Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.779353
Title: Oracle assessment, improvement and placement
Author: Jahangirova, Gunel
ISNI:       0000 0004 7965 0494
Awarding Body: UCL(University College London)
Current Institution: University College London (University of London)
Date of Award: 2019
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Abstract:
The oracle problem remains one of the key challenges in software testing, for which little automated support has been developed so far. This thesis analyses the prevalence of failed error propagation in programs with real faults to address the oracle placement problem and introduces an approach for iterative assessment and improvement of the oracles. To analyse failed error propagation in programs with real faults, we have conducted an empirical study, considering Defects4J, a benchmark of Java programs, of which we used all 6 projects available, 384 real bugs and 528 methods fixed to correct such bugs. The results indicate that the prevalence of failed error propagation is negligible. Moreover, the results on real faults differ from the results on mutants, indicating that if failed error propagation is taken into account, mutants are not a good surrogate of real faults. When measuring failed error propagation, for each method we use the strongest possible oracle as postcondition, which checks all externally observable program variables. The low prevalence of failed error propagation is caused by the presence of such a strong oracle, which usually is not available in practice. Therefore, there is a need for a technique to assess and improve existing weaker oracles. We propose a technique for assessing and improving test oracles, which necessarily places the human tester in the loop and is based on reducing the incidence of both false positives and false negatives. A proof showing that this approach results in an increase in the mutual information between the actual and perfect oracles is provided. The application of the approach to five real-world subjects shows that the fault detection rate of the oracles after improvement increases, on average, by 48.6%. The further evaluation with 39 participants assessed the ability of humans to detect false positives and false negatives manually, without any tool support. The correct classification rate achieved by humans in this case is poor (29%) indicating how helpful our automated approach can be for developers. The comparison of humans' ability to improve oracles with and without the tool in a study with 29 other participants also empirically validates the effectiveness of the approach.
Supervisor: Tonella, P. ; Clark, D. ; Harman, M. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.779353  DOI: Not available
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