A Machine Learning Approach for Estimating Overtime Allocation in Software Development Projects

Overtime planning in software projects has traditionally been approached with search-based multi-objective optimization algorithms. However, the explicit solutions produced by these algorithms often lack applicability and acceptance in the software industry due to their disregard for project managers\u27 intuitive knowledge. This study presents a machine learning model that learns the preferred overtime allocation patterns from solutions annotated by project managers and applied to four publicly available software development projects. The model was trained using 1092 instances of annotated solutions gathered from software houses, and the Random Forest Regression (RFR) algorithm was used to estimate the PMs’ preference. The evaluation results using MAE, RMSE, and R2 revealed that RFR exhibits excellent predictive power in this domain with minimal error. RFR also outperformed the baseline regression models in all the performance measures. The proposed machine learning approach provides a reliable and effective tool for estimating project managers\u27 preferences for overtime plans

Heuristic Method of Safe Manoeuvre Selection Based on Collision Threat Parameters Areas

This paper is a continuation of papers dedicated to a radar-based CTPA (Collision Threat Parameters Area) display designed to support safe manoeuvre selection. The display visualizes all the ships in an encounter and presents situational overview from the own ship's point of view. It calculates and displays information on unsafe or unrealistic own ship's course & speed allowing a user to select a safe manoeuvre. So far only the manual selection was possible, thus the paper aims at presenting a heuristic approach towards the manoeuvre selection when using the display

Evolutionary Ship Track Planning within Traffic Separation Schemes – Evaluation of Individuals

The paper presents an extended version of the author’s Evolutionary Sets of Safe Ship Trajectories method. The method plans safe tracks of all ships involved in an encounter including speed reduction maneuvers, if necessary, and taking into account Rule 10 of COLREGS, which specifies ships’ behavior within Traffic Separation Schemes governed by IMO. The paper focuses on the evaluation phase of the evolutionary process and shows how fitness function is designed to compare various possible tracks as well as to assess the quality of a final solution. The impact of the fitness function on the method’s results is illustrated by examples

Fuzzy Collision Threat Parameters Area (FCTPA) – A New Display Proposal

The paper introduces a visualization method that enables the navigator to estimate an encounter situation and choose collision avoidance manoeuvre if necessary. It is based on the CTPA method and offers new features: fuzzy sectors of forbidden speed and course values and the possibility to use any given ship domain. The method is fast enough to be applied in the real-time decision-support system