3,887 research outputs found
A new method for constructing metamorphic relations
A fundamental problem for software testing is the oracle problem, which means that in many practical situations, it is extremely expensive, if not impossible, to verify the test result given any possible program input. Metamorphic testing is an approach to alleviating the oracle problem. The key part of metamorphic testing is a set of necessary properties of the software under test, namely metamorphic relations. Metamorphic relations not only help generate test cases, but also provide a mechanism to partially verify the test results without the need of oracle. In most previous studies, metamorphic relations were identified manually by testers in an ad hoc way. There is no systematic methodology that helps us identify metamorphic relations. In this paper, we propose a simple method, namely, the composition of metamorphic relations, for systematically constructing new metamorphic relations based on the already identified metamorphic relations. We conduct a case study and show that new metamorphic relations can be easily constructed by compositing some existing metamorphic relations. It is also observed that the new metamorphic relations are very likely to deliver a higher cost-effectiveness of metamorphic testing than the original metamorphic relations
Test Case Purification for Improving Fault Localization
Finding and fixing bugs are time-consuming activities in software
development. Spectrum-based fault localization aims to identify the faulty
position in source code based on the execution trace of test cases. Failing
test cases and their assertions form test oracles for the failing behavior of
the system under analysis. In this paper, we propose a novel concept of
spectrum driven test case purification for improving fault localization. The
goal of test case purification is to separate existing test cases into small
fractions (called purified test cases) and to enhance the test oracles to
further localize faults. Combining with an original fault localization
technique (e.g., Tarantula), test case purification results in better ranking
the program statements. Our experiments on 1800 faults in six open-source Java
programs show that test case purification can effectively improve existing
fault localization techniques
On testing effectiveness of metamorphic relations: A case study
One fundamental challenge for software testing is the oracle problem which means that either there does not exist a mechanism (called oracle) to verify the test output given any possible program input or it is very expensive if not impossible to apply the oracle. Metamorphic testing is an innovative approach to oracle problem. In metamorphic testing metamorphic relations are derived from the innate characteristics of the software under test. These relations can help to generate test data and verify the correctness of the test result without the need of oracle. The effectiveness of metamorphic relations can play a significant role in the testing process. It has been argued that the metamorphic relations that cause different software execution behaviors should have high fault detection ability. In this paper we conduct a case study to analyze the relationship between the execution behavior and the fault-detection effectiveness of metamorphic relations. Some code coverage criteria are used to reflect the execution behavior. It is shown that there is a certain degree of correlation between the code coverage achieved by a metamorphic relation and its fault-detection effectiveness
Performance-Driven Metamorphic Testing of Cyber-Physical Systems
Cyber-physical systems (CPSs) are a new generation
of systems, which integrate software with physical processes. The
increasing complexity of these systems, combined with the un certainty in their interactions with the physical world, makes the
definition of effective test oracles especially challenging, facing the
well-known test oracle problem. Metamorphic testing has shown
great potential to alleviate the test oracle problem by exploiting the
relations among the inputs and outputs of different executions of
the system, so-called metamorphic relations (MRs). In this article,
we propose an MR pattern called PV for the identification of
performance-driven MRs, and we show its applicability in two
CPSs from different domains, which are automated navigation
systems and elevator control systems. For the evaluation, we as sessed the effectiveness of this approach for detecting failures in an
open-source simulation-based autonomous navigation system, as
well as in an industrial case study from the elevation domain. We
derive concrete MRs based on the PV pattern for both case studies,
and we evaluate their effectiveness with seeded faults. Results show
that the approach is effective at detecting over 88% of the seeded
faults, while keeping the ratio of FPs at 4% or lower.European Union's Horizon 2020 Research and Innovation Programme (Grant Number: 871319)Junta de Andalucía US-1264651 (APOLO)Junta de Andalucía P18-FR-2895 (EKIPMENT-PLUS)Ministerio de Ciencia e Innovación RTI2018-101204-B-C21 (HORATIO)Mondragon Unibertsitatea IT1519-2
The Integration of Machine Learning into Automated Test Generation: A Systematic Mapping Study
Context: Machine learning (ML) may enable effective automated test
generation.
Objective: We characterize emerging research, examining testing practices,
researcher goals, ML techniques applied, evaluation, and challenges.
Methods: We perform a systematic mapping on a sample of 102 publications.
Results: ML generates input for system, GUI, unit, performance, and
combinatorial testing or improves the performance of existing generation
methods. ML is also used to generate test verdicts, property-based, and
expected output oracles. Supervised learning - often based on neural networks -
and reinforcement learning - often based on Q-learning - are common, and some
publications also employ unsupervised or semi-supervised learning.
(Semi-/Un-)Supervised approaches are evaluated using both traditional testing
metrics and ML-related metrics (e.g., accuracy), while reinforcement learning
is often evaluated using testing metrics tied to the reward function.
Conclusion: Work-to-date shows great promise, but there are open challenges
regarding training data, retraining, scalability, evaluation complexity, ML
algorithms employed - and how they are applied - benchmarks, and replicability.
Our findings can serve as a roadmap and inspiration for researchers in this
field.Comment: Under submission to Software Testing, Verification, and Reliability
journal. (arXiv admin note: text overlap with arXiv:2107.00906 - This is an
earlier study that this study extends
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