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

Automatic Software Repair: a Bibliography

This article presents a survey on automatic software repair. Automatic software repair consists of automatically finding a solution to software bugs without human intervention. This article considers all kinds of repairs. First, it discusses behavioral repair where test suites, contracts, models, and crashing inputs are taken as oracle. Second, it discusses state repair, also known as runtime repair or runtime recovery, with techniques such as checkpoint and restart, reconfiguration, and invariant restoration. The uniqueness of this article is that it spans the research communities that contribute to this body of knowledge: software engineering, dependability, operating systems, programming languages, and security. It provides a novel and structured overview of the diversity of bug oracles and repair operators used in the literature

Metamorphic testing: a review of challenges and opportunities

Metamorphic testing is an approach to both test case generation and test result verification. A central element is a set of metamorphic relations, which are necessary properties of the target function or algorithm in relation to multiple inputs and their expected outputs. Since its first publication, we have witnessed a rapidly increasing body of work examining metamorphic testing from various perspectives, including metamorphic relation identification, test case generation, integration with other software engineering techniques, and the validation and evaluation of software systems. In this paper, we review the current research of metamorphic testing and discuss the challenges yet to be addressed. We also present visions for further improvement of metamorphic testing and highlight opportunities for new research

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

Model Transformation Testing and Debugging: A Survey

Model transformations are the key technique in Model-Driven Engineering (MDE) to manipulate and construct models. As a consequence, the correctness of software systems built with MDE approaches relies mainly on the correctness of model transformations, and thus, detecting and locating bugs in model transformations have been popular research topics in recent years. This surge of work has led to a vast literature on model transformation testing and debugging, which makes it challenging to gain a comprehensive view of the current state of the art. This is an obstacle for newcomers to this topic and MDE practitioners to apply these approaches. This paper presents a survey on testing and debugging model transformations based on the analysis of \nPapers~papers on the topics. We explore the trends, advances, and evolution over the years, bringing together previously disparate streams of work and providing a comprehensive view of these thriving areas. In addition, we present a conceptual framework to understand and categorise the different proposals. Finally, we identify several open research challenges and propose specific action points for the model transformation community.This work is partially supported by the European Commission (FEDER) and Junta de Andalucia under projects APOLO (US-1264651) and EKIPMENT-PLUS (P18-FR-2895), by the Spanish Government (FEDER/Ministerio de Ciencia e Innovación – Agencia Estatal de Investigación) under projects HORATIO (RTI2018-101204-B-C21), COSCA (PGC2018-094905-B-I00) and LOCOSS (PID2020-114615RB-I00), by the Austrian Science Fund (P 28519-N31, P 30525-N31), and by the Austrian Federal Ministry for Digital and Economic Affairs and the National Foundation for Research, Technology and Development (CDG

Doctor of Philosophy

dissertationIn computer science, functional software testing is a method of ensuring that software gives expected output on specific inputs. Software testing is conducted to ensure desired levels of quality in light of uncertainty resulting from the complexity of software. Most of today's software is written by people and software development is a creative activity. However, due to the complexity of computer systems and software development processes, this activity leads to a mismatch between the expected software functionality and the implemented one. If not addressed in a timely and proper manner, this mismatch can cause serious consequences to users of the software, such as security and privacy breaches, financial loss, and adversarial human health issues. Because of manual effort, software testing is costly. Software testing that is performed without human intervention is automatic software testing and it is one way of addressing the issue. In this work, we build upon and extend several techniques for automatic software testing. The techniques do not require any guidance from the user. Goals that are achieved with the techniques are checking for yet unknown errors, automatically testing object-oriented software, and detecting malicious software. To meet these goals, we explored several techniques and related challenges: automatic test case generation, runtime verification, dynamic symbolic execution, and the type and size of test inputs for efficient detection of malicious software via machine learning. Our work targets software written in the Java programming language, though the techniques are general and applicable to other languages. We performed an extensive evaluation on freely available Java software projects, a flight collision avoidance system, and thousands of applications for the Android operating system. Evaluation results show to what extent dynamic symbolic execution is applicable in testing object-oriented software, they show correctness of the flight system on millions of automatically customized and generated test cases, and they show that simple and relatively small inputs in random testing can lead to effective malicious software detection