A literature review of using machine learning in software development life cycle stages
The software engineering community is rapidly adopting machine learning for transitioning
modern-day software towards highly intelligent and self-learning systems. However, the …
modern-day software towards highly intelligent and self-learning systems. However, the …
Software project management using machine learning technique—A Review
Project management planning and assessment are of great significance in project
performance activities. Without a realistic and logical plan, it isn't easy to handle project …
performance activities. Without a realistic and logical plan, it isn't easy to handle project …
Cognifying model-driven software engineering
The limited adoption of Model-Driven Software Engineering (MDSE) is due to a variety of
social and technical factors, which can be summarized in one: its (real or perceived) benefits …
social and technical factors, which can be summarized in one: its (real or perceived) benefits …
Machine learning for software engineering: A systematic mapping
Context: The software development industry is rapidly adopting machine learning for
transitioning modern day software systems towards highly intelligent and self-learning …
transitioning modern day software systems towards highly intelligent and self-learning …
Towards an evidence-based theoretical framework on factors influencing the software development productivity
WA Chapetta, GH Travassos - Empirical Software Engineering, 2020 - Springer
Context: Productivity refers to the rate at which a company produces goods, and its
observation takes into account the number of people and the amount of other necessary …
observation takes into account the number of people and the amount of other necessary …
Machine learning and deep learning in project analytics: methods, applications and research trends
Project analytics refers to applying analytical techniques and methods to past and present
data to gain insights into how the underlying project is performing. Machine learning (ML) …
data to gain insights into how the underlying project is performing. Machine learning (ML) …
Questions for data scientists in software engineering: a replication
In 2014, a Microsoft study investigated the sort of questions that data science applied to
software engineering should answer. This resulted in 145 questions that developers …
software engineering should answer. This resulted in 145 questions that developers …
Support vector regression for predicting the productivity of higher education graduate students from individually developed software projects
C López-Martín, RL Ulloa-Cazarez… - IET …, 2017 - Wiley Online Library
Productivity prediction of a software engineer is necessary to determine whether corrective
actions are needed and to identify improvement options to produce better results. It can be …
actions are needed and to identify improvement options to produce better results. It can be …
Supporting the Triaging Process in Software Development/eingereicht von Saad Shafiq
S Shafiq - 2022 - epub.jku.at
In software development, triaging deals with activities that are involved in the management
of work items, ie, items that need to be completed in a given iteration/sprint. These activities …
of work items, ie, items that need to be completed in a given iteration/sprint. These activities …
A Data-driven Productivity Assessment Framework for Collaborative Research Teams
H Wang, X Li, K Shen, T Wang, Y Chen - Proceedings of the 2021 5th …, 2021 - dl.acm.org
Team collaboration aims to generate higher collective productivity compared to the sum of
individuals. Propelled by modern communication technologies, collaboration has become …
individuals. Propelled by modern communication technologies, collaboration has become …