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Evidence-based decision-making entails collecting (costly) observations about an underlying phenomenon of interest, and subsequently committing to an (informed) decision on the basis of accumulated evidence. In this setting, active sensing…
Many software engineering tasks, such as testing, and anomaly detection can benefit from the ability to infer a behavioral model of the software.Most existing inference approaches assume access to code to collect execution sequences. In…
Changes, planned or unexpected, are common during the execution of real-life processes. Detecting these changes is a must for optimizing the performance of organizations running such processes. Most of the algorithms present in the…
Today's distributed tracing frameworks are ill-equipped to troubleshoot rare edge-case requests. The crux of the problem is a trade-off between specificity and overhead. On the one hand, frameworks can indiscriminately select requests to…
Modern software systems become too complex to be tested and validated. Detecting software partial failures in complex systems at runtime assist to handle software unintended behaviors, avoiding catastrophic software failures and improving…
We present Dyve, a dynamic process verifier that enhances reasoning error detection in large language models by integrating fast and slow thinking, inspired by Kahneman's Systems Theory. Dyve adaptively applies immediate token-level…
This paper presents a practical solution for detecting data races in parallel programs. The solution consists of a combination of execution replay (RecPlay) with automatic on-the-fly data race detection. This combination enables us to…
Data leakage is a well-known problem in machine learning. Data leakage occurs when information from outside the training dataset is used to create a model. This phenomenon renders a model excessively optimistic or even useless in the real…
Data-flow testing (DFT) aims to detect potential data interaction anomalies by focusing on the points at which variables receive values and the points at which these values are used. Such test objectives are referred as \emph{def-use…
In the modern world, we are permanently using, leveraging, interacting with, and relying upon systems of ever higher sophistication, ranging from our cars, recommender systems in e-commerce, and networks when we go online, to integrated…
Performance antipatterns document bad design patterns that have negative influence on system performance. In our previous work we formalized such antipatterns as logical predicates that predicate on four views: (i) the static view that…
Cut-in maneuvers in high-speed traffic pose critical challenges that can lead to abrupt braking and collisions, necessitating safe and efficient lane change strategies. We propose a Dynamic Bayesian Network (DBN) framework to integrate…
Evaluating Software testability can assist software managers in optimizing testing budgets and identifying opportunities for refactoring. In this paper, we abandon the traditional approach of pursuing testability measurements based on the…
Reliability and real-time responsiveness in safety-critical systems have traditionally been achieved using error detection mechanisms, such as LockStep, which require pre-configured checker cores,strict synchronisation between main and…
Dynamic neural networks (DyNNs) have become viable techniques to enable intelligence on resource-constrained edge devices while maintaining computational efficiency. In many cases, the implementation of DyNNs can be sub-optimal due to its…
An efficient entailment proof system is essential to compositional verification using separation logic. Unfortunately, existing decision procedures are either inexpressive or inefficient. For example, Smallfoot is an efficient procedure but…
Diagnosing and fixing performance problems on multicore machines with deep memory hierarchies is extremely challenging. Certain problems are best addressed when we can analyze the entire trace of program execution, e.g., every memory…
Dynamic race detection based on the happens before (HB) partial order has now become the de facto approach to quickly identify data races in multi-threaded software. Most practical implementations for detecting these races use timestamps to…
Evidence-based Prescriptive Analytics (EbPA) is necessary to determine optimal operational set-points that will improve business productivity. EbPA results from what-if analysis and counterfactual experimentation on CAUSAL Digital Twins…
Without quantitative data, deciding whether and how to use static analysis in a development workflow is a matter of expert opinion and guesswork rather than an engineering trade-off. Moreover, relevant data collected under real-world…