Related papers: Empirical Analysis on Productivity Prediction and …
In general, estimating software effort using a Use Case Point (UCP) size requires the use of productivity as a second prediction factor. However, there are three drawbacks to this approach: (1) there is no clear procedure for predicting…
Project productivity is a key factor for producing effort estimates from Use Case Points (UCP), especially when the historical dataset is absent. The first versions of UCP effort estimation models used a fixed number or very limited numbers…
Early software effort estimation is a hallmark of successful software project management. Building a reliable effort estimation model usually requires historical data. Unfortunately, since the information available at early stages of…
Context: Predicting software project effort from Use Case Points (UCP) method is increasingly used among researchers and practitioners. However, unlike other effort estimation domains, this area of interest has not been systematically…
Use Case Points (UCP) is a well-known method to estimate the project size, based on Use Case diagram, at early phases of software development. Although the Use Case diagram is widely accepted as a de-facto model for analyzing object…
Software estimation is a crucial task in software engineering. Software estimation encompasses cost, effort, schedule, and size. The importance of software estimation becomes critical in the early stages of the software life cycle when the…
Analysing and improving productivity has been one of the main goals of software engineering research since its beginnings. A plethora of studies has been conducted on various factors that resulted in several models for analysis and…
It is well recognized that the project productivity is a key driver in estimating software project effort from Use Case Point size metric at early software development stages. Although, there are few proposed models for predicting…
Clustering is a central primitive in unsupervised learning, yet practice is dominated by heuristics whose outputs can be unstable and highly sensitive to representations, hyperparameters, and initialisation. Existing theoretical results are…
We address the problem of uncertainty quantification (UQ) in the localization of a sound source within adverse acoustic environments. Estimating the position of the source is influenced by various factors, such as noise and reverberation,…
Managing software development productivity and effort are key issues in software organizations. Identifying the most relevant factors influencing project performance is essential for implementing business strategies by selecting and…
Performance variability is an important measure for a reliable high performance computing (HPC) system. Performance variability is affected by complicated interactions between numerous factors, such as CPU frequency, the number of…
Accurate prediction of application performance is critical for enabling effective scheduling and resource management in resource-constrained dynamic edge environments. However, achieving predictable performance in such environments remains…
Effort estimation is a key factor for software project success, defined as delivering software of agreed quality and functionality within schedule and budget. Traditionally, effort estimation has been used for planning and tracking project…
Detecting and localizing change points in sequential data is of interest in many areas of application. Various notions of change points have been proposed, such as changes in mean, variance, or the linear regression coefficient. In this…
Machine prediction algorithms (e.g., binary classifiers) often are adopted on the basis of claimed performance using classic metrics such as sensitivity and predictive value. However, classifier performance depends heavily upon the context…
The blast wave of machine learning and artificial intelligence has also reached the power systems community, and amid the frenzy of methods and black-box tools that have been left in its wake, it is sometimes difficult to perceive a glimmer…
In many domains, the previous decade was characterized by increasing data volumes and growing complexity of computational workloads, creating new demands for highly data-parallel computing in distributed systems. Effective operation of…
Pursuing invariant prediction from heterogeneous environments opens the door to learning causality in a purely data-driven way and has several applications in causal discovery and robust transfer learning. However, existing methods such as…
This study concentrates on clustering problems and aims to find compact clusters that are informative regarding the outcome variable. The main goal is partitioning data points so that observations in each cluster are similar and the outcome…