Neural Network Based Approach of Software Maintenance Prediction for Laboratory Information System

Authors: Vuk M. Popovic, Dunja D. Popovic

Abstract:

Software maintenance phase is started once a software project has been developed and delivered. After that, any modification to it corresponds to maintenance. Software maintenance involves modifications to keep a software project usable in a changed or a changing environment, to correct discovered faults, and modifications, and to improve performance or maintainability. Software maintenance and management of software maintenance are recognized as two most important and most expensive processes in a life of a software product. This research is basing the prediction of maintenance, on risks and time evaluation, and using them as data sets for working with neural networks. The aim of this paper is to provide support to project maintenance managers. They will be able to pass the issues planned for the next software-service-patch to the experts, for risk and working time evaluation, and afterward to put all data to neural networks in order to get software maintenance prediction. This process will lead to the more accurate prediction of the working hours needed for the software-service-patch, which will eventually lead to better planning of budget for the software maintenance projects.

Keywords: Laboratory information system, maintenance engineering, neural networks, software maintenance, software maintenance costs.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1132367

References:

[1] R. Singh, “International Standard ISO/IEC 12207 Software Life Cycle Processes”, Software Process: Improvement and Practice, vol. 2, 1996.
[2] G. Parikh, “Exploring the world of software maintenance: what is software maintenance?”, ACM SIGSOFT Software Engineering Notes, vol. 11, 1986.
[3] A. April, J. H. Hayes and A. Abran, “Software Maintenance Maturity Model (SMmm): the software maintenance process model”, Journal of Software Maintenance: Research and Practice, vol. 17, issue 3, 2005.
[4] Cycle Processes”, Software Process: Improvement and Practice, vol. 2, 1996.
[5] Patrick Li, “JIRA 7 Essentials”, Packt Publishing Ltd, Apr 2015
[6] Matthew Doar, “Practical JIRA Administration”, O'Reilly Media, Inc., May 2011
[7] Ravi Sagar, “Mastering JIRA”, Packt Publishing Ltd, May 2015
[8] F. J. Pino, F. Ruiz, F. García and M. Piattini, “A software maintenance methodology for small organizations: Agile_MANTEMA”, Journal of Software: Evolution and Process, vol. 24, 2012.
[9] A. April and A. Abran, “A Software Maintenance Maturity Model (S3M): Measurement Practices at Maturity Levels 3 and 4”, Electronic Notes in Theoretical Computer Science, Volume 233, 27 March 2009.
[10] K. Xu, M. Xie, LC. Tang, SL. Ho, “Application of neural network in forecasting engine systems reliability”, Applied Soft Computing, vol. 2, 2003.
[11] P.S. Rajpal, K.S. Shishodia, G.S. Sekhon, “An artificial neural network for modeling reliability, availability and maintainability of a repairable system”, Reliability Engineering and System Safety, vol. 91, 2006.
[12] Y. Takada, K. Matsumoto and K. Torii, “A softwarereliability prediction model using a neural-network”, Systems Comput Japan., vol. 25, 1994.
[13] T.M. Khoshgoftaar and R.M. Szabo, “Using neural networks to predict software faults during testing”, IEEE Trans Reliab., vol. 45, 1996.
[14] K.Y. Cai, L. Cai, W.D. Wang, Z.Y. Yu and D. Zhang, “On the neural network approach in software reliability modeling”, J Systems Software, vol. 58, 2001.
[15] L. Tian and A. Noore, Evolutionary neural network modeling for software cumulative failure time prediction”, Reliab Eng Syst Saf., vol. 87, 2005.
[16] D. Srinivasan, Neurocomputing, vol. 23, 1998.
[17] M. H. Beale, M. T. Hagan, H. B. Demuth, “MATLAB Neural Network Toolbox User’s Guide”, The MathWorks, Inc. , 2004
[18] PerOlof Bengtsson and Jan Bosch, Architecture Level Prediction of Software Maintenance, 1999.