Software Parallel Processing in Pervasive Computing

2011 ◽  
pp. 56-66
Author(s):  
Jitesh Dundas
Keyword(s):  
Project Management ◽  
Parallel Processing ◽  
Pervasive Computing ◽  
Propagation Speed ◽  
Periodic Wave ◽  
Software Project ◽  
Software System ◽  
Periodic Waves ◽  
Worst Case ◽  
Project Estimation

This chapter proposes the application of periodic wave concepts in management of software Parallel processing projects or processes. This chapter lays special emphasis on Runaway project, which create a lot of problems in Project Management for the stakeholders. This chapter proposes a new and dynamic way to control the software project estimation activity of Runaway project/processes, thereby reducing their future occurrences. This chapter also explains how the equations of unidirectional periodic Waves can be applied in software Parallel processing to measure quality and project execution in a dynamic way at any point in time. The concepts proposed here are dynamic unlike PERT/CPM and other metrics, which fail in worst-case scenarios. The Propagation Speed ‘C’ at any point in time of a stage or part of a software system executing in Parallel can be given by: C = H/ k (1). Where, H = length of the Wave (i.e. highest point), k = time taken in completing the stage.

scholarly journals Productivity Metric Estimation: Comprehensive Examine of Efficiency, Effectiveness and Value Based Metrics

2019 ◽  
Vol 9 (2) ◽  
pp. 5042-5049
Keyword(s):  
Project Management ◽  
Dynamic Environments ◽  
Software Project ◽  
Successful Development ◽  
Management Level ◽  
Dynamic Framework ◽  
Business Applications ◽  
It Performance ◽  
Project Estimation ◽  
And Performance

The successful software project estimation is critically dependent upon measurement of productivity. The overall goal of productivity metric is to improve the ability and performance of information system evaluation as well as maintenance, moreover determine the performance of the information technology (IT) performance in business applications. The activities corresponding to productivity metrics vary at distinct management levels. The activities used within project management leads to dynamic framework that could be opted in changing or dynamic environments. At the project management level, productivities activities concentrates on effectiveness. For instance, during estimation of effort required to reproduce a system, assumption must be manufacture regarding the ability that could lead to successful development of software project and this is fetched from past projects. This paper focuses on identifying the productivity metrics that are essential and appropriate for project management process.

scholarly journals Estimating Software Development Efforts Using a Random Forest-Based Stacked Ensemble Approach

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1195
Author(s):  
Priya Varshini A G ◽  
Anitha Kumari K ◽  
Vijayakumar Varadarajan
Keyword(s):  
Deep Learning ◽  
Random Forest ◽  
Software Development ◽  
Weighted Averaging ◽  
Software Project ◽  
Effort Estimation ◽  
Software Effort Estimation ◽  
Single Model ◽  
Ensemble Techniques ◽  
Project Estimation

Software Project Estimation is a challenging and important activity in developing software projects. Software Project Estimation includes Software Time Estimation, Software Resource Estimation, Software Cost Estimation, and Software Effort Estimation. Software Effort Estimation focuses on predicting the number of hours of work (effort in terms of person-hours or person-months) required to develop or maintain a software application. It is difficult to forecast effort during the initial stages of software development. Various machine learning and deep learning models have been developed to predict the effort estimation. In this paper, single model approaches and ensemble approaches were considered for estimation. Ensemble techniques are the combination of several single models. Ensemble techniques considered for estimation were averaging, weighted averaging, bagging, boosting, and stacking. Various stacking models considered and evaluated were stacking using a generalized linear model, stacking using decision tree, stacking using a support vector machine, and stacking using random forest. Datasets considered for estimation were Albrecht, China, Desharnais, Kemerer, Kitchenham, Maxwell, and Cocomo81. Evaluation measures used were mean absolute error, root mean squared error, and R-squared. The results proved that the proposed stacking using random forest provides the best results compared with single model approaches using the machine or deep learning algorithms and other ensemble techniques.

SOFTWARE PROJECT MANAGEMENT FOR DEVELOPING COUNTRIES

2005 ◽  
Vol 38 (1) ◽  
pp. 35-40
Author(s):  
Christopher Peterson ◽  
Zenon Chaczko ◽  
Craig Scott ◽  
David Davis
Keyword(s):  
Developing Countries ◽  
Project Management ◽  
Software Project ◽  
Software Project Management

Panel Discussion: Software Project Management

1986 ◽  
Vol 19 (6) ◽  
pp. 13-14
Author(s):  
E. Knuth ◽  
B.T. Cronhjort ◽  
H. Hubmer ◽  
G. Kovacs ◽  
L. Krzanik
Keyword(s):  
Project Management ◽  
Panel Discussion ◽  
Software Project ◽  
Software Project Management
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