Embracing Artificial Intelligence (AI) for Network and Service

Dear Colleagues,

A big shift is occurring before our eyes thanks to the recent advancements in Artificial Intelligence (AI) and the power of generative models. Such models could have a significant impact on how we develop software services and in turn on the ability of software services themselves.

Several decades of research had previously reported that the development speed of a large software system is approximately constant, due to the limited gain achieved by adding more developers, known as the Self-Regulation Law or Lehman's Third Law of Software Evolution. Additionally, Brook's Law states that adding manpower to a late software project makes it later, as new members have to be trained therefore slowing down the existing team. The Self-Regulation Law expresses that large and complex software systems have a limited growth over time, and the ability to improve their behavior is also limited.

Nowadays, differently than what was done before, a team of developers can employ AI generative models to assist the analysis and changes in a large software system, mitigating the implications of the Self-Regulation Law. Software evolution could then be performed at a quicker rate and with more automation than ever before. As a result, the development of new versions of software services could quickly gain pace and accelerate the production of advanced services.

For more complex services to emerge, aside from their development, a further issue to address is the configuration management of the several constituting components that are often distributed across a network of hosts. The configuration, monitoring, and repair of software components, platforms, and services tend to be time-consuming given their ad hoc nature due to the great amount of possible combinations available. AI could be employed to finally achieve a greater level of autonomic computing, by analyzing a large amount of data related to configurations and monitoring.

Future advanced software systems could comprise a set of services that improve human life by analyzing data gathered from the monitoring of health and the natural environment, and then give insights and adjust eating habits, the use of natural resources, the state of a city infrastructure, pollution trends, climate changes, etc.

Dr. Emiliano Tramontana
Prof. Dr. Hironori Washizaki
Guest Editors

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• AI-powered software development tools and environments
• AI support for analyzing big data for health, natural resources, pollution, etc.
• AI and autonomic computing
• AI-supported network management
• AI-enabled virtualization environments
• AI and blockchain-based systems
• AI management of cloud services

Title: none
Authors: Daniela Constanza Quiñones Otey
Affiliation: Pontificia Universidad Católica de Valparaíso Valparaíso, Chile
Abstract: As voice assistants become more common in smartphones, smart speakers, and other devices, understanding and improving the user experience (UX) can have a significant impact on their use and effectiveness. UX evaluation can detect usability problems, identify user needs and preferences, and guide the design of more intuitive and effective voice interfaces. Heuristic evaluation is a widely used method for assessing usability and UX. This method is useful for evaluating UX with voice assistants, as it allows for the quick and cost-effective identification of problems. To achieve this, it is crucial to have a set of heuristics that can detect specific problems related to these types of intelligent assistants. However, traditional UX/usability heuristics may not directly apply to voice interfaces, which rely on spoken language and voice feedback. Therefore, we proposed HEUXIVA, a set of 13 Heuristics for Evaluating User eXperience with Voice Assistants. We used a formal 8-stage methodology to develop the heuristics. We validated and refined the heuristic proposal in two iterations, through heuristic evaluations, expert judgment, and user testing. Based on the results obtained, we concluded that the proposed set is effective as it allows for the detection of specific usability/UX problems related to voice assistants. Additionally, the heuristics were perceived as clear, useful, and easy to use by experts, mentioning that they help evaluate the features of voice assistants and facilitate the UX evaluation process.