Related papers: Ignoring Time Dependence in Software Engineering D…
Determining the factors that have an influence on software systems development and deployment project outcomes has been the focus of extensive and ongoing research for more than 30 years. We provide here a survey of the research literature…
Time-to-event models are a popular tool to analyse data where the outcome variable is the time to the occurrence of a specific event of interest. Here we focus on the analysis of time-to-event outcomes that are either intrisically discrete…
Link prediction appears as a central problem of network science, as it calls for unfolding the mechanisms that govern the micro-dynamics of the network. In this work, we are interested in ego-networks, that is the mere information of…
Model composition plays a central role in many software engineering activities such as evolving models to add new features and reconciling conflicting design models developed in parallel by different development teams. As model composition…
This study aims to assist Company leaders and management team in providing more accurate time and cost estimations for future software projects. This paper focuses on analyzing the relationship between project complexity and cost and time…
Missing data is a fundamental challenge in data science, significantly hindering analysis and decision-making across a wide range of disciplines, including healthcare, bioinformatics, social science, e-commerce, and industrial monitoring.…
Empirical software engineering has received much attention in recent years and coined the shift from a more design-science-driven engineering discipline to an insight-oriented, and theory-centric one. Yet, we still face many challenges,…
This paper is concerned with a nonparametric regression problem in which the input variables and the errors are autocorrelated in time. The motivation for the research stems from modeling wind power curves. Using existing model selection…
Deep tabular models have demonstrated remarkable success on i.i.d. data, excelling in a variety of structured data tasks. However, their performance often deteriorates under temporal distribution shifts, where trends and periodic patterns…
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…
Emergent communication studies the development of language between autonomous agents, aiming to improve understanding of natural language evolution and increase communication efficiency. While temporal aspects of language have been…
User studies are paramount for advancing science. However, researchers face several barriers when performing them despite the existence of supporting tools. In this work, we study how existing tools and their features cope with previously…
A physical (e.g. astrophysical, geophysical, meteorological etc.) data may appear as an output of an experiment or it may contain some sociological, economic or biological information. Whatever be the source of a time series data some…
While learning with limited labelled data can improve performance when the labels are lacking, it is also sensitive to the effects of uncontrolled randomness introduced by so-called randomness factors (e.g., varying order of data). We…
It is impossible to separate the human factors from software engineering expertise during software development, because software is developed by people and for people. The intangible nature of software has made it a difficult product to…
Microservice architecture can be modeled as a network of microservices making calls to each other, commonly known as the service dependency graph. Network Science can provide methods to study such networks. In particular, temporal network…
Many recent studies use individual longitudinal data to analyze job search behaviors. Such data allow the use of fixed-effects models, which supposedly address the issue of dynamic selection and make it possible to identify the structural…
The decision-making process in real-world implementations has been affected by a growing reliance on data-driven models. We investigated the synergetic pattern between the data-driven methods, empirical domain knowledge, and…
In this short note, we discuss the circumstances that can lead to a failure to observe the design order of discretization error convergence in accuracy verification when solving a time-dependent problem. In particular, we discuss the…
Missing data are ubiquitous in the era of big data and, if inadequately handled, are known to lead to biased findings and have deleterious impact on data-driven decision makings. To mitigate its impact, many missing value imputation methods…