Background

The aim of this special issue is to compile relevant advances in the application and use of machine learning and evolutionary computation to improve software testing.

Current software systems are increasingly complex and therefore, it is more difficult and costly to ensure that they do what they are supposed to do. Software testing plays a key role in increasing the confidence on the correctness of systems. Despite the huge amount of resources devoted to testing (up to 60% of the budget), testing is still mainly a manual process that is prone to errors process. Therefore, there is a need to improve testing by automating most of the tasks so that costs can be cut and using better techniques to increase the ability to detect errors.

Intelligent systems are ubiquitous in our daily routine (smartphones, navigation systems, smartwatches, etc.). In addition to being the basis of sophisticated gadgets, these systems are fundamental in areas such as healthcare diagnostics and medical devices, traffic estimation, and weather forecast. They are a clear case of complex systems and therefore, they are difficult to design, implement, and test. In the case of testing, classical techniques cannot be used because intelligent systems have some peculiarities. On one hand, they are usually governed by non-deterministic algorithms using advance AI techniques where classical testing will struggle. On the other hand, they have to analyze huge amounts of data in real-time, so it is extremely important that the solutions are scalable. Therefore, it is important that testing methodologies adapt to these challenging systems so that the number of errors can be reduced, avoiding recalls derived from wrong implementations.

During the last years, we have contemplated the emergence of new testing techniques based on the application of machine learning and evolutionary computation techniques. The reliability of intelligent systems is improved thanks to good software testing methodologies, and software testing is improved thanks to knowledge obtained from the techniques used to develop these systems. The main aim of this special issue is to contribute to the progress in the improvement and appropriate use of machine learning and evolutionary computation techniques in software testing. We are interested in the adaption of existing testing approaches, but we also look forward to novel testing techniques. In particular, surveys on a specific line of research within the broad scope of the special issue are welcomed.

Topics of Interest

Researchers and practitioners may submit their work on the application of machine learning and evolutionary computation to software testing. The topics of interest for this special issue include, but are not limited to, the following:

Submission

We are soliciting original contributions that have not been published and are not currently under consideration elsewhere. Both theoretical studies and state-of-the-art practical applications are welcome. All submitted papers will be peer-reviewed and selected on the basis of their quality and relevance to the theme of this special section.

We also encourage extensions of conference papers, unless prohibited by copyright, if there is a significant difference in the technical content. Improvements such as adding a new case study or including a description of additional related studies do not satisfy this requirement. A description explaining the difference between the conference paper and the journal submission is required. The overlap between each submission and other articles, including the authors’ own papers and dissertations, should be less than 30%. Each submission must conform to the IJPE template. Please click here to submit your paper.

Special Attention

Important Dates

Guest Editors

About the Guest Editors

Professor Manuel Núñez is a Professor of Computer Science with the Complutense University of Madrid, Spain. He received a Ph.D. in Mathematics and an M.S. in Economics. He belongs to the IEEE SMC Technical Committee on Computational Collective Intelligence and is the regional editor for Europe of the International Journal of Performability Engineering. He is a member of several Editorial Boards, including Software Testing, Verification & Reliability, and he regularly serves on Program Committees of international events, including recent conferences on testing such as ICTSS´20, ICST´21, and QRS´21. His main line of research considers formal methods for testing complex systems. He also works on the use of formal methods in user modelling, with an emphasis on collective intelligence. He has published more than 160 papers.

Professor Juan Boubeta-Puig is an Associate Professor with the Department of Computer Science and Engineering at the University of Cádiz (UCA), Spain. He received his Ph.D. in Computer Science from UCA in 2014 and was honored with the Extraordinary Ph.D. Award from UCA and the Best Ph.D. Thesis Award from the Spanish Society of Software Engineering and Software Development Technologies (SISTEDES). He has more than 80 publications in national and international journals, conferences and workshops. His research interests include real-time big data analytics through Complex Event Processing (CEP), Event-Driven Service-Oriented Architecture (SOA 2.0), Internet of Things (IoT) and Model-Driven Development (MDD) of advanced user interfaces, and their application to e-health, smart city, industry 4.0 and cybersecurity.

Professor Gregorio Díaz is an Associate Professor at the University of Castilla-La Mancha, Spain, within the ReTiCS research group. His research interests aim to make software more reliable, secure, and easier to design. He has published more than 60 papers in ranked journals and international conferences. He has taught in undergraduate and postgraduate studies awarded with the quality award Euro-Inf Bachelor by EQANIE.