Effort estimation is an essential task in a software project as it helps to establish feasible plans for the implementation of a project. It largely influences success or failure of the project. Project planning becomes more efficient with accurate effort estimates, thus providing a number of benefits to the organization. Estimating effort in agile projects has been a challenging task for researchers. Several studies exist in that domain. While some have considered people-related factors, others have catered for project-related factors for estimating effort. Others have adopted machine learning (ML) techniques to produce an accurate estimation. This paper presents a model to estimate and predict effort in a sprint using ML techniques while considering various factors that affect a sprint. The model has been evaluated using various regression algorithms, namely linear regression, K-nearest neighbor, decision tree, polynomial kernel, radius basis function and multi-layer perception (MLP). The model has produced more reliable estimates, with low error values using MLP algorithm.

努力估算是软件项目中的一项基本任务，因为它有助于为项目的实施建立可行的计划。它在很大程度上影响项目的成败。通过准确的工作量估算，项目计划将变得更加高效，从而为组织带来许多好处。评估敏捷项目的工作量对于研究人员而言是一项艰巨的任务。在该领域中存在一些研究。尽管有些人考虑了与人有关的因素，但另一些人则考虑了与项目有关的因素以估算工作量。其他人则采用机器学习（ML）技术来产生准确的估计。本文提出了一种模型，该模型使用ML技术估算和预测冲刺的工作量，同时考虑了影响冲刺的各种因素。该模型已使用各种回归算法进行了评估，即线性回归，K近邻，决策树，多项式核，半径基函数和多层感知（MLP）。该模型使用MLP算法产生了更可靠的估计值，并且误差值低。