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Transforming the construction sector is key to reaching net-zero, and many stakeholders expect its decarbonization through digitalization. But no quantified evidence has been brought to date. We propose the first environmental…
It is well known that the software process impacts the quality of the resulting product. There are also anecdotal claims that agile processes result in higher level of quality than traditional methodologies. However, still solid evidence of…
Computational models are powerful tools for understanding human cognition and behavior. They let us express our theories clearly and precisely, and offer predictions that can be subtle and often counter-intuitive. However, this same…
The growing adoption of Industrial Internet of Things (IIoT) technologies enables automated, real-time collection of manufacturing process data, unlocking new opportunities for data-driven product development. Current data-driven methods…
Assessing and predicting the complex concept of software quality is still challenging in practice as well as research. Activity-based quality models break down this complex con- cept into more concrete definitions, more precisely facts…
Bayesian optimal experimental design is a sub-field of statistics focused on developing methods to make efficient use of experimental resources. Any potential design is evaluated in terms of a utility function, such as the (theoretically…
Quantifying prediction uncertainty when applying object detection models to new, unlabeled datasets is critical in applied machine learning. This study introduces an approach to estimate the performance of deep learning-based object…
Consideration set formation using non-compensatory screening rules is a vital component of real purchasing decisions with decades of experimental validation. Marketers have recently developed statistical methods that can estimate…
Combining machine learning with econometric analysis is becoming increasingly prevalent in both research and practice. A common empirical strategy involves the application of predictive modeling techniques to 'mine' variables of interest…
Decision-making is a key software engineering skill. Developers constantly make choices throughout the software development process, from requirements to implementation. While prior work has studied developer decision-making, the choices…
Quality attributes and constraints are among the main drivers of architectural decision making. The quality attributes are improved or damaged by the architectural decisions, while restrictions directly include or exclude parts of the…
Managing requirements on quality aspects is an important issue in the development of software systems. Difficulties arise from expressing them appropriately what in turn results from the difficulty of the concept of quality itself. Building…
Model-driven engineering (MDE) is believed to have a significant impact in software quality. However, researchers and practitioners may have a hard time locating consolidated evidence on this impact, as the available information is…
The surging demand for new energy vehicles is driven by the imperative to conserve energy, reduce emissions, and enhance the ecological ambiance. By conducting behavioral analysis and mining usage patterns of new energy vehicles, particular…
Design space exploration is an important but costly step involved in the design/deployment of custom architectures to squeeze out maximum possible performance and energy efficiency. Conventionally, optimizations require iterative sampling…
Bayesian optimal experiments that maximize the information gained from collected data are critical to efficiently identify behavioral models. We extend a seminal method for designing Bayesian optimal experiments by introducing two…
This paper addresses the problem of evaluating the quality of finite element meshes for the purpose of structural mechanic simulations. It proposes the application of a machine learning model trained on data collected from expert…
Contemporary intelligent systems incorporate software components, including machine learning components. As they grow in complexity and data volume such machine learning systems face unique quality challenges like scalability and…