Related papers: Walk the Talk: Is Your Log-based Software Reliabil…
When faults occur in microservice applications -- as they inevitably do -- developers depend on observability data to quickly identify and diagnose the issue. To collect such data, microservices need to be instrumented and the respective…
Today, software systems have a significant role in various domains among which are healthcare, entertainment, transport and logistics, and many more. It is only natural that with this increasing dependency on software, the number of…
Faithfulness hallucinations in VQA occur when vision-language models produce fluent yet visually ungrounded answers, severely undermining their reliability in safety-critical applications. Existing detection methods mainly fall into two…
Identity-based software signing tools aim to make software artifact provenance verifiable while reducing the operational burden of long-lived key management. However, there is limited cross-tool longitudinal evidence about which usability…
Formal specification languages have long languished, due to the grave scalability problems faced by complete verification methods. Runtime verification promises to use formal specifications to automate part of the more scalable art of…
Software reliability is an important quality attrib-ute, often evaluated as either a function of time or of system structures. The goal of this study is to have this metric cover both for component-based software, be-cause its reliability…
Context: Ensuring high levels of dependability in modern computer-based systems has become increasingly challenging due to their complexity. Although systems are validated at design time, their behavior can be different at runtime, possibly…
The momentum gained by microservices and cloud-native software architecture pushed nowadays enterprise IT towards multi-service applications. The proliferation of services and service interactions within applications, often consisting of…
Knowledge-based systems reason over some knowledge base. Hence, an important issue for such systems is how to acquire the knowledge needed for their inference. This paper assesses active learning methods for acquiring knowledge for "static…
Studying the reliability of complex systems using machine learning techniques involves facing a series of technical and practical challenges, ranging from the intrinsic nature of the system and data to the difficulties in modeling and…
This paper introduces a scalable Anomaly Detection Service with a generalizable API tailored for industrial time-series data, designed to assist Site Reliability Engineers (SREs) in managing cloud infrastructure. The service enables…
Explainability algorithms aimed at interpreting decision-making AI systems usually consider balancing two critical dimensions: 1) \textit{faithfulness}, where explanations accurately reflect the model's inference process. 2)…
Runtime verification is a lightweight verification technique that complements model checking by analyzing system executions at runtime rather than exploring a complete system model in advance. It is particularly useful for partially…
Developing autonomous driving systems (ADSs) involves generating and storing extensive log data from test drives, which is essential for verification, research, and simulation. However, these high-frequency logs, recorded over varying…
The detection of anomalies is essential mining task for the security and reliability in computer systems. Logs are a common and major data source for anomaly detection methods in almost every computer system. They collect a range of…
Log data are generated from logging statements in the source code, providing insights into the execution processes of software applications and systems. State-of-the-art log-based anomaly detection approaches typically leverage deep…
Fault diagnosis (FD) is essential for maintaining operational safety and minimizing economic losses by detecting system abnormalities. Recently, deep learning (DL)-driven FD methods have gained prominence, offering significant improvements…
The increasing volume of log data produced by software-intensive systems makes it impractical to analyze them manually. Many deep learning-based methods have been proposed for log-based anomaly detection. These methods face several…
Logging, the practice of inserting log statements into source code, is critical for improving software reliability. Recently, language model-based techniques have been developed to automate log statement generation based on input code.…
Serverless computing has redefined cloud application deployment by abstracting infrastructure and enabling on-demand, event-driven execution, thereby enhancing developer agility and scalability. However, maintaining consistent application…