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Assessment of Ant Colony using Component based Software Engineering Metrics


  • Department of CSE, U.I.E.T., Kurukshetra University, Kurukshetra - 136119, Haryana, India
  • School of ICT, Gautam Buddha University, Greater Noida - 201312, Uttar Pradesh, India


Objectives: Conventionally, to solve a problem it is necessary to design a new system every time and that it is difficult to reuse existing system for the designing of new system. This paper estimates usability of reusable components and interaction between components for integration of system. Methods: To handle this kind of problem Component Based System (CBS) has been considered. The reusability concept is the main factor of Component Based Software Engineering (CBSE) for components. More usage of reusable components provides better results in terms of estimation of reliability and efficiency of Component Based System (CBS). Ant Colony Optimization (ACO) is one among soft computing technique which may be used to estimate the path followed by components and interaction of components. In this paper ACO is used as methodology to find out more reusable components to increase the reliability or efficiency of the system. MATLAB is used for implementation of ACO. Performance metrics like Component efficiency, component density and component dependency are used for the analysis of proposed work. Findings: The results shows that in proposed mechanism the component efficiency is high, component density is also high as compare to existing CBS. Applications: This paper may helpful for the analysis of soft computing techniques by the use of CBSE performance metrics.


ACO, Component Based Software Engineering (CBSE), Component Based System (CBS), Reusability, Soft Computing and MATLAB.

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  • Katiyar S, Nasiruddin II, Abdul Ansari Q. Ant colony optimization: A tutorial review. MR International Journal of Engineering and Technology. 2015; 7(2):35-41.
  • Ray S, Bhattacharya A, Bhattacharjee S. Optimal placement of switches in a radial distribution network for reliability improvement. International Journal of Electrical Power and Energy Systems. 2016 Mar; 76:53–68.
  • Jain P. Towards the adoption of modern software development approach: component based software engineering. Indian Journal of Science and Technology. 2016 Aug; 9(32):1-5.
  • Hong TP, Huang LI, Lin WY, Liu YY, Chakraborty G. Dynamic migration in multiple ant colonies. Proceedings of 2nd International Conference on Cybernetics; Gdynia. 2015. p. 146–50.
  • Biswas S, Kaiser MS, Mamun SA. Applying ant colony optimization in software testing to generate prioritized optimal path and test data. Proceeding of 2nd International Conference on Electrical Engineering and Information Communication Technology; Dhaka. 2015. p. 1-6.
  • Ibnez ML, Stutzle T, Dorigo M. Ant colony optimization: A component-wise overview. IRIDIA Technical Report Series. 2015. p. 1-41.
  • Kaur I, Kaur S. Software component retrieval using GA and ACO. International Journal of Advanced Research in Computer Science and Software Engineering. 2015 Jul; 5(7):212-5.
  • Sabeena S, Sarojini B. Optimal feature subset selection using ant colony optimization. Indian Journal of Science and Technology. 2015 Dec; 8(35):1-5.
  • Dizaji ZA, Gharehchopogh FS. A hybrid of ant colony optimization and chaos optimization algorithms approach for software cost estimation. Indian Journal of Science and Technology. 2015 Jan; 8(2):128-33.
  • Madaan N, Kaur J. A survey on selection techniques of component based software. International Journal of Advanced Computation Technology. 2014; 4(13):1245-50.
  • Sandhu K, Gaba T. A novel technique for components retrieval from repositories. International Journal of Advanced Computer Technology. 2014; 3(6):912-20.
  • Tyagi K, Sharma A. A heuristic model for estimating componentbased software system reliability using ant colony optimization. World Applied Sciences Journal. 2014; 31(11):1983-91.
  • Niranjan P, Shireesha P, Reddy MV. Development of reuse repository and software component performance analysis. International Journal of Application or Innovation in Engineering and Management. 2013 Jun; 2(6):473-7.
  • Chikhalikar A, Darade A. Swarm intelligence techniques: Comparative study of ACO and BCO. Journal of Computer Society of India, Mumbai. 2013 Apr:1-9.
  • Selvi V, Umarani R. Comparative analysis of ant colony and particle swarm optimization techniques. International Journal of Computer Applications. 2010; 5(4):1-6.


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