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Machine-dependent software

{{more footnotes needed|date=January 2018}}Machine-dependent software is software that runs only on a specific computer. Applications that run on multiple computer architectures are called machine-independent, or cross-platform.Agrawala, & Rauscher (2014) Many organisations opt for such software because they believe that machine-dependent software is an asset and will attract more buyers. Organizations that want application software to work on heterogeneous computers may port that software to the other machines. Deploying machine-dependent applications on such architectures, such applications require porting. This procedure includes composing, or re-composing, the application's code to suit the target platform.

Porting

Porting is the process of converting an application from one architecture to another.Rashid, Patnaik, & Bhattacherjee, 2014 Software languages such as Java are designed so that applications can migrate across architectures without source code modifications. The term is applied when programming/equipment is changed to make it usable in a different architecture.

Code that does not operate properly on a specific system must be ported to another system.

Porting effort depends upon a few variables, including the degree to which the first environment (the source stage) varies from the new environment (the objective stage) and the experience of the creators in knowing platform-specific programming dialects.Huang, Li, & Xie, 2015

Many languages offer a machine independent intermediate code that can be processed by platform-specific interpreters to address incompatibilities.Yin, et al., 2012 The transitional representation characterises a virtual machine that can execute all modules written in the intermediate dialect. The intermediate code guidelines are interpreted into distinct machine code arrangements by a code generator to make executable code. The intermediate code may also be executed directly without static conversion into platform-specific code.Mathur, Miles, & Du, 2015

Approaches

  • Port the translator. This can be coded in portable code.
  • Adapt the source code to the new machine.
  • Execute the adjusted source utilizing the translator with the code generator source as data. This will produce the machine code for the code generator.

See also

References

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External links

  • Agrawala, A. K., & Rauscher, T. G., 2014, [https://books.google.com/books?id=1Z2jBQAAQBAJ&q=%22Machine-dependent+software%22 Foundations of microprogramming: architecture, software, and applications], Academic press
  • Huang, J., Li, Y. F., & Xie, M., 2015, [https://www.sciencedirect.com/science/article/pii/S0950584915001275 An empirical analysis of data preprocessing for machine learning-based software cost estimation], Information and Software Technology, 67, 108–127
  • Lee, J. H., Yu, J. M., & Lee, D. H., 2013, [https://www.researchgate.net/profile/Jae_Min_Yu/publication/257337906_A_tabu_search_algorithm_for_unrelated_parallel_machine_scheduling_with_sequence-_and_machine-dependent_setups_Minimizing_total_tardiness/links/54eac7450cf2f7aa4d57fce2/A-tabu-search-algorithm-for-unrelated-parallel-machine-scheduling-with-sequence-and-machine-dependent-setups-Minimizing-total-tardiness.pdf A tabu search algorithm for unrelated parallel machine scheduling with sequence-and machine-dependent setups: minimizing total tardiness], The International Journal of Advanced Manufacturing Technology, 69(9–12), 2081–2089
  • Lin, S. W., & Ying, K. C., 2014, [https://www.sciencedirect.com/science/article/pii/S0305054814001427 ABC-based manufacturing scheduling for unrelated parallel machines with machine-dependent and job sequence-dependent setup times], Computers & Operations Research, 51, 172–181
  • Mathur, R., Miles, S., & Du, M., 2015, Adaptive Automation: Leveraging Machine Learning to Support Uninterrupted Automated Testing of Software Applications, arXiv preprint {{arXiv|1508.00671}}
  • Rashid, E. A., Patnaik, S. B., & Bhattacherjee, V. C., 2014, [http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.846.4890&rep=rep1&type=pdf Machine learning and software quality prediction: as an expert system], International Journal of Information Engineering and Electronic Business (IJIEEB), 6(2), 9
  • Röhrich, T., & Welfonder, E., 2014, [https://www.sciencedirect.com/science/article/pii/S1474667017601356 Machine Independent Software Wiring and Programming of Distributed Digital Control Systems], In Digital Computer Applications to Process Control: Proceedings of the 7th IFAC/IFIP/IMACS Conference, Vienna, Austria, 17–20 September 1985 (p. 247), Elsevier
  • Shepperd, M., Bowes, D., & Hall, T., 2014, [http://eprints.lancs.ac.uk/127414/1/Bias.pdf Researcher bias: The use of machine learning in software defect prediction], Software Engineering, IEEE Transactions on, 40(6), 603–616
  • Wang, J. B., Sun, L. H., & Sun, L. Y., 2011, [https://www.sciencedirect.com/science/article/pii/S0307904X1000363X Single-machine total completion time scheduling with a time-dependent deterioration], Applied Mathematical Modelling, 35(3), 1506–1511
  • Yin, Y., Liu, M., Hao, J., & Zhou, M., 2012, Sin

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Category:Software architecture