A. van Deursen
268 records found
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Transformer-based language models are highly effective for code completion, with much research dedicated to enhancing the content of these completions. Despite their effectiveness, these models come with high operational costs and can be intrusive, especially when they suggest to
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Effective change management is crucial for businesses heavily reliant on software and services to minimise incidents induced by changes. Unfortunately, in practice it is often difficult to effectively use artificial intelligence for IT Operations (AIOps) to enhance service manage
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Enhancing Incident Management
Insights from a Case Study at ING
An incident management process is necessary in businesses that depend strongly on software and services. A proper process is essential to guarantee that incidents are well-handled, especially in a financial software-defined business needing to adhere to guidelines and regulations
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Anomaly detection techniques are essential in automating the monitoring of IT systems and operations. These techniques imply that machine learning algorithms are trained on operational data corresponding to a specific period of time and that they are continuously evaluated on new
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Sprint planning is essential for the successful execution of agile software projects. While various prioritization criteria influence the selection of user stories for sprint planning, their relative importance remains largely unexplored, especially across different project conte
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Data vs. Model Machine Learning Fairness Testing
An Empirical Study
Although several fairness definitions and bias mitigation techniques exist in the literature, all existing solutions evaluate fairness of Machine Learning (ML) systems after the training stage. In this paper, we take the first steps towards evaluating a more holistic approach by
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Kotlin language has recently become prominent for developing both Android and server-side applications. These programs are typically designed to be fast and responsive, with asynchrony and concurrency at their core. To enable developers to write asynchronous and concurrent code s
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While the concept of large-scale stream processing is very popular nowadays, efficient dynamic allocation of resources is still an open issue in the area. The database research community has yet to evaluate different autoscaling techniques for stream processing engines under a ro
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Counterfactual explanations offer an intuitive and straightforward way to explain black-box models and offer algorithmic recourse to individuals. To address the need for plausible explanations, existing work has primarily relied on surrogate models to learn how the input data is
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Stream processing in the last decade has seen broad adoption in both commercial and research settings. One key element for this success is the ability of modern stream processors to handle failures while ensuring exactly-once processing guarantees. At the moment of writing, virtu
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AIOps solutions enable faster discovery of failures in operational large-scale systems through machine learning models trained on operation data. These models become outdated during the occurrence of concept drift, a term used to describe shifts in data distributions. In operatio
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Code comments are a key resource for information about software artefacts. Depending on the use case, only some types of comments are useful. Thus, automatic approaches to clas-sify these comments have been proposed. In this work, we address this need by proposing, STACC, a set o
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Modern agile software projects are subject to constant change, making it essential to re-asses overall delay risk throughout the project life cycle. Existing effort estimation models are static and not able to incorporate changes occurring during project execution. In this paper,
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Uncovering Energy-Efficient Practices in Deep Learning Training
Preliminary Steps Towards Green AI
Modern AI practices all strive towards the same goal: better results. In the context of deep learning, the term "results"often refers to the achieved accuracy on a competitive problem set. In this paper, we adopt an idea from the emerging field of Green AI to consider energy cons
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Binary reverse engineering is used to understand and analyse programs for which the source code is unavailable. Decompilers can help, transforming opaque binaries into a more readable source code-like representation. Still, reverse engineering is difficult and costly, involving c
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Existing work on Counterfactual Explanations (CE) and Algorithmic Recourse (AR) has largely been limited to the static setting and focused on single individuals: given some estimated model, the goal is to find valid counterfactuals for an individual instance that fulfill various
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Previous work has shown that Large Language Models are susceptible to so-called data extraction attacks. This allows an attacker to extract a sample that was contained in the training data, which has massive privacy implications. The construction of data extraction attacks is cha
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Search-based approaches have been used in the literature to automate the process of creating unit test cases. However, related work has shown that generated tests with high code coverage could be ineffective, i.e., they may not detect all faults or kill all injected mutants. In t
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Getting Things Done
The Eelco Way
Eelco Visser (1966–2022) was a leading member of the department of Software Technology (ST) of the faculty of Electrical Engineering Mathematics, and Computer Science (EEMCS) of Delft University of Technology. He had a profound influence on the educational programs in computer sc
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We present CounterfactualExplanations.jl: a package for generating Counterfactual Explanations (CE) and Algorithmic Recourse (AR) for black-box models in Julia. CE explain how inputs into a model need to change to yield specific model predictions. Explanations that involve realis
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