Automated metamorphic testing on the analyses of feature models

Copyright © 2010 Elsevier B.V. All rights reserved.Context: A feature model (FM) represents the valid combinations of features in a domain. The automated extraction of information from FMs is a complex task that involves numerous analysis operations, techniques and tools. Current testing methods in this context are manual and rely on the ability of the tester to decide whether the output of an analysis is correct. However, this is acknowledged to be time-consuming, error-prone and in most cases infeasible due to the combinatorial complexity of the analyses, this is known as the oracle problem.Objective: In this paper, we propose using metamorphic testing to automate the generation of test data for feature model analysis tools overcoming the oracle problem. An automated test data generator is presented and evaluated to show the feasibility of our approach.Method: We present a set of relations (so-called metamorphic relations) between input FMs and the set of products they represent. Based on these relations and given a FM and its known set of products, a set of neighbouring FMs together with their corresponding set of products are automatically generated and used for testing multiple analyses. Complex FMs representing millions of products can be efficiently created by applying this process iteratively.Results: Our evaluation results using mutation testing and real faults reveal that most faults can be automatically detected within a few seconds. Two defects were found in FaMa and another two in SPLOT, two real tools for the automated analysis of feature models. Also, we show how our generator outperforms a related manual suite for the automated analysis of feature models and how this suite can be used to guide the automated generation of test cases obtaining important gains in efficiency.Conclusion: Our results show that the application of metamorphic testing in the domain of automated analysis of feature models is efficient and effective in detecting most faults in a few seconds without the need for a human oracle.This work has been partially supported by the European Commission(FEDER)and Spanish Government under CICYT project SETI(TIN2009-07366)and the Andalusian Government project ISABEL(TIC-2533)

An empirical comparison between direct and indirect test result checking approaches

The SOQUA 2006 Workshop was held in conjunction with the 14th ACM SIGSOFT International Symposium on Foundations of Software Engineering (SIGSOFT 2006/FSE-14) ACM Press, New York, NY.An oracle on software testing is a mechanism for checking whether the system under test has behaved correctly for any executions. In some situations, oracles are unavailable or too expensive to apply. This is known as the oracle problem. It is crucial to develop techniques to address it, and metamorphic testing (MT) was one of such proposals. This paper conducts a controlled experiment to investigate the cost effectiveness of using MT by 38 testers on three open-source programs. The fault detection capability and time cost of MT are compared with the popular assertion checking method. Our results show that MT is cost-efficient and has potentials for detecting more faults than the assertion checking method. Copyright 2006 ACM.preprintThis research is supported in part by a grant of the Research Grants Council of Hong Kong (project no. HKU 7145/04E), a grant of City University of Hong Kong and a grant of The University of Hong Kong

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

AI-driven web API testing

Testing of web APIs is nowadays more critical than ever before, as they are the current standard for software integration. A bug in an organization’s web API could have a huge impact both in ternally (services relying on that API) and externally (third-party applications and end users). Most existing tools and testing ap proaches require writing tests or instrumenting the system under test (SUT). The main aim of this dissertation is to take web API testing to an unprecedented level of automation and thoroughness. To this end, we plan to apply artificial intelligence (AI) techniques for the autonomous detection of software failures. Specifically, the idea is to develop intelligent programs (we call them “bots”) ca pable of generating hundreds, thousands or even millions of test inputs and to evaluate whether the test outputs are correct based on: 1) patterns learned from previous executions of the SUT; and 2) knowledge gained from analyzing thousands of similar programs. Evaluation results of our initial prototype are promising, with bugs being automatically detected in some real-world APIs.Ministerio de Economía y Competitividad BELI (TIN2015-70560-R)Ministerio de Ciencia, Innovación y Universidades RTI2018-101204-B-C21 (HORATIO)Ministerio de Educación, Cultura y Deporte FPU17/0407

Dynamic random testing of web services: a methodology and evaluation

In recent years, Service Oriented Architecture (SOA) has been increasingly adopted to develop distributed applications in the context of the Internet. To develop reliable SOA-based applications, an important issue is how to ensure the quality of web services. In this paper, we propose a dynamic random testing (DRT) technique for web services, which is an improvement over the widely-practiced random testing (RT) and partition testing (PT). We examine key issues when adapting DRT to the context of SOA, including a framework, guidelines for parameter settings, and a prototype for such an adaptation. Empirical studies are reported where DRT is used to test three real-life web services, and mutation analysis is employed to measure the effectiveness. Our experimental results show that, compared with the three baseline techniques, RT, Adaptive Testing (AT) and Random Partition Testing (RPT), DRT demonstrates higher fault-detection effectiveness with a lower test case selection overhead. Furthermore, the theoretical guidelines of parameter setting for DRT are confirmed to be effective. The proposed DRT and the prototype provide an effective and efficient approach for testing web services. IEE

Automated analysis of feature models: Quo vadis?

Feature models have been used since the 90's to describe software product lines as a way of reusing common parts in a family of software systems. In 2010, a systematic literature review was published summarizing the advances and settling the basis of the area of Automated Analysis of Feature Models (AAFM). From then on, different studies have applied the AAFM in different domains. In this paper, we provide an overview of the evolution of this field since 2010 by performing a systematic mapping study considering 423 primary sources. We found six different variability facets where the AAFM is being applied that define the tendencies: product configuration and derivation; testing and evolution; reverse engineering; multi-model variability-analysis; variability modelling and variability-intensive systems. We also confirmed that there is a lack of industrial evidence in most of the cases. Finally, we present where and when the papers have been published and who are the authors and institutions that are contributing to the field. We observed that the maturity is proven by the increment in the number of journals published along the years as well as the diversity of conferences and workshops where papers are published. We also suggest some synergies with other areas such as cloud or mobile computing among others that can motivate further research in the future.Ministerio de Economía y Competitividad TIN2015-70560-RJunta de Andalucía TIC-186

Improving the Dependability of Machine Learning Applications

As machine learning (ML) applications become prevalent in various aspects of everyday life, their dependability takes on increasing importance. It is challenging to test such applications, however, because they are intended to learn properties of data sets where the correct answers are not already known. Our work is not concerned with testing how well an ML algorithm learns, but rather seeks to ensure that an application using the algorithm implements the specification correctly and fulfills the users' expectations. These are critical to ensuring the application's dependability. This paper presents three approaches to testing these types of applications. In the first, we create a set of limited test cases for which it is, in fact, possible to predict what the correct output should be. In the second approach, we use random testing to generate large data sets according to parameterization based on the application's equivalence classes. Our third approach is based on metamorphic testing, in which properties of the application are exploited to define transformation functions on the input, such that the new output can easily be predicted based on the original output. Here we discuss these approaches, and our findings from testing the dependability of three real-world ML applications

An Enhanced Software Quality Testing Approach Using Metamorphic Testing Technique

The software testing process plays an important role in improving the quality of the software product. The product or program which is free from errors greatly contributes to assuring the quality of the software. An oracle in software testing is a person (tester) who performs the testing process. The oracle problem is the difficulty of determining the expected outcomes of selected test cases. A tester (oracle) may not always be available, or might be available but the process is too expensive and difficult to apply. The research presented in this paper proposes an approach for reducing the effect of the oracle problem during testing software and hence enhancing the quality of testing. Metamorphic Testing (MT) approach has been introduced and applied to generate a follow-up test case for multiple executions of program under test and verify the result automatically.  An experimental method has been used to explain the mechanism of work for (MT). JUNIT tool which supports MT has been used to apply selected case studies (trigonometric function, geometric shapes classification, booking web service). The obtained results showed a good enhancement in the testing process. The importance of this research lies in overcoming oracle problem or alleviates it and thus, the research contributes to knowledge the domain by guiding researchers to use the metamorphic method because of its great advantages, as well as evaluating the effect of metamorphic method through empirical studies