Complex Fuzzy Logic Reasoning-Based Methodologies for Quantitative Software Requirements Specifications

Quantitative software engineering is one of the most important paradigms for software development. That is, Requirements, Analysis, Design, Coding, and Testing. One of the challenges associated with quantitative software engineering is the fact that many of the quantifiable parameters are concomitant with uncertainty. Part of the uncertainty is due to the fact that a significant portion of the software engineering process involves human beings presenting rational, yet difficult to quantify, behavior. Due to this fact, soft computing approaches, specifically fuzzy logic based reasoning, present significant opportunities for constructing sound quantitative software engineering models. This work presents a new and innovative approach for fuzzy logic based quantitative software engineering procedures. We present a complex fuzzy logic based inference system used to account for the intricate relations between software engineering constraints such as quality, software features, and development effort. The new model concentrates on the requirements specifications part of the software engineering process. Moreover, the new model significantly improves the expressive power and inference capability of the soft computing component in the soft computing based quantitative software engineering.

[1]  P. Cintula Advances in the ŁΠ and logics , 2003 .

[2]  Abraham Kandel,et al.  Complex fuzzy logic , 2003, IEEE Trans. Fuzzy Syst..

[3]  Witold Pedrycz,et al.  Fuzzy Adaptive Logic Networks as Hybrid Models of Quantitative Software Engineering , 2006, Intell. Autom. Soft Comput..

[4]  Abraham Kandel,et al.  Complex fuzzy sets , 2002, IEEE Trans. Fuzzy Syst..

[5]  J. Noppen,et al.  Dealing with fuzzy information in software design methods , 2004, IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04..

[6]  Miryung Kim,et al.  An Empirical Study of RefactoringChallenges and Benefits at Microsoft , 2014, IEEE Transactions on Software Engineering.

[7]  Abraham Kandel,et al.  Fuzzy Semantic Analysis and Formal Specification of Conceptual Knowledge , 1995, Inf. Sci..

[8]  Petr Cintula,et al.  Fuzzy class theory , 2005, Fuzzy Sets Syst..

[9]  Soren Lauesen,et al.  Software Requirements: Styles & Techniques , 2002 .

[10]  Abraham Kandel,et al.  A Comparative Study of Artificial Neural Networks and Info-Fuzzy Networks as Automated Oracles in Software Testing , 2012, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[11]  Kent L. Beck,et al.  Extreme programming explained - embrace change , 1990 .

[12]  Abraham Kandel,et al.  Axiomatic Theory of Complex Fuzzy Logic and Complex Fuzzy Classes , 2011, Int. J. Comput. Commun. Control.

[13]  Ivar Jacobson,et al.  Unified Modeling Language User Guide, The (2nd Edition) (Addison-Wesley Object Technology Series) , 2005 .

[14]  Tore Dybå,et al.  An empirical investigation of the key factors for success in software process improvement , 2005, IEEE Transactions on Software Engineering.

[15]  Abraham Kandel Automated Test Reduction Using an Info-Fuzzy Network , 2003 .

[16]  S. K. Sanyal,et al.  A new soft-computing based framework for project management using game theory , 2012, 2012 International Conference on Communications, Devices and Intelligent Systems (CODIS).

[17]  W. W. Royce,et al.  Managing the development of large software systems: concepts and techniques , 1987, ICSE '87.

[18]  Ken Schwaber,et al.  Agile Software Development with SCRUM , 2001 .

[19]  Abraham Kandel,et al.  Computational Intelligence in Software Quality Assurance , 2005, Series in Machine Perception and Artificial Intelligence.

[20]  June M. Verner,et al.  In-house software development: what project management practices lead to success? , 2005, IEEE Software.

[21]  Abraham Kandel,et al.  An axiomatic approach to fuzzy set theory , 1990, Inf. Sci..

[22]  S. K. Pillai,et al.  Evaluation of neural networks for software development effort estimation using a new criterion , 2014, SOEN.

[23]  B. Randell,et al.  Software Engineering: Report of a conference sponsored by the NATO Science Committee, Garmisch, Germany, 7-11 Oct. 1968, Brussels, Scientific Affairs Division, NATO , 1969 .

[24]  Yuanyuan Zhang,et al.  Search-based software engineering: Trends, techniques and applications , 2012, CSUR.

[25]  Durga Prasad Mohapatra,et al.  A survey of computational intelligence approaches for software reliability prediction , 2014, SOEN.

[26]  W. Pedrycz,et al.  Self organizing maps as a tool for software analysis , 2001, Canadian Conference on Electrical and Computer Engineering 2001. Conference Proceedings (Cat. No.01TH8555).

[27]  Scott Dick,et al.  Toward complex fuzzy logic , 2005, IEEE Transactions on Fuzzy Systems.

[28]  L. Zadeh Fuzzy sets as a basis for a theory of possibility , 1999 .

[29]  Esperanza Marcos,et al.  An Approach to the Integration of Qualitative and Quantitative Research Methods in Software Engineering Research , 2006, PhiSE.

[30]  Yoji Akao,et al.  Quality Function Deployment : Integrating Customer Requirements into Product Design , 1990 .

[31]  Joy Beatty,et al.  Software Requirements 3 , 2013 .

[32]  A. Kandel Fuzzy Mathematical Techniques With Applications , 1986 .

[33]  Robert LIN,et al.  NOTE ON FUZZY SETS , 2014 .

[34]  Aleksandar Dimov,et al.  Software reliability assessment via fuzzy logic model , 2011, CompSysTech '11.

[35]  Abraham Kandel,et al.  The Theory and Applications of Generalized Complex Fuzzy Propositional Logic , 2013, Soft Computing: State of the Art Theory and Novel Applications.

[36]  R. Pressman Software Engineering: a Practioner''s approach , 1987 .

[37]  S. Lauesen Software Requirements Styles and Techniques , 2001 .

[38]  W. Eric Wong,et al.  An empirical study on the specification and selection of components using fuzzy logic , 2005, CBSE'05.

[39]  Roy George,et al.  Uncertainty management issues in the object-oriented data model , 1996, IEEE Trans. Fuzzy Syst..

[40]  Azriel Levy,et al.  Foundations of set theory, 2nd Edition , 1973, Studies in logic and the foundations of mathematics.

[41]  Franco Montagna,et al.  On the predicate logics of continuous t-norm BL-algebras , 2005, Arch. Math. Log..

[42]  Alistair Sutcliffe,et al.  Scenario-based requirements analysis , 1998, Requirements Engineering.

[43]  Ch. Aswani Kumar,et al.  Generation of High Level Views in Reverse Engineering Using Formal Concept Analysis , 2014 .

[44]  Abhishek Singhal,et al.  Estimation of software reusability for component based system using soft computing techniques , 2014, 2014 5th International Conference - Confluence The Next Generation Information Technology Summit (Confluence).

[45]  Dean Leffingwell,et al.  Agile Software Requirements: Lean Requirements Practices for Teams, Programs, and the Enterprise , 2011 .

[46]  Abraham Kandel,et al.  A new interpretation of complex membership grade , 2011, Int. J. Intell. Syst..

[47]  Barry W. Boehm,et al.  A spiral model of software development and enhancement , 1986, Computer.

[48]  Alan M. Davis,et al.  A Unified Model of Requirements Elicitation , 2004, J. Manag. Inf. Syst..

[49]  Ivar Jacobson,et al.  Object-oriented software engineering - a use case driven approach , 1993, TOOLS.

[50]  Ali Selamat,et al.  An Adaptive Fuzzy Decision Matrix Model for Software Requirements Prioritization , 2014 .

[51]  Witold Pedrycz,et al.  Fuzzy Logic Classifiers and Models in Quantitative Software Engineering , 2007 .

[52]  Jonathan Lee,et al.  New Approach to Requirements Trade-Off Analysis for Complex Systems , 1998, IEEE Trans. Knowl. Data Eng..

[53]  Jonathan Lee Software Engineering with Computational Intelligence , 2003 .

[54]  Stefanie Seiler Software For Use A Practical Guide To The Models And Methods Of Usage Centered Design , 2016 .