiring the inspection of less than 1.5% of the assertions automatically generated by EvoSuite.
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Software Verification and Validation Lab (SVV Lab)
Researchers ; Students
http://hdl.handle.net/10993/19573

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