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In a recent study, Reinforcement Learning (RL) used in combination with many-objective search, has been shown to outperform alternative techniques (random search and many-objective search) for online testing of Deep Neural Network-enabled systems. ...

Software aging, characterized by an increasing failure rate or performance decline in long-running software systems, poses significant risks including financial losses and potential harm to human life. This is primarily attributed to the ...

• We propose the SGT model for feature extraction from complex software code.
• A node degree-based sub-graph sampling retains core nodes, reducing complexity.
• Random oversampling increases ARB instances, improving model training ...

Evaluating the performance of Microservices Architectures (MSA) is essential to ensure their proper functioning and meet end-user satisfaction. For MSA performance analysts, one of the most challenging tasks is to determine the cause of any deviation of ...

Deep Neural Networks (DNN) are core components for classification and regression tasks of many software systems. Companies incur in high costs for testing DNN with datasets representative of the inputs expected in operation, as these need to be manually ...

The spread of the Internet of Things (IoT) involving an uncountable number of applications, combined with the rise of Machine Learning (ML), has enabled the rapid growth of pervasive and intelligent systems in a variety of domains, including ...

• Running AI and ML models on resource-constrained IoT devices.
• A new federated architecture specialized for the IoT scenario.
• On-board training by combining federated learning with transfer learning.
• Reduce latency, network and ...

Testing Autonomous Driving Systems (ADS) is essential for safe development of self-driving cars. For thorough and realistic testing, ADS are usually embedded in a simulator and tested in interaction with the simulated environment. However, their high ...

Microservice-based systems are usually developed according to agile practices like DevOps, which enables rapid and frequent releases to promptly react and adapt to changes. Monitoring is a key enabler for these systems, as they allow to ...

• We drew a comprehensive map from 71 monitoring tools for DevOps and Microservices.
• The features, assumptions, constraints, monitored info and techniques are analyzed.
• Results are discussed for DevOps engineers, researchers and tool ...

Microservice Architectures (MSA) are nowadays largely adopted by companies in several domains to provide on-demand services. The reliability of microservices is fundamental to avoid failures compromising the business functionalities. MSA ...

• Automatic combinatorial test case generation for functional and robustness testing.
• Service mesh infrastructure for monitoring of microservices.
• Causal inference engine for identifying causal relations in microservices failure ...

Context. Energy efficiency is gaining importance in the design of software systems, but is still marginally addressed in the area of microservice-based systems. Energy-related aspects often get neglected in favor of other software quality ...

This paper proposes a new evolutionary strategy - called Evolutionary Abduction (EVA) - designed to target a class of problems called Combinatorial Causal Optimization Problems (CCOP). In a CCOP, the goal is to find combinations of causes that best ...

With software systems becoming increasingly pervasive and autonomous, our ability to test for their quality is severely challenged. Many systems are called to operate in uncertain and highly-changing environment, not rarely required to make ...

Deep Neural Networks (DNN) are nowadays largely adopted in many application domains thanks to their human-like, or even superhuman, performance in specific tasks. However, due to unpredictable/unconsidered operating conditions, unexpected failures ...

Multi-access Edge Computing (MEC) is an emerging computing model that provides the necessary on-demand resources and services to the edge of the network, ensuring powerful computing, storage capacity, mobility, location, and context awareness support to ...

Real-time object detection systems are rapidly adopted in many edge computing systems for IoT applications. Since the computational resources on edge devices are often limited, continuous real-time object detection may suffer from the degradation of ...

Essential services in an Internet of Things (IoT)-based critical system should be continuously provided even when undesirable events like failures, attacks, and emergencies happen. In this work, we analyze the system’s ability to survive failures ...

Software aging – the phenomenon affecting many long-running systems, causing performance degradation or an increasing failure rate over mission time, and eventually leading to failure – is known to affect mobile devices and their ...

• Fine-grained and fast software (micro-)rejuvenation technique for the Android OS.

A powerful form of causal inference employed in many tasks, such as medical diagnosis, criminology, root cause analysis, biology, is abduction. Given an effect, it aims at generating a plausible and useful set of explanatory hypotheses for its causes. ...

Field testing refers to testing techniques that operate in the field to reveal those faults that escape in-house testing. Field testing techniques are becoming increasingly popular with the growing complexity of contemporary software systems. In this ...

Deep Neural Networks (DNN) are typically tested for accuracy relying on a set of unlabelled real world data (operational dataset), from which a subset is selected, manually labelled and used as test suite. This subset is required to be small (due to ...

This paper analyzes the phenomenon of software aging – namely, the gradual performance degradation and resource exhaustion in the long run – in the Android OS. The study intends to highlight if, and to what extent, devices from different vendors, ...