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

A Survey on Metamorphic Testing

A test oracle determines whether a test execution reveals a fault, often by comparing the observed program output to the expected output. This is not always practical, for example when a program's input-output relation is complex and difficult to capture formally. Metamorphic testing provides an alternative, where correctness is not determined by checking an individual concrete output, but by applying a transformation to a test input and observing how the program output “morphs” into a different one as a result. Since the introduction of such metamorphic relations in 1998, many contributions on metamorphic testing have been made, and the technique has seen successful applications in a variety of domains, ranging from web services to computer graphics. This article provides a comprehensive survey on metamorphic testing: It summarises the research results and application areas, and analyses common practice in empirical studies of metamorphic testing as well as the main open challenges

A Survey on Metamorphic Testing

A test oracle determines whether a test execution reveals a fault, often by comparing the observed program output to the expected output. This is not always practical, for example when a program’s input-output relation is complex and difficult to capture formally. Metamorphic testing provides an alternative, where correctness is not determined by checking an individual concrete output, but by applying a transformation to a test input and observing how the program output “morphs” into a different one as a result. Since the introduction of such metamorphic relations in 1998, many contributions on metamorphic testing have been made, and the technique has seen successful applications in a variety of domains, ranging from web services to computer graphics. This article provides a comprehensive survey on metamorphic testing: It summarises the research results and application areas, and analyses common practice in empirical studies of metamorphic testing as well as the main open challenges.European Commission (FEDER)Spanish Govermen

A Scenario-Based Functional Testing Approach to Improving DNN Performance

This paper proposes a scenario-based functional testing approach for enhancing the performance of machine learning (ML) applications. The proposed method is an iterative process that starts with testing the ML model on various scenarios to identify areas of weakness. It follows by a further testing on the suspected weak scenarios and statistically evaluate the model's performance on the scenarios to confirm the diagnosis. Once the diagnosis of weak scenarios is confirmed by test results, the treatment of the model is performed by retraining the model using a transfer learning technique with the original model as the base and applying a set of training data specifically targeting the treated scenarios plus a subset of training data selected at random from the original train dataset to prevent the so-call catastrophic forgetting effect. Finally, after the treatment, the model is assessed and evaluated again by testing on the treated scenarios as well as other scenarios to check if the treatment is effective and no side effect caused. The paper reports a case study with a real ML deep neural network (DNN) model, which is the perception system of an autonomous racing car. It is demonstrated that the method is effective in the sense that DNN model's performance can be improved. It provides an efficient method of enhancing ML model's performance with much less human and compute resource than retrain from scratch.Comment: The paper is accepted to appear in the proceedings of IEEE 17th International Conference on Service-oriented Systems Engineering (IEEE SOSE 2023) as an invited paper of 2023 IEEE CISOSE Congres

Metamorphic testing for web services: Framework and a case study

Service Oriented Architecture (SOA) has become a major application development paradigm. As a basic unit of SOA applications, Web services significantly affect the quality of the applications constructed from them. Since the development and consumption of Web services are completely separated under SOA environment, the consumers are normally provided with limited knowledge of the services and thus have little information about test oracles. The lack of source code and the restricted control of Web services limit the testability of Web services. To address the prominent oracle problem when testing Web services, we propose a metamorphic testing framework for Web services taking into account the unique features of SOA. We conduct a case study where the new metamorphic testing framework is employed to test a Web service that implements the electronic payment. The results of case study show the feasibility of the framework for web services, and also the efficiency of metamorphic testing. The work presented in the paper alleviates the test oracle problem when testing Web services under SOA

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

Analysis and Detection of Metamorphic Computer Viruses

Virus writers and anti-virus researches generally agree that metamorphism is the way to generate undetectable viruses. Several virus writers have released virus creation kits and claimed that they possess the ability to automatically produce morphed virus variants that look substantially different from one another. To see how effective these code morphing engines are, and how much difference exists between variants of a same virus, we measured the similarity between virus variants generated by four virus generators downloaded from the Internet. Our result shows that the effectiveness of these generators varies widely. While the best generator, NGVCK, is able to create viruses that share only a few percent of similarities, the other generators produce viruses that are over 60% similar, on average. In addition, our similarity graphs show that some of these variant pairs have long segments of identical assembly opcodes at identical positions of the virus files. Compared to random utility files which have a similarity of about 35%, we see that some of the virus creation kits are not very effective. To detect metamorphic virus variants, we experimented with the use of hidden Markov models (HMMs) to capture the statistical properties of viruses in the same family. We generated 200 NGVCK viruses, trained 25 models and used the trained models to classify 65 programs including both NGVCK viruses and other random non-viral programs. For seven of our models we were able to perfectly distinguish the two types of files by their scores. The other cases produced different number of false positives and false negatives, depending on the threshold used in the classifying process. In most cases, our models were able to have a detection rate of over 90% and a false positive rate of less than 10%. The number of states N of a model does not seem to have much impact on the performance of the HMM. We saw only small differences in the performance measures for models with N from 3 to 6. If the variants of a metamorphic virus are sufficiently different that signature-based scanning cannot detect a newly morphed variant, the HMM approach provides a feasible solution. As with any statistical detection method, false predictions are possible. We showed the tradeoff between the detection rate and false positive rate