Fuzzy Emotional COCOMO II Software Cost Estimation (FECSCE) using Multi-Agent Systems

Software development cost estimation is important for effective project management. Many models have been introduced to predict software development cost. In this paper, a novel emotional COnstructive COst MOdel II (COCOMO II) has been proposed for software cost estimation. In COCOMO II only the project characteristics are considered, whereas the characteristics of team members are also important factors. This paper presents a model, namely FECSCE, which in addition to project characteristics considers the communication skills, personality, mood and capabilities of team members. In FECSCE, we have used a Multi-Agent System (MAS) in order to simulate team communications.

[1]  M. Sugeno,et al.  Structure identification of fuzzy model , 1988 .

[2]  Chang-Shing Lee,et al.  A Novel Fuzzy CMMI Ontology and Its Application to Project Estimation , 2008 .

[3]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[4]  Arantza Aldea,et al.  Emotions in human and artificial intelligence , 2005, Comput. Hum. Behav..

[5]  Andrew Ortony,et al.  The Cognitive Structure of Emotions , 1988 .

[6]  N. Ghasem-Aghaee,et al.  Towards Fuzzy Agents with Dynamic Personality for Human Behavior Simulation , 2003 .

[7]  R. Dillibabu,et al.  Cost estimation of a software product using COCOMO II.2000 model -- a case study , 2005 .

[8]  Zeeshan Muzaffar,et al.  Handling imprecision and uncertainty in software development effort prediction: A type-2 fuzzy logic based framework , 2009, Inf. Softw. Technol..

[9]  René Bañares-Alcántara,et al.  Simulation of work teams using a multi-agent system , 2003, AAMAS '03.

[10]  Chang-Shing Lee,et al.  Ontology-based Intelligent Decision Support Agent for CMMI Project Monitoring and Control , 2006, NAFIPS 2006 - 2006 Annual Meeting of the North American Fuzzy Information Processing Society.

[11]  Chang-Shing Lee,et al.  Ontology-based Intelligent Decision Support Agent for CMMI Project Monitoring and Control , 2006, NAFIPS 2006.

[12]  Nadia Magnenat-Thalmann,et al.  Generic personality and emotion simulation for conversational agents , 2004, Comput. Animat. Virtual Worlds.

[13]  A. Damasio.  Descartes' error: emotion, reason, and the human brain. avon books , 1994 .

[14]  Taghi M. Khoshgoftaar,et al.  Identification of fuzzy models of software cost estimation , 2004, Fuzzy Sets Syst..

[15]  Doo-Hwan Bae,et al.  Dynamic project performance estimation by combining static estimation models with system dynamics , 2009, Inf. Softw. Technol..

[16]  Ebrahim H. Mamdani,et al.  An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller , 1999, Int. J. Hum. Comput. Stud..

[17]  P. Costa,et al.  Normal Personality Assessment in Clinical Practice: The NEO Personality Inventory. , 1992 .

[18]  Oscar Castillo,et al.  2 Type-1 Fuzzy Logic , 2007 .

[19]  Sumedha Kshirsagar,et al.  A multilayer personality model , 2002, SMARTGRAPH '02.

[20]  R. R. Abidin.  Psychological Assessment Resources , 1995 .

[21]  Marjan Kaedi,et al.  Anger filter in agent simulation of human behavior , 2007 .

[22]  Barry W. Boehm,et al.  Software Engineering Economics , 1993, IEEE Transactions on Software Engineering.

[23]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[24]  Robert J. Kauffman,et al.  An Empirical Test of Object-Based Output Measurement Metrics in a Computer Aided Software Engineering (Case) Environment , 1991, J. Manag. Inf. Syst..

[25]  Murray R. Barrick,et al.  THE BIG FIVE PERSONALITY DIMENSIONS AND JOB PERFORMANCE: A META-ANALYSIS , 1991 .

[26]  Danny Ho,et al.  Improving the COCOMO model using a neuro-fuzzy approach , 2007, Appl. Soft Comput..

[27]  Rodina Binti Ahmad,et al.  Effects of software process maturity on COCOMO II’s effort estimation from CMMI perspective , 2008, 2008 IEEE International Conference on Research, Innovation and Vision for the Future in Computing and Communication Technologies.

[28]  Siew Hock Ow.  Improving the Accuracy of Software Cost Estimation Model Based on a New Fuzzy Logic Model , 2010 .

[29]  Yong-Feng Lin,et al.  Adaptable, Distributed Ontology Alignment System , 2008 .

[30]  Stacy Marsella,et al.  Modeling the cognitive antecedents and consequences of emotion , 2009, Cognitive Systems Research.

[31]  Stacy Marsella,et al.  A domain-independent framework for modeling emotion , 2004, Cognitive Systems Research.

[32]  Jürgen Bode,et al.  Multi-agent system for cost estimation , 1996 .

[33]  Kvsvn Raju,et al.  Improving the Accuracy of Effort Estimation through Fuzzy Set Representation of Size , 2009 .

[34]  M. Kazemifard,et al.  An Event-based Implementation of Emotional Agents , 2006 .

[35]  Rodina Binti Ahmad,et al.  Impact of CMMI Based Software Process Maturity on COCOMO II's Effort Estimation , 2010, Int. Arab J. Inf. Technol..

[36]  Desmond L. Cook.  Educational project management , 1971 .

[37]  Chang-Shing Lee,et al.  Intelligent estimation agent based on CMMI ontology for project planning , 2008, 2008 IEEE International Conference on Systems, Man and Cybernetics.

[38]  Barry W. Boehm,et al.  Anchoring the Software Process , 1996, IEEE Softw..

[39]  Oscar Castillo,et al.  Hybrid Intelligent Systems for Pattern Recognition Using Soft Computing - An Evolutionary Approach for Neural Networks and Fuzzy Systems , 2005, Studies in Fuzziness and Soft Computing.

[40]  Chang-Shing Lee,et al.  Ontology-based computational intelligent multi-agent and its application to CMMI assessment , 2009, Applied Intelligence.

[41]  Tuncer Ören,et al.  Cognitive complexity and dynamic personality in agent simulation , 2007, Comput. Hum. Behav..

[42]  Mary Beth Chrissis,et al.  CMMI: Guidelines for Process Integration and Product Improvement , 2003 .

[43]  R. Meredith Belbin,et al.  Team Roles at Work , 2022 .

[44]  Yahachiro Tsukamoto,et al.  AN APPROACH TO FUZZY REASONING METHOD , 1993 .

[45]  K. V. S. V. N. Raju,et al.  An Improved Fuzzy Approach for COCOMO's Effort Estimation Using Gaussian Membership Function , 2009, J. Softw..