Related papers: BayesFLo: Bayesian fault localization of complex s…
Background: Compilers are fundamental to software development, translating high-level source code into executable software systems. Faults in compilers can have severe consequences and thus effective localization and resolution of compiler…
Localizing the root cause of network faults is crucial to network operation and maintenance. However, due to the complicated network architectures and wireless environments, as well as limited labeled data, accurately localizing the true…
The software engineering field has a long history of classifying software failure causes. Understanding them is paramount for fault injection, focusing testing efforts or reliability prediction. Since software fails in manifold complex…
Fault localization is a practical research topic that helps developers identify code locations that might cause bugs in a program. Most existing fault localization techniques are designed for imperative programs (e.g., C and Java) and rely…
In the quest to improve efficiency, interdependence and complexity are becoming defining characteristics of modern complex networks representing engineered and natural systems. Graph theory is a widely used framework for modeling such…
Discovering a unique causal structure is difficult due to both inherent identifiability issues, and the consequences of finite data. As such, uncertainty over causal structures, such as those obtained from a Bayesian posterior, are often…
To fix a software bug, you must first find it. As software grows in size and complexity, finding bugs is becoming harder. To solve this problem, measures have been developed to rank lines of code according to their "suspiciousness" wrt…
Fault Localization (FL), in which a developer seeks to identify which part of the code is malfunctioning and needs to be fixed, is a recurring challenge in debugging. To reduce developer burden, many automated FL techniques have been…
In Spectrum-Based Fault Localization (SBFL), a suspiciousness score is assigned to each code element based on test coverage and test outcomes. The scores are then used to rank the code elements relative to each other in order to aid the…
The current hardware landscape and application scale is driving performance engineers towards writing bespoke optimizations. Verifying such optimizations, and generating minimal failing cases, is important for robustness in the face of…
Diagnosis in PV systems aims to detect, locate and identify faults. Diagnosing these faults is vital to guarantee energy production and extend the useful life of PV power plants. In the literature, multiple machine learning approaches have…
Federated Learning (FL) involves training a model over a dataset distributed among clients, with the constraint that each client's dataset is localized and possibly heterogeneous. In FL, small and noisy datasets are common, highlighting the…
Deep learning has enjoyed much recent success, and applying state-of-the-art model learning methods to controls is an exciting prospect. However, there is a strong reluctance to use these methods on safety-critical systems, which have…
Machine learning (ML) methods have recently emerged as an effective way to perform automated parameter tuning of databases. State-of-the-art approaches include Bayesian optimization (BO) and reinforcement learning (RL). In this work, we…
The Bayes factor, the data-based updating factor from prior to posterior odds, is a principled measure of relative evidence for two competing hypotheses. It is naturally suited to sequential data analysis in settings such as clinical trials…
Ensuring safe operation of safety-critical complex systems interacting with their environment poses significant challenges, particularly when the system's world model relies on machine learning algorithms to process the perception input. A…
This article introduces novel and practicable Bayesian factor analysis frameworks that are computationally feasible for moderate to large spatiotemporal data. Previous Bayesian analysis of spatiotemporal data has utilized a Bayesian factor…
The decreasing cost and improved sensor and monitoring system technology (e.g. fiber optics and strain gauges) have led to more measurements in close proximity to each other. When using such spatially dense measurement data in Bayesian…
The proliferation of heterogeneous configurations in distributed systems presents significant challenges in ensuring stability and efficiency. Misconfigurations, driven by complex parameter interdependencies, can lead to critical failures.…
Understanding of the pathophysiology of obstructive lung disease (OLD) is limited by available methods to examine the relationship between multi-omic molecular phenomena and clinical outcomes. Integrative factorization methods for…