A shift-invariant morphological system for software development cost estimation

Abstract: This work presents a shift-invariant morphological system to solve the problem of software development cost estimation (SDCE). It consists of a hybrid morphological model, which is a linear combination between a morphological-rank (MR) operator (nonlinear) and a Finite Impulse Response (FIR) operator (linear), referred to as morphological-rank-linear (MRL) filter. A gradient steepest descent method to adjust the MRL filter parameters (learning process), using the Least Mean Squares (LMS) algorithm, and a systematic approach to overcome the problem of non-differentiability of the morphological-rank operator are used to improve the numerical robustness of the training algorithm. Furthermore, an experimental analysis is conducted with the proposed system using the NASA software project database, and in the experiments, two relevant performance metrics and an evaluation function are used to assess its performance. The results obtained are compared to models recently presented in literature, showing superior performance of this kind of morphological systems for the SDCE problem.

[1]  Jean Serra,et al.  Image Analysis and Mathematical Morphology , 1983 .

[2]  Petros Maragos,et al.  MRL-filters: a general class of nonlinear systems and their optimal design for image processing , 1998, IEEE Trans. Image Process..

[3]  Lutz Prechelt,et al.  PROBEN 1 - a set of benchmarks and benchmarking rules for neural network training algorithms , 1994 .

[4]  Adriano Lorena Inácio de Oliveira,et al.  Estimation of software project effort with support vector regression , 2006, Neurocomputing.

[5]  Philippe Salembier,et al.  Adaptive rank order based filters , 1992, Signal Process..

[6]  Stephen S. Wilson.  Morphological Networks , 1989, Other Conferences.

[7]  Edward R. Dougherty,et al.  Facilitation of optimal binary morphological filter design via structuring element libraries and design constraints , 1992 .

[8]  Stefan Koch,et al.  Software project effort estimation with voting rules , 2009, Decis. Support Syst..

[9]  Gary William Flake,et al.  Efficient SVM Regression Training with SMO , 2002, Machine Learning.

[10]  Jennifer L. Davidson,et al.  Morphology neural networks: An introduction with applications , 1993 .

[11]  H. Minkowski.  Gesammelte abhandlungen von Hermann Minkowski, unter mitwirkung von Andreas Speiser und Hermann Weyl hrsg. von David Hilbert , 2022 .

[12]  Harvard Univer.  A Representation Theory for Morphological Image and Signal Processing , 1989 .

[13]  R.N. Charette,et al.  Why software fails [software failure] , 2005, IEEE Spectrum.

[14]  Chris F. Kemerer,et al.  An empirical validation of software cost estimation models , 1987, CACM.

[15]  Martin J. Shepperd,et al.  Estimating Software Project Effort Using Analogies , 1997, IEEE Trans. Software Eng..

[16]  Edward J. Coyle,et al.  Stack filters and the mean absolute error criterion , 1988, IEEE Trans. Acoust. Speech Signal Process..

[17]  José Demisio Simões da Silva,et al.  An investigation of artificial neural networks based prediction systems in software project management , 2008, J. Syst. Softw..

[18]  Philippe Salembier.  Structuring element adaptation for morphological filters , 1992, J. Vis. Commun. Image Represent..

[19]  Capers Jones,et al.  Why software fails , 1996 .

[20]  Silvio Romero de Lemos Meira,et al.  Software Effort Estimation Using Machine Learning Techniques with Robust Confidence Intervals , 2007 .

[21]  David M. Auslander,et al.  Control and dynamic systems , 1970 .

[22]  Silvio Romero de Lemos Meira,et al.  Software Effort Estimation Using Machine Learning Techniques with Robust Confidence Intervals , 2007, 19th IEEE International Conference on Tools with Artificial Intelligence(ICTAI 2007).

[23]  Silvio Romero de Lemos Meira,et al.  A GA-based feature selection and parameters optimization for support vector regression applied to software effort estimation , 2008, SAC '08.

[24]  Stephen Marshall,et al.  The use of genetic algorithms in morphological filter design , 1996, Signal Process. Image Commun..

[25]  Michael P. Clements,et al.  On the limitations of comparing mean square forecast errors , 1993 .

[26]  Mahmoud O. Elish.  Improved estimation of software project effort using multiple additive regression trees , 2009, Expert Syst. Appl..

[27]  Victor R. Basili,et al.  A meta-model for software development resource expenditures , 1981, ICSE '81.

[28]  Silvio Romero de Lemos Meira,et al.  Software Effort Estimation using Machine Learning Techniques with Robust Confidence Intervals , 2007, 7th International Conference on Hybrid Intelligent Systems (HIS 2007).

[29]  Amrit L. Goel,et al.  Empirical Data Modeling in Software Engineering Using Radical Basis Functions , 2000, IEEE Trans. Software Eng..

[30]  Tiago Alessandro Espínola Ferreira,et al.  An intelligent hybrid morphological-rank-linear method for financial time series prediction , 2009, Neurocomputing.

[31]  Petros Maragos,et al.  Character recognition using min-max classifiers designed via an LMS algorithm , 1992, Other Conferences.