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Finding The Best Software Project Options by PDBO Algorithm for Improving Software Development Effort, Time and Quality

Published: 09 May 2016 Publication History

Abstract

There are many project changes (project options) that affect project estimates (effort, time and quality). The goal is to find the best project changes that optimize those project estimates. This problem was solved by some techniques including STAR (AI search tool), however, this technique didn't produce stable results, and case-based reasoning (CBR) which requires local dataset to predict the estimates. In this paper, problem data based optimization (PDBO) algorithm is adapted to optimize project estimates for different project goals without using past dataset and there are little/no options available about the software project. This paper also compares PDBO with intelligent water drops (IWD) and genetic algorithm (GA) in terms of stability, solution quality and processing time. The experiments are conducted on a software project with size 25KSLOC and the Jet Propulsion Laboratory (JPL) flight software project data. The results are stable for several runs of PDBO and are satisfactory.

References

[1]
Sweta Kumari, Shashank Pushkar, "Performance Analysis of the Software Cost Estimation Methods: A Review", International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 7, July 2013.
[2]
Sunita Devnani-Chulani, "bayesian analysis of software cost and quality models", a dissertation presented to the faculty of the graduate school university of southern California, May 1999.
[3]
Astha Dhiman, Chander Diwaker, "Optimization of COCOMO II Effort Estimation using Genetic Algorithm", American International Journal of Research in Science, Technology, Engineering & Mathematics.
[4]
Deepak Gupta, Vinay Kr.Goyal, Harish Mittal, "Comparative Study of Soft Computing Techniques for Software Quality Model", International Journal of Software Engineering Research & Practices Vol.1, Issue 1, Jan, 2011.
[5]
Tim Menzies, Oussama, Jairus Hihn, Barry Boehm, "Can We Build Software Faster and Better and Cheaper? ", ACM 2009, ISBN: 978-1-60558-634-2.
[6]
Adam Brady, Tim Menzies, "Case-Based Reasoning vs Parametric Models for Software Quality Optimization", PROMISE '10 Timisoara, Romania, Copyright 2010 ACM.
[7]
Sunita Chulani and Barry Boehm, "Modeling Software Defect Introduction and Removal: COQUALMO (Constructive Quality Model) ", USC - Center for Software Engineering, Los Angeles, CA 90089-0781, 1999.
[8]
"COCOMO II Model definition manual, version 1.4", University of Southern California.
[9]
"COCOMO II Model definition manual, version 2.1", 1995-2000 Center for Software Engineering, USC.
[10]
Sunita Chulani, "results of Delphi for the defects introduction model (sub-model of the cost/quality model extension to COCOMO II) ", Center for software engineering, 1997.
[11]
Sunita Chulani and Barry Boehm, "Modeling Software Defect Introduction and Removal: COQUALMO (Constructive Quality Model) ", USC - Center for Software Engineering, Los Angeles, CA 90089-0781, 1999.
[12]
Abdulelah G. Saif, Safia Abbas and Zaki Fayed, "The PDBO Algorithm for Discrete Time, Cost and Quality Trade -off in Software Projects with Expressing Quality by Defects", Proceedings of International Conference on Communication, Management and Information Technology (ICCMIT 2015).
[13]
Hamed Shah-Hosseini, "The intelligent water drops algorithm: a nature-inspired swarm-based optimization algorithm", Int. J. Bio-Inspired Computation, Vol. 1, Nos. 1/2, 2009.
[14]
Khaled El-Rayes, M.ASCE and Amr Kandil, A.M.ASCE, "Time-Cost-Quality Trade-Off Analysis for Highway Construction", Journal of Construction Engineering and Management, Vol. 131, No. 4, April 1, 2005.
[15]
B. Boehm, "Software Engineering Economics", Prentice Hall, 1981.
[16]
Z. Chen, T. Menzies, and D. Port, "Feature subset selection can improve software cost estimation", In PROMISE'05, 2005.
[17]
T. Menzies, Z. Chen, D. Port, and J. Hihn, " Simple software cost estimation: Safe or unsafe?", In Proceedings, PROMISE workshop, ICSE 2005, 2005.
[18]
T. Menies, K. Lum, and J. Hihn, " The deviance problem in effort estimation", In PROMISE, 2006, 2006.
[19]
Y. Jiang, B. Cukic, T. Menzies, and N. Bartlow, "Comparing design and code metrics for software quality prediction", In Proceedings of the PROMISE 2008 Workshop (ICSE), 2008.
[20]
T. Menzies, B. Turhan, A. Bener, G. Gay, B. Cukic, and Y. Jiang, "Implications of ceiling effects in defect predictors", In Proceedings of PROMISE 2008 Workshop (ICSE), 2008.

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  • (2020)Software development effort estimation: a systematic mapping studyIET Software10.1049/iet-sen.2018.5334Online publication date: 3-Mar-2020

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INFOS '16: Proceedings of the 10th International Conference on Informatics and Systems
May 2016
347 pages
ISBN:9781450340625
DOI:10.1145/2908446
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Published: 09 May 2016

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Author Tags

  1. COCOMO models
  2. metaheuristic algorithms
  3. software quality optimization

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  • (2020)Software development effort estimation: a systematic mapping studyIET Software10.1049/iet-sen.2018.5334Online publication date: 3-Mar-2020

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