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Software Reusability Estimation Using Machine Learning Techniques—A Systematic Literature Review

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Evolving Technologies for Computing, Communication and Smart World

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 694))

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Abstract

Software engineering is an application of engineering which is more focused on original development, but reusability plays a very significant role in order to produce good quality, error free, and less maintainable software. Software reusability is an attribute of quality which helps in selecting beforehand acquired notions in new statuses. Software reusability not only advances productivity, but it also provides a good quality software and has also optimistic effect on maintainability. Software reusability is advantageous in the manner that it provides high reliability, low cost of maintenance, and reduction in development time. In this paper, we have discussed and analyzed various machine learning techniques used for estimation of software reusability. It is found that machine learning techniques are competitive in nature with other reusability estimation techniques and can be used for estimation of reusability. This study will help software developers and information industry to elucidate that how software reusability can assist them in selecting advanced quality of software.

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References

  1. Adekola OD, Idowu SA, Okolie SO, Joshua JV, Akinsanya AO, Eze MO, Seun E (2017) Software maintainability and reusability using cohesion metrics. IJCTT 54:63–73

    Google Scholar 

  2. Wangoo DP, Singh A (2018) A classification based predictive cost model for measuring reusability level of open source software. 5:19–23

    Google Scholar 

  3. Singh AP, Tomar P (2018) Component reusability metrics to measure reusability of web services using fuzzy-multi-criteria approach. J Softw Evol Process 1–16

    Google Scholar 

  4. Padhy N, Singh RP, Satapathy SC (2019) Enhanced evolutionary computing based artificial intelligence model for web-solutions software reusability estimation. Clust Comput 22:9787–9804

    Google Scholar 

  5. Singh P, Sangwan O, Singh A, Pratap A (2015) A framework for assessing the software reusability using fuzzy logic approach for aspect oriented software. IJITCS 02:12–20

    Article  Google Scholar 

  6. Zhang D (2000) Applying machine learning algorithms in software development. In: Proceedings of the 2000 monterey workshop on modeling software system structures in a fastly moving scenario

    Google Scholar 

  7. Lounis H, Ait-Mehedine L (2004) Machine-learning techniques for software product quality assessment. In: Fourth international conference on quality software (QSIC 2004) proceedings, pp 102–109

    Google Scholar 

  8. Feldt R, Neto FG, Torkar R (2018) Ways of applying artificial intelligence in software engineering. In: RAISE ’18: proceedings of the 6th international workshop on realizing artificial intelligence synergies in software engineering, pp 35–41

    Google Scholar 

  9. Srinivasan K, Fisher D (1995) Machine learning approaches to estimating software development effort. IEEE Trans Softw Eng 21(2):126–137

    Article  Google Scholar 

  10. Singh Y, Bhatia P, Sangwan OP (2011) Software reusability assessment using soft computing techniques. ACM SIGSOFT Softw Eng Notes 36:1–7

    Google Scholar 

  11. Sanz-Rodriguez J, Dodero JM, Sanchez-Alonso S (2011) Metrics-based evaluation of learning object reusability. Software Qual J 19(1):121–140

    Article  Google Scholar 

  12. Sanz-Rodriguez J, Dodero JM, Sanchez-Alonso S (2010) Metrics-based evaluation of learning object reusability. Springer Science + Business Media, LLC, Berlin, pp 121–140

    Google Scholar 

  13. Nesbit JC, Li J (2004) Web-based tools for learning object evaluation. https://pdfs.semanticscholar.org/f1a5/e157937e377c65c53c3e26089e1c691f90c9.pdf

  14. Neven F, Duval E (2002) Reusable learning objects: a survey of LOM-based repositories. In: Proceedings of the tenth ACM international conference on multimedia, New York, NY, USA, pp 291–294

    Google Scholar 

  15. Zimmermann B, Meyer M, Rensing C, Steinmetz R (2007) Improving retrieval of reusable learning resources by estimating adaptation effort. Presented at the proceedings of the first international workshop on learning object discovery & exchange, pp 46–53

    Google Scholar 

  16. Papamichail M, Diamantopoulos T, Chrysovergis I, Samlidis P, Symeonidis A (2018) User-perceived reusability estimation based on analysis of software repositories. In: 2018 IEEE workshop on machine learning techniques for software quality evaluation (MaLTeSQuE), pp 49–54

    Google Scholar 

  17. Mao Y, Sahraoui HA, Lounis H (1998) Reusability hypothesis verification using machine learning techniques: a case study. In: Proceedings 13th IEEE international conference on automated software engineering (Cat. No.98EX239), pp 84–93

    Google Scholar 

  18. Prakash BVA, Ashoka DV, Aradhya VNM (2012) Application of data mining techniques for software reuse process. Procedia Technol 4:384–389

    Article  Google Scholar 

  19. Lounis H, Gayed TF, Boukadoum M (2011) using efficient machine-learning models to assess two important quality factors: maintainability and reusability. In: 2011 joint conference of the 21st international workshop on software measurement and the 6th international conference on software process and product measurement, pp 170–177

    Google Scholar 

  20. Di Stefano JS, Menzies T (2002) Machine learning for software engineering: case studies in software reuse. In: 14th IEEE international conference on tools with artificial intelligence (ICTAI 2002), Proceedings, pp 246–251

    Google Scholar 

  21. de Almeida MA, Louis H, Melo WL (1998) An investigation on the use of machine learned models for estimating correction costs. In: Proceedings of the 20th international conference on software engineering, pp 473–476

    Google Scholar 

  22. Zahara SI, Ilyas M, Zia T (2013) A study of comparative analysis of regression algorithms for reusability evaluation of object oriented based software components. In: 2013 international conference on open source systems and technologies, pp 75–80

    Google Scholar 

  23. Padhy N, Panigrahi R, Baboo S (2015) A systematic literature review of an object oriented metric: reusability. In: 2015 international conference on computational intelligence and networks, pp 190–191

    Google Scholar 

  24. Deepika, Sangwan OP (2016) Software reusability estimation using neuro-fuzzy technique—a review. Cyber Times Int J Technol Manag 9:40–46

    Google Scholar 

  25. Padhy N, Panigrahi R, Satapathy SC (2019) Identifying the reusable components from component-based system: proposed metrics and model. In: Information systems design and intelligent applications, pp 89–99

    Google Scholar 

  26. Vinobha A, Senthil Velan S, Babu C (2014) Evaluation of reusability in aspect oriented software using inheritance metrics. In: 2014 IEEE international conference on advanced communications, control and computing technologies, pp 1715–1722

    Google Scholar 

  27. Ahmaro IYY, Bin Mohd Yusoff MZ, Mohd Abualkishik A (2014) The current practices of software reusability approaches in Malaysia. In: 2014 8th Malaysian software engineering conference (MySEC), pp 172–176

    Google Scholar 

  28. Fazal-e-Amin AKM, Oxley A (2011) A review of software component reusability assessment approaches. Res J Inf Technol 3:1–11

    Google Scholar 

  29. Singh A, Tomar P (2016) Web service component reusability evaluation: a fuzzy multi-criteria approach. I.J. Inf Technolo Comput Sci 40–47

    Google Scholar 

  30. Padhy N, Singh RP, Satapathy SC (2018) Software reusability metrics estimation: algorithms, models and optimization techniques. Comput Electr Eng 69:653–668

    Article  Google Scholar 

  31. Nagpal R, Kumar Bhatia P, Sharma A (2015) Rank university websites using fuzzy AHP and fuzzy TOPSIS approach on usability. IJIEEB 7(1):29–36

    Google Scholar 

  32. Singh C, Pratap A, Singhal A (2014) Estimation of software reusability for component-based system using soft computing techniques. In: 2014 5th international conference—confluence the next generation information technology summit (Confluence), pp 788–794

    Google Scholar 

  33. Maggo S, Gupta C (2014) A machine learning based efficient software reusability prediction model for java based object oriented software. Int J Inf Technol Comput Sci 6:113

    Google Scholar 

  34. Shri A, Sandhu PS, Gupta V, Anand S (2010) Prediction of reusability of object-oriented software system using clustering approach. World Acad Sci Eng Technol 43:853–856

    Google Scholar 

  35. Deepika, Sangwan OP (2016) Neuro-fuzzy based approach to software reusability estimation. IJCTA 151–159

    Google Scholar 

  36. Imoize AL, Idowu D, Bolaji T (2019) A brief overview of software reuse and metrics in software engineering. World Sci News 122:56–70

    Google Scholar 

  37. Kaur PJ, Kaushal S (2018) A fuzzy approach for estimating quality of aspect oriented systems. Int J Parallel Program. https://doi.org/10.1007/s10766-018-0618-2

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Guru Jambheshwar University of Science and Technology, Hisar, Haryana, India

    Deepika & Om Prakash Sangwan

Authors
  1. Deepika
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  2. Om Prakash Sangwan
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Correspondence to Deepika .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Jaypee University of Information Technology, Solan, Himachal Pradesh, India

    Pradeep Kumar Singh

  2. CDAC Noida, Noida, Uttar Pradesh, India

    Arti Noor

  3. Department of Electrical Engineering, Indian Institute of Technology, Patna, Bihar, India

    Maheshkumar H. Kolekar

  4. Department of Computer Engineering, Institute of Technology, Nirma University, Ahmedabad, Gujarat, India

    Sudeep Tanwar

  5. Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, OH, USA

    Raj K. Bhatnagar

  6. JSSATEN Noida, Noida, Uttar Pradesh, India

    Shaweta Khanna

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Deepika, Sangwan, O.P. (2021). Software Reusability Estimation Using Machine Learning Techniques—A Systematic Literature Review. In: Singh, P.K., Noor, A., Kolekar, M.H., Tanwar, S., Bhatnagar, R.K., Khanna, S. (eds) Evolving Technologies for Computing, Communication and Smart World. Lecture Notes in Electrical Engineering, vol 694. Springer, Singapore. https://doi.org/10.1007/978-981-15-7804-5_5

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  • DOI: https://doi.org/10.1007/978-981-15-7804-5_5

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  • Print ISBN: 978-981-15-7803-8

  • Online ISBN: 978-981-15-7804-5

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