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We present and evaluate Spectrum-Based Log Diagnosis (SBLD), a method to help developers quickly diagnose problems found in complex integration and deployment runs. Inspired by Spectrum-Based Fault Localization, SBLD leverages the…

Vibration-based condition monitoring techniques are commonly used to identify faults in rolling element bearings. Accuracy and speed of fault detection procedures are critical performance measures in condition monitoring. Delay is…

Unit tests are critical in the hardware design lifecycle to ensure that component design modules are functionally correct and conform to the specification before they are integrated at the system level. Thus developing unit tests targeting…

Automotive engineering makes extensive use of numerical simulation throughout the design process. The development of numerical models, their validation against experimental tests, and their updating during vehicle and engine projects…

The working conditions of large-scale industrial systems are very complex. Once a failure occurs, it will affect industrial production, cause property damage, and even endanger the workers' lives. Therefore, it is important to control the…

Debugging Cyber-Physical System (CPS) models can be extremely complex. Indeed, only the detection of a failure is insuffcient to know how to correct a faulty model. Faults can propagate in time and in space producing observable…

Detecting faults in manufacturing applications can be difficult, especially if each fault model is to be engineered by hand. Data-driven approaches, using Machine Learning (ML) for detecting faults have recently gained increasing interest,…

Maintaining software artifacts is among the hardest tasks an engineer faces. Like any other piece of code, model transformations developed by engineers are also subject to maintenance. To facilitate the comprehension of programs, software…

Large language models (LLMs) are trained through multi-stage pipelines over heterogeneous data sources, yet developers lack a principled way to pinpoint the specific data responsible for an observed behavior. This lack of observability…

Although an ever-growing number of applications employ deep learning based systems for prediction, decision-making, or state estimation, almost no certification processes have been established that would allow such systems to be deployed in…

Determining whether a configurable software system has a performance bug or it was misconfigured is often challenging. While there are numerous debugging techniques that can support developers in this task, there is limited empirical…

Deep learning (DL) models have achieved paradigm-changing performance in many fields with high dimensional data, such as images, audio, and text. However, the black-box nature of deep neural networks is a barrier not just to adoption in…

Embedded systems in safety-critical environments are continuously required to deliver more performance and functionality, while expected to provide verified safety guarantees. Nonetheless, platform-wide software verification (required for…

In recent years, more vulnerabilities have been discovered every day, while manual vulnerability repair requires specialized knowledge and is time-consuming. As a result, many detected or even published vulnerabilities remain unpatched,…

Model-based diagnostic reasoning often leads to a large number of diagnostic hypotheses. The set of diagnoses can be reduced by taking into account extra observations (passive monitoring), measuring additional variables (probing) or…

Nowadays, we are witnessing an increasing demand in both corporates and academia for exploiting Deep Learning (DL) to solve complex real-world problems. A DL program encodes the network structure of a desirable DL model and the process by…

Vibration-based condition monitoring techniques are commonly used to detect and diagnose failures of rolling bearings. Accuracy and delay in detecting and diagnosing different types of failures are the main performance measures in condition…

Despite being one of the most basic tasks in software development, debugging is still performed in a mostly manual way, leading to high cost and low performance. To address this problem, researchers have studied promising approaches, such…

This paper introduces a robust, learning-based method for diagnosing the state of distribution network switchgear, which is crucial for maintaining the power quality for end users. Traditional diagnostic models often rely heavily on expert…

This paper presents LLM4SecHW, a novel framework for hardware debugging that leverages domain specific Large Language Model (LLM). Despite the success of LLMs in automating various software development tasks, their application in the…