Abstract
People in software development teams are crucial in order to gain and retain strategic advantage inside a highly competitive market. As a result, human factors have gained attention in the software industry. Software Project Managers are decisive to achieve project success. A competent project manager is capable of solving any problem that an organization may encounter, regardless of its complexity. This paper presents I-Competere which is a tool developed to forecast competence gaps in key management personnel by predicting planning and scheduling competence levels. Based on applied intelligence techniques, I-Competere allows the forecast and anticipation of competence needs thus articulating personnel development tools and techniques. The results of the test, using several artificial neural networks, are more than promising and show prediction accuracy.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Aggarwal, K., Singh, Y., Chandra, P., & Puri, M. (2005). Bayesian regularization in a neural network model to estimate lines of code using function points. Journal of Computer Science, 1(4), 505–509.
Ahmeda, M., Saliub, M., & AlGhamdia, J. (2005). Adaptive fuzzy logic-based framework for software development effort prediction. Information and Software Technology, 47(1), 31–48.
Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716–723.
Aramo-Immonen, H., Bikfalvi, A., Mancebo, N., Vanharanta, H. (2011). Project managers competence identification. International Journal of Human Capital and Information Technology Professionals (IJHCITP),2(1), 33–47.
Arbib, M. (1995). The handbook of brain theory and neural networks. Cambridge: MIT Press.
Barr, J., & Saraceno, F. (2002). A computational theory of the firm. Journal of Economic Behavior & Organization, 49(3), 345–361.
Bascil, M., & Temurtas, F. (2011). A study on hepatitis disease diagnosis using multilayer neural network with levenberg marquardt training algorithm. Journal of Medical Systems, 35, 433–436. doi:10.1007/s10916-009-9378-2.
Bermejo, P., Joho, H., Joemon, M., Villa, R. (2009). Comparison of feature construction methods for video relevance prediction. In Advances in multimedia modeling. Proceedings of 15th international multimedia modeling conference, MMM 2009. Sophia-Antipolis, France, 7–9 January 2009 (pp. 185–196).
Bisagni, C., Lanzi, L., Ricci, S. (2002) Optimization of helicopter subfloor components under crashworthiness requirements using neural networks. Journal of Aircraft, 39(2), 296–304.
Breiman, L., & Spector, P. (1992). Submodel selection and evaluation in regression. The x-random case. International Statistical Review, 60(3), 291–319.
Burger, M., & Neubauer, A. (2003). Analysis of tikhonov regularization for function approximation by neural networks. Neural Networks, 16(1), 79–90.
Bygstad, B., & Aanby, H. (2010). Ict infrastructure for innovation: a case study of the enterprise service bus approach. Information Systems Frontiers, 12(3), 257–265.
Cauchy, A. (1847). Methodes generales pour la resolution des systemes d’equations simultanees. Compte Rendu des Séances de L’Académie des Sciences, 25(3), 536–538.
Cho, S.B., & Shimohara, K. (1998). Evolutionary learning of modular neural networks with genetic programming. Applied Intelligence, 9(3), 191–200.
Crawford, L. (2005). Senior management perceptions of project management competence. International Journal of Project Management, 23(1), 7–16.
Crowther, P., & Cox, R. (2005). A method for optimal division of data sets for use in neural networks. In Knowledge-based intelligent information and engineering systems. Proceedings of 9th international conference, KES 2005, Melbourne, Australia, 14–16 September 2005, Part IV (pp. 906–912).
de Barcelos Tronto, I., da Silva, J., Sant’Anna, N. (2008). An investigation of artificial neural networks based prediction systems in software project management. Journal of Systems and Software, 81(3), 356–367.
Dietterich, T. (1998). Approximate statistical tests for comparing supervised classification learning algorithms. Neural Computation, 10(7), 1895–1923.
Durand, N., Alliot, J.M., Médioni, F. (2000). Neural nets trained by genetic algorithms for collision avoidance. Applied Intelligence, 13(3), 205–213.
El Emam, K., & Koru, A. (2008). A replicated survey of it software project failures. IEEE Software, 25(5), 84–90.
Fahlman, S. (1988). Faster-learning variations on back-propagation: an empirical study. In Proceedings of Connectionist Models Summer School (pp. 38–51).
Feldt, R., Angelis, L., Torkar, R., & Samuelsson, M. (2010). Links between the personalities, views and attitudes of software engineers. Information and Software Technology, 52(6), 611–624.
Ferri, C., Hernández-Orallo, J., & Modroiu, R. (2009). An experimental comparison of performance measures for classification. Pattern Recognition Letters, 30(1), 27–38.
Galinec, D. (2010). Human capital management process based on information technology models and governance. International Journal of Human Capital and Information Technology Professionals, 1(1), 44–60.
García-Crespo, A., González-Carrasco, I., Colomo-Palacios, R., López-Cuadrado, J., & Ruiz-Mezcua, B. (2011). Methodology for software development estimation optimization based on neural networks. IEEE Latin America Transactions, 9(3), 391–405.
Garcia-Crespo, A., Ruiz-Mezcua, B., Fernandez, D., & Zaera, R. (2006). Prediction of the response under impact of steel armours using a multilayer perceptron. Neural Computing & Applications, 16(2), 147–154.
Garg, A., Goyal, D., & Lather, A. (2010). The influence of the best practices of information system development on software smes: a research scope. International Journal of Business Information Systems, 5(3), 268–290.
Geraldi, J., Lee-Kelley, L., & Kutsch, E. (2010). The titanic sunk, so what? Project manager response to unexpected events. International Journal of Project Management, 28(6), 547–558.
Gevrey, M., Dimopoulos, I., & Lek, S. (2003). Review and comparison of methods to study the contribution of variables in artificial neural network models. Ecological Modelling, 160(3), 249–264.
Gibbs, W. (1994). Software’s chronic crisis. Scientific American, 271(3), 72–81.
Girosi, F., Jones, M., & Poggio, T. (1995). Regularization theory and neural networks architectures. Neural Computation, 7(2), 219–269.
Glass, R. (2006). The standish report: does it really describe a software crisis? Communications of the ACM, 49(8), 15–16.
Gomez, I., & Martin, M.P. (2011). Prototyping an artificial neural network for burned area mapping on a regional scale in mediterranean areas using modis images. International Journal of Applied Earth Observation and Geoinformation, 13(5), 741–752.
Gonzalez-Carrasco, I., Garcia-Crespo, A., Ruiz-Mezcua, B., & Lopez-Cuadrado, J. (2012). An optimization methodology for machine learning strategies and regression problems in ballistic impact scenarios. Applied Intelligence, 36(2), 424–441.
Grochowski, M., & Dutch, W. (2007). Learning highly non-separable boolean functions using constructive feedforward neural network. In ICANN’07 proceedings of the 17th international conference on artificial neural networks (pp. 180–189).
Haykin, S. (1994). Neural networks: A comprehensive foundation. Prentice Hall PTR.
He, Y., & Sun, Y. (2001). Neural network-based l1-norm optimisation approach for fault diagnosis of nonlinear circuits with tolerance. IEE Proceedings. Circuits, Devices and Systems, 148(4), 223–228.
Henderson, C., Potter, W., McClendon, R., & Hoogenboom, G. (2000). Predicting aflatoxin contamination in peanuts: a genetic algorithm/neural network approach. Applied Intelligence, 12(13), 183–192.
Henry, J. (2003). Software project management: A real-world guide to success. Addison Wesley Longman.
Hestenes, R., & Stiefel, E. (1952). Methods of conjugate gradients for solving linear systems. Journal of Research of the National Bureau of Standards, 49(6), 409–436.
Hodgson, D. (2002). Disciplining the professional: the case of project management. Journal of Management Studies, 39(6), 803–821.
Huang, M., Tsou, Y., & Lee, S. (2006). Integrating fuzzy data mining and fuzzy artificial neural networks for discovering implicit knowledge. Knowledge-Based Systems, 19(6), 396–403.
Jorgensen, M., & Molokken-Ostvold, K. (2006). How large are software cost overruns? A review of the 1994 chaos report. Information and Software Technology, 48(4), 297–301.
Kao, Y.T., & Zahara, E. (2008). A hybrid genetic algorithm and particle swarm optimization for multimodal functions. Applied Soft Computing, 8(2), 849–857.
Kelemenis, A., Ergazakis, K., & Askounis, D. (2011). Support managers’ selection using an extension of fuzzy topsis. Expert Systems with Applications, 38(3), 2774–2782.
Kumar, K.V., Ravi, V., Carr, M., & Raj Kiran, N. (2008). Software development cost estimation using wavelet neural networks. Journal of Systems and Software, 81(11), 1853–1867.
Kwak, Y. (2005). A brief history of project management. In The story of managing projects. Greenwood Publishing Group.
Lanzi, L., Bisagni, C., & Ricci, S. (2004). Neural network systems to reproduce crash behavior of structural components. Computers & Structures, 82(1), 93–108.
Levenberg, K. (1944). A method for the solution of certain non-linear problems in least squares. Quarterly Journal of Applied Mathematics, II(2), 164–168.
Li, J., Wu, C., & Wu, H. (2011). Wavelet neural network process control technology in the application of aluminum electolysis. In X. Wan (Ed.), Electrical power systems and computers. Lecture notes in electrical engineering (Vol. 99, pp. 937–941). Berlin: Springer.
Looney, C. (1993). Advances in feedforward neural networks: demystifying knowledge acquiring black boxes. Transactions on Knowledge and Data Engineering, 8(2), 211–226.
Majumder, M., Roy, P., & Mazumdar, A. (2007). Optimization of the water use in the river damodar in west Bengal in India: an integrated multi-reservoir system with the help of artificial neural network. Engineering Computing and Architecture, 1(2), 1–12, article 1192.
Marquardt, D. (1963). An algorithm for least-squares estimation of nonlinear parameters. SIAM Journal on Applied Mathematics, 11(2), 431–441.
McConnell, S. (2004). Professional software development. Addison-Wesley.
Morris, P., Crawford, L., Hodgson, D., Shepherd, M., & Thomas, J. (2006). Exploring the role of formal bodies of knowledge in defining a profession-the case of project management. International Journal of Project Management, 24(8), 710–721.
Muller, R., & Turner, J. (2007). Matching the project manager’s leadership style to project type. International Journal of Project Management, 25(1), 21–32.
Natarajan, T., Rajah, S., & Manikavasagam, S. (2011). Snapshot of personnel productivity assessment in Indian IT industry. International Journal of Information Technology Project Management, 2(1), 48–61.
Nelson, M., & Illingworth, W. (1991). A practical guide to neural nets. Reading: Addison-Wesley.
Onita, C., & Dhaliwal, J. (2010). Alignment within the corporate IT unit: an analysis of software testing and development. European Journal of Information Systems, 20(1), 48–68.
O’Sullivan, D., & Dooley, L. (2010). Collaborative innovation for the management of information technology resources. International Journal of Human Capital and Information Technology Professionals, 1(1), 16–30.
Patnayakuni, R., & Ruppel, C. (2010). A socio-technical approach to improving the systems development process. Information Systems Frontiers, 12(2), 219–234.
Peng, X., Zhe, H., Guifang, G., Gang, X., Binggang, C., & Zengliang, L. (2011). Driving and control of torque for direct-wheel-driven electric vehicle with motors in serial. Expert Systems with Applications, 38(1), 80–86.
Pereira, J., Cerpa, N., Verner, J., Rivas, M., & Procaccino, J. (2008). What do software practitioners really think about project success: a cross-cultural comparison. Journal of Systems and Software, 81(6), 897–907.
Priddy, K., & Keller, P. (2005). Artificial neural networks: An introduction. SPIE Press.
Principe, J., Euliano, N., & Lefebvre, W. (1999). Neural and adaptive systems: Fundamentals through simulations with CD-ROM. New York: Wiley.
Principe, J., Lefebvre, C., Lynn, G., Fancourt, C., & Wooten, D. (2007). Neuro solutions documentation. NeuroDimension, Inc.
Ramon y Cajal, S. (1894). The croonian lecture: La fine structure des centres nerveux. Proceedings of the Royal Society of London, 55, 444–468.
Rissanen, J. (1978). Modeling by shortest data description. Automatica, 14(5), 445–471.
Roy, N., Potter, W., & Landau, D. (2004). Designing polymer blends using neural networks, genetic algorithms, and markov chains. Applied Intelligence, 20(3), 215–229.
Ruano-Mayoral, M., Colomo-Palacios, R., Garcia-Crespo, A., & Gomez-Berbis, J. (2010). Software project managers under the team software process: a study of competences. International Journal of Information Technology Project Management, 1(1), 42–53.
Shahhosseini, V., & Sebt, M. (2011). Competency-based selection and assignment of human resources to construction projects. Scientia Iranica, 18(2), 163–180.
Sokolova, M., Rasras, R., & Skopin, D. (2006). The artificial neural network based approach for mortality structure analysis. American Journal of Applied Science, 3(2), 1698–1702.
Songwu, L., Member, S., & Basar, T. (1998). Robust nonlinear system identification using neural network models. IEEE Transactions on Neural Networks, 9(3), 407–429.
Stamelos, I. (2010). Software project management anti-patterns. Journal of Systems and Software, 83(1), 52–59.
Stamelos, I. (2010). Software project management anti-patterns. Journal of Systems and Software, 83(1), 52–59.
Stavrou, E.T., Charalambous, C., & Spiliotis, S. (2007). Human resource management and performance: a neural network analysis. European Journal of Operational Research, 181(1), 453–467.
Su, Y., & Huang, C. (2007). Neural-network-based approaches for software reliability estimation using dynamic weighted combinational models. Journal of Systems and Software, 80(4), 606–615.
Swingler, K. (1996). Applying neural networks. A practical guide. Academic Press.
Tang, F., Mu, J., & MacLachlan, D.L. (2010). Disseminative capacity, organizational structure and knowledge transfer. Expert Systems with Applications, 37(2), 1586–1593
Tarassenko, L. (1998). A guide to neural computing applications. Arnol/NCAF.
Watanabe, J., & Maruyama, T. (2010). Software development industrialization by process-centered style. Fujitsu Scientific and Technical Journal, 46(2), 168–176.
Wong, T., Wong, S., & Chin, K. (2011). A neural network-based approach of quantifying relative importance among various determinants toward organizational innovation. Expert Systems with Applications, 38(10), 13064–13072.
Wythoff, B. (1993). Backpropagation neural networks: a tutorial. Chemometrics and Intelligent Laboratory Systems, 18, 115–155.
Xie, C.-L., Chang, J.-Y., Shi, X.-C., & Dai, J.-M. (2010). Fault diagnosis of nuclear power plant based on genetic-rbf neural network. International Journal of Computer Applications in Technology, 39(1/2/3), 159–165.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Colomo-Palacios, R., González-Carrasco, I., López-Cuadrado, J.L. et al. I-Competere: Using applied intelligence in search of competency gaps in software project managers. Inf Syst Front 16, 607–625 (2014). https://doi.org/10.1007/s10796-012-9369-6
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10796-012-9369-6