相关论文: Flexible In-The-Field Monitoring

When interacting with their software systems, users may have to deal with problems like crashes, failures, and program instability. Faulty software running in the field is not only the consequence of ineffective in-house verification and…

Monitoring techniques can extract accurate data about the behavior of software systems. When used in the field, they can reveal how applications behave in real-world contexts and how programs are actually exercised by their users.…

Collecting data about the sequences of function calls executed by an application while running in the field can be useful to a number of applications, including failure reproduction, profiling, and debugging. Unfortunately, collecting data…

Field data is an invaluable source of information for testers and developers because it witnesses how software systems operate in real environments, capturing scenarios and configurations relevant to end-users. Unfortunately, collecting…

Collecting traces from software running in the field is both useful and challenging. Traces may indeed help revealing unexpected usage scenarios, detecting and reproducing failures, and building behavioral models that reflect how the…

Field failures, that is, failures caused by faults that escape the testing phase leading to failures in the field, are unavoidable. Improving verification and validation activities before deployment can identify and timely remove many but…

In a heterogeneous, dynamic environment, like Grid, post-mortem analysis is of no use and data needs to be collected and analysed in real time. Novel techniques are also required for dynamically tuning the application's performance and…

Wind farms are an indispensable driver toward renewable and nonpolluting energy resources. However, as ideal sites are limited, placement in remote and challenging locations results in higher logistics costs and lower average wind speeds.…

The thesis advances the field of software security by providing knowledge and automation support for software vulnerability assessment using data-driven approaches. Software vulnerability assessment provides important and multifaceted…

Modern software development and operations rely on monitoring to understand how systems behave in production. The data provided by application logs and runtime environment are essential to detect and diagnose undesired behavior and improve…

Autonomous vehicles require reliable hazard detection. However, primary sensor systems may miss near-field obstacles, resulting in safety risks. Although a dedicated fast-reacting near-field monitoring system can mitigate this, it typically…

Data collection and analysis in the field is critical for operations in domains such as environmental science and public safety. However, field workers currently face data- and platform-oriented issues in efficient data collection and…

Today's software systems like cyber-physical production systems or big data systems have to process large volumes and diverse types of data which heavily influences the quality of these so-called data-intensive systems. However, traditional…

The software is often produced under significant time constraints. Our idea is to understand the effects of various software development practices on the performance of developers working in stressful environments, and identify the best…

As software systems increase in complexity, conventional monitoring methods struggle to provide a comprehensive overview or identify performance issues, often missing unexpected problems. Observability, however, offers a holistic approach,…

Monitoring software systems at runtime is key for understanding workloads, debugging, and self-adaptation. It typically involves collecting and storing observable software data, which can be analyzed online or offline. Despite the…

More and more distributed software systems are being developed and deployed today. Like other software, distributed software systems also need very strong quality assurance support. Distributed software is often very large/complex, has…

Efficient data collection is essential in applied studies where frequent measurements are costly, time-consuming, or burdensome. This challenge is especially pronounced in functional data settings, where each subject is observed at only a…

As the world becomes increasingly dependent on technology, researchers in both industry and academia endeavor to understand how technology is used, the impact it has on everyday life, the artifact life-cycle and overall integrations of…

Existing network simulations often rely on simplistic models that send packets at random intervals, failing to capture the critical role of application-level behaviour. This paper presents a statistical approach that extracts and models…