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Talib, Ramzan
- Interactive Systems Regarding Global Software Development and Offshoring
Abstract Views :206 |
PDF Views:66
Authors
Affiliations
1 Department of Computer Science, Govt College University Faisalabad, Illama Iqbal Road, Faisalabad 38000, PK
2 Department of Software Engineering, Govt College University Faisalabad, Illama Iqbal Road, Faisalabad 38000, PK
1 Department of Computer Science, Govt College University Faisalabad, Illama Iqbal Road, Faisalabad 38000, PK
2 Department of Software Engineering, Govt College University Faisalabad, Illama Iqbal Road, Faisalabad 38000, PK
Source
Current Science, Vol 112, No 10 (2017), Pagination: 2134-2138Abstract
Modern information technology (IT) methods are reshaping the global market with great success. With today's global software industry, IT has made innovations everywhere, including businesses and consumer practices. This has made developing countries like India and China participate in the global market. This communication focuses on intelligent interacting systems which are present over globe and these are a source of rising the software development cycle with the help of modern communication facilities. Free e-Market globalization is now vital for billions of people. However, IT leadership is not possible without a review of the existing system. The present study is based over the issue of the global market related research, education and investment in IT technology. IT-based-leadership can give sustainable global competitive advantage to our country. So the role of iterative software development is crucial to be targeted in a systematic fashion.Keywords
Global Software Development, Information Technology, Innovation, Software Services, Technology.Full Text
References
- Tjader, C. Y. et al., Offshore outsources decision making: a policymaker’s perspective. Eur. J. Oper. Res., 2010, 207(1), 434–444.
- Nurcin, C. et al., Simulation-based workforce assignment in a multi-organizational social network for alliance-based software development. Simul. Modeling Pract. Theory, 2011, 19(10), 2169–2188.
- Rosalba, G., et al., Empirical studies on the use of social software in global software development – a systematic mapping study. Inf. Software Technol., 2013, 55(7), 1143–1164.
- Vernera, J. M. et al., Risks and risk mitigation in global software development: a tertiary study etc. Inf. Software Technol., 2014, 56(1), 54–78.
- Hina, S. et al., Global software testing under deadline pressure: vendor-side experiences. Inf. Software Technol., 2014, 56(1), 6–19.
- Jan, B. et al., From integration to composition: on the impact of software product lines, global development and ecosystems. J. Syst. Software, 2010, 83(1), 67–76.
- Ita, R. et al., A process framework for global software engineering teams. Inf. Software Technol., 2012, 54(11), 1175–1191.
- Javier, Portillo-Rodrígueza et al., Using agents to manage sociotechnical congruence in a global software engineering project. Inf. Sci., 2014, 264, 230–259.
- Srinivas, N. et al., Knowledge transfer challenges and mitigation strategies in global software development – a systematic literature review and industrial validation. Int. J. Inf. Manage., 2013, 33(2), 333–355.
- Alberto, A. et al., Coordination implications of software architecture in a global software development project. J. Syst. Software, 2010, 83(10), 1881–1895.
- Javier, Portillo-Rodrígueza et al., Tools used in global software engineering: a systematic mapping review. Inf. Software Technol., 2012, 54(7), 663–685.
- Christina, M. et al., The effect of governance on global software development: an empirical research in transactive memory systems. Inf. Software Technol., available online 19 April 2014.
- Zahedi, M., Shahin. M. and Babar, M. A., A systematic review of knowledge sharing challenges and practices in global software development. Int. J. Inf. Manage. Part A, 2016, 36(6), 995–1019.
- Ali, N. and Lai, R., A method of requirements change management for global software development. Inf. Software Technol., 2016, 70, 49–67.
- Niazi, M. et al., Challenges of project management in global software development: a client-vendor analysis. Inf. Software Technol., 2016, 80, 1–19.
- Yagüe, A. et al., An exploratory study in communication in Agile Global Software Development. Comput. Standards Interf., 2016, 48, 184–197.
- Mate, A. and Trujillo, J., Empowering global software development with business intelligence. Inf. Software Technol., 2016, 76, 81–91.
- Vizcaíno, A. et al., A validated ontology for global software development. Comput. Standards Interf., 2016, 46, 66–78.
- Comparison of Bioinspired Computation and Optimization Techniques
Abstract Views :249 |
PDF Views:68
Authors
Affiliations
1 Department-of-Computer Science, Government-College-University, Faisalabad - 38000, PK
1 Department-of-Computer Science, Government-College-University, Faisalabad - 38000, PK
Source
Current Science, Vol 115, No 3 (2018), Pagination: 450-453Abstract
In this article we focus on the bioinspired algorithms and their computational classification. The basic ideas and various techniques developed recently are described. The research outcomes in the computational area of solution optimization are presented for different problems, i.e. mathematical, combinatorial, exact approximation and multiple objective optimization. Moreover, evolutionary, stochastic and swarm optimization algorithms are discussed. All these areas have principles of extracting natural concepts in the form of mathematics and algorithms. Nature-inspired algorithms can help explore new dimensions to solve many problems with optimal cost and time. This review shows that bioinspired computing can provide innovative optimal computational algorithms.Keywords
Bioinspired Computing, Combinatorial Optimization, Computational Complexity, Evolutionary Algorithms.Full Text
References
- Kar, A. K., Bio inspired computing – a review of algorithms and scope of applications. Expert Syst. Appl., 2016, 59, 20–32.
- Aziz, M. S. and El Sheriff, A. Y., Biomimicry as an approach for bio-inspired structure with the aid of computation. Alexandria Eng. J., 2016, 55(1), 707–714.
- Marinescu, D. C., Nature-inspired Algorithms and Systems, Complex Systems and Clouds, 2017, pp. 33–63.
- Kurdi, H. et al., A combinatorial optimization algorithm for multiple cloud service composition. Comput. Electr. Eng., 2015, 42, 107–113.
- Amiri, M. and Amiri, M., A new bio-inspired stimulator to suppress hyper-synchronized neural firing in a cortical network. J. Theor. Biol., 2016, 410, 107–118.
- Cordone, R. and Lulli, G., Multimode extensions of combinatorial optimization problems. Electron. Notes Discrete Math., 2016, 55, 17–20.
- Raja, M. A. Z. et al., Design of bio-inspired computational intelligence technique for solving steady thin film flow of Johnson–Segalman fluid on vertical cylinder for drainage problems, J. Taiwan Inst. Chem. Eng., 2016, 60, 59–75.
- An, H. et al., Structural optimization for multiple structure cases and multiple payload cases with a two-level multipoint approximation method. Chin. J. Aeronaut., 2016, 29(5), 1273–1284.
- Yi, C. and Sjoden, G., Heuristic optimization of group structure using physics-based fitness approximation. Ann. Nucl. Energy, 2016, 96, 389–400.
- Dullinger, C. and Struckl, W., Simulation-based multi-objective system optimization of train traction systems. Simul. Model. Practice Theory, 2017, 72, 104–117.
- Vitayasak, S. and Pongcharoen, P., A tool for solving stochastic dynamic facility layout problems with stochastic demand using a genetic algorithm or modified backtracking search algorithm. Int. J. Prod. Econ., 2016, 190, 146–157.
- Sundar, V. S. and Michael, D. S., Surrogate-enhanced stochastic search algorithms to identify implicitly defined functions for reliability analysis. Struct. Safety, 2016, 62, 1–11
- Li, W. et al., Multi-objective evolutionary algorithms and hyperheuristics for wind farm layout optimization. Renewable Energy, 2017, 105, 473–482.
- Jothi, R. et al., Functional grouping of similar genes using eigen analysis on minimum spanning tree based neighborhood graph. Comput. Biol. Med., 2016, 71, 135–148.
- Shalom, M. et al., On-line maximum matching in complete multipartite graphs with an application to optical networks. Discrete Appl. Math., 2016, 199, 123–136.
- Zhang, Z. et al., Generating combinatorial test suite using combinatorial optimization. J. Syst. Software, 2014, 98, 191–207.
- Zhao, J. and Wang, N., A bio-inspired algorithm based on membrane computing and its application to gasoline blending scheduling. Comput. Chem. Eng., 2011, 35(2), 272–283.
- Zheng, Z. and Jiang, J., Bio-inspired coplanar-gate-coupled ITOfree oxide-based transistors employing natural nontoxic bio-polymer electrolyte. Org. Electron., 2016, 37, 474–478.
- Sesum-Cavic, V. et al., Bio-inspired search algorithms for unstructured P2P overlay networks. Swarm Evol. Comput., 2016, 29, 73–93.
- Maitra, A. et al., A brief survey on bio-inspired algorithms for autonomous landing. IFAC-Papers Online, 2016, 49(1), 407–412.
- Dou, R. and Duan, H., Lévy flight based pigeon-inspired optimization for control parameters optimization in automatic carrier landing system. Aerosp. Sci. Technol., 2017, 61, 11–20.
- Konar, D. and Bhattacharyya, S., An improved hybrid quantuminspired genetic algorithm (HQIGA) for scheduling of real-time task in multiprocessor system. Appl. Soft Comput., 2017, 53, 296–307.
- Ahmed, A. M. et al., BoDMaS: Bio-inspired selfishness detection and mitigation in data management for ad-hoc social networks. Ad Hoc Networks, 2017, 55, 119–131.
- Samanta, S. and Choudhury, A., Quantum-inspired evolutionary algorithm for scaling factor optimization during manifold medical information embedding. Quantum Inspired Comput. Intell., 2017, 285–326.
- Jia, J. et al., Joint topology control and routing for multi-radio multi-channel WMNs under SINR model using bio-inspired techniques. Appl. Soft Comput., 2015, 32, 49–58.
- Sen, D. and Kankanhalli, M., A bio-inspired center-surround model for salience computation in images. J. Vis. Commun. Image Represent., 2015, 30, 277–288.
- Bonabeau, E., Dorigo, M. and Theraulaz, G., Swarm intelligence: from natural to artificial systems. J. Artif. Soc. Soc. Simul., 1999, 4, 320.
- Ismkhan, H., Effective heuristics for ant colony optimization to handle large-scale problems. Swarm Evol. Computat., 2017, 32, 140–149.
- Luo, J. and Liu, Q., An artificial bee colony algorithm for multiobjective optimization. Appl. Soft Comput., 2017, 50, 235–251.
- Das, S., Biswas, A., Dasgupta, S. and Abraham, A., Bacterial foraging optimization algorithm: theoretical foundations, analysis, and applications. Found. Comput. Intell., 2009, 3, 23–55.
- Pradhan, P. M. and Panda, G., Solving multiobjective problems using cat swarm optimization. Expert Syst. Appl., 2012, 39(3), 2956–2964.
- Yang, X. S. and Deb, S., Engineering optimisation by cuckoo search. Int. J. Math. Model. Num. Optim., 2010, 1(4), 330–343.
- Chu, Y., Mi, H., Liao, H., Ji, Z. and Wu, Q. H., A fast bacterial swarming algorithm for high-dimensional function optimization. In IEEE Congress on Evolutionary Computation (CEC 2008), 2008, pp. 3135–3140.
- Yang, X. S., Firefly algorithm, Levy flights and global optimization. Res. Dev. Intell. Syst., 2010, XXVI, 209–218.
- Shen, W., Guo, X., Wu, C. and Wu, D., Forecasting stock indices using radial basis function neural networks optimized by artificial fish swarm algorithm. Knowledge-Based Syst., 2011, 24(3), 378–385.
- Shi, Y., Particle swarm optimization, developments, applications and resources. In Proceedings of the IEEE Congress on Evolutionary Computation, 2001, vol. 1, pp. 81–86.