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The International Work-Conference on Artificial and Natural Neural Networks (IWANN) is a biennial meeting that seeks to provide a discussion forum for scientists, engineers, educators and students about the latest ideas and realizations in the foundations, theory, models and applications of hybrid systems inspired on nature (neural networks, fuzzy logic and evolutionary systems) as well as in emerging areas related to the above items. As in previous editions of IWANN, it also aims to create a friendly environment that could lead to the establishment or strengthening of scientific collaborations and exchanges among attendees. IWANN is included in the ranking of the best conferences established by the Computer Science Conference Ranking based on the "Estimated Impact of Conference (EIC,2009)", concretely in position 55 among 701 considered (in the Artificial Intelligence field), and in the rank B in Computing Research and Education Association (CORE).
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 to increase 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 and prone to errors process. Therefore, there is a need to improve testing so that costs can be cut, by automating most of the tasks, and the amount of detected errors can be increased, by using better techniques.
Intelligent systems are ubiquitous in our daily routine: smartphones, navigation systems, smartwatches, etc. In addition to be the basis of (more or less) sophisticated gadgets, these systems are fundamental in areas such as healthcare diagnostics and medical devices, traffic estimation, weather forecast, and many others. They are a clear case of complex systems. Therefore, these systems are difficult to design, implement, and test. In the case of testing, classical techniques cannot be used because intelligent systems have some peculiarities. On the 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 that it is extremely important that the solutions scale. Therefore, it is important that testing methodologies adapt to deal with these challenging systems, so that the number of errors can be reduced, avoiding recalls derived from wrong implementations.
During the last years, we are contemplating the emergence of new testing techniques based on the application of AI techniques such as evolutionary computation, artificial life, neural computation and swarm intelligence. Therefore, there is a feedback process between the fields: 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 intelligent systems.
The main aim of the Software Testing and Intelligent Systems special session is to contribute to the progress in the improvement and appropriate use of software testing and intelligent systems. We are interested in the adaption of existing testing approaches, as well as new ones, to test intelligent systems. In addition, we look forward to novel testing techniques based on computational intelligence paradigms. We are sure that the collaboration of researchers from different areas will result in benefits that can be applied in some of the research lines that are under the umbrella of the IWANN conference.
The topics of interest for this special session include, but not are limited to, the following:
- Heuristic techniques in software testing
- Formal approaches in intelligent systems
- Risk analysis of intelligent systems
- Swarm intelligence in software testing
- Monitoring of intelligent systems
- Case studies and applications
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