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Cost-effective and fault-resilient reusability prediction model by using adaptive genetic algorithm based neural network for web-of-service applications

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Abstract

The exponential rise in software technologies and its significances has demanded academia-industries to ensure low cost software solution with assured service quality and reliability. A low cost and fault-resilient software design is must, where to achieve low cost design the developers or programmers prefer exploiting source or function reuse. However, excessive reusability makes software vulnerable to get faulty due to increased complexity and aging proneness. Non-deniably assessing reusability of a class of function in software can enable avoiding any unexpected fault or failure. To achieve it developing a robust and efficient reusability estimation or prediction model is of utmost significance. On the other hand, the aftermath consequences of excess reusability caused faults might lead significant losses. Hence assessing cost effectiveness and efficacy of a reusability prediction model is must for software design optimization. In this paper, we have examined different reusability prediction models for their cost effectiveness and prediction efficiency over object-oriented software design. At first to examine the reusability of a class, three key object oriented software metrics (OO-SM); cohesion, coupling and complexity of the software components are used. Furthermore, our proposed cost-efficient reusability prediction model incorporates Min–Max normalization, outlier detection, reusability threshold estimation; T test analysis based feature selection and various classification algorithms. Different classifiers including decision tree (DT), Naïve Bayes (NB), artificial neural network (ANN) algorithms, extreme learning machine (ELM), regression algorithms, multivariate adaptive regression spline (MARS) and adaptive genetic algorithm (AGA) based ANN are used for reusability prediction. Additionally, the cost effectiveness of each reusability prediction model is estimated, where the overall results have revealed that AGA based ANN as classifier in conjunction with OO-SM, normalization, T test analysis based feature selection outperforms other state-of-art techniques in terms of both accuracy as well as cost-effectiveness.

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References

  1. Caldiera, G., Basili, V.R.: Identifying and qualifying reusable software components. IEEE Softw. 24, 61–70 (1991)

    Google Scholar 

  2. Sommerville, I.: Software Engineering, 9th edn. Addison-Wesley, New York (2011)

    MATH  Google Scholar 

  3. Goel, B.M., Bhatia, P.K.: Analysis of reusability of object-oriented systems using object-oriented metrics. ACM SIGSOFT Softw. Eng. Notes 38, 1–5 (2013)

    Google Scholar 

  4. Kumar, V., Kumar, R., Sharma, A.: Applying neuro-fuzzy approach to build the reusability assessment framework across software component releases—an empirical evaluation. Int. J. Comput. Appl. 70, 41–47 (2013)

    Google Scholar 

  5. Singh, G.: Metrics for measuring the quality of object-oriented software. ACM SIGSOFT Softw. Eng. Notes 38, 1–5 (2013)

    Google Scholar 

  6. Singhani, H., Suri, P.R.: Testability assessment model for object oriented software based on internal and external quality factors. Glob. J. Comput. Sci. Technol. C. 15, 5 (2015)

    Google Scholar 

  7. Mijac, M., Stapic, Z.: Reusability metrics of software components: survey. In: Conference Paper (2015)

  8. Srivastava, S., Kumar, R.: Indirect method to measure software quality using CK-OO suite. International Conference on Intelligent Systems and Signal Processing (ISSP), Gujarat, pp. 47–51 (2013)

  9. Goel, B.M., Bhatia, P.K.: Analysis of reusability of object-oriented system using CK metrics. Int. J. Comput. Appl. 60(10), 0975–8887 (2012)

    Google Scholar 

  10. Rosenberg, L.H., Hyatt, L.E.: Software quality metrics for object-oriented environments. Crosstalk J. 10, 1–16 (1997)

    Google Scholar 

  11. Chidamber, S.R., Kemerer, C.F.: A metrics suite for object oriented design. IEEE Trans. Softw. Eng. 20, 476–493 (1994)

    Article  Google Scholar 

  12. Antony, P.J.: Predicting reliability of software using thresholds of CK metrics. Int. J. Adv. Netw. Appl. 4, 6 (2013)

    Google Scholar 

  13. Hudiab, A., Al-Zaghoul, F., Saadeh, M., Saadeh, H.: ADTEM—architecture design testability evaluation model to assess software architecture based on testability metrics. J. Softw. Eng. Appl. 8, 201–210 (2015)

    Article  Google Scholar 

  14. Singh, S., Thapa, M., Singh, S., Singh, G.: Software engineering—survey of reusability based on software component. Int. J. Comput. Appl. 8(12) (2010)

    Article  Google Scholar 

  15. Berander, P.: Software Quality Attributes and Trade-Offs. Blekinge institute of technology, Karlskrona (2005)

    Google Scholar 

  16. Shatnawi, R.: A quantitative investigation of the acceptable risk levels of object-oriented metrics in open-source systems. IEEE Trans. Softw. Eng. 36, 216–225 (2010)

    Article  Google Scholar 

  17. Shatnawi, R., Li, W., Swain, J., Newman, T.: Finding software metrics threshold values using roc curves. J. Softw. Maint. Evol. 22, 1–16 (2010)

    Article  Google Scholar 

  18. Goel, B.M., Bhatia, pk: Analysis of reusability of object-oriented system using CK metrics. Int. J. Comput. Appl. 60, 10 (2012)

    Google Scholar 

  19. Bakar, N.S.A.A.: The analysis of object-oriented metrics in C++ programs. In: Lecture Notes on Software Engineering 4(1) (2016)

    Article  Google Scholar 

  20. Torkamani, M.A.: Metric suite to evaluate reusability of software product line. Int. J. Electr. Comput. Eng. (IJECE) 4(2), 285–294 (2014)

    Google Scholar 

  21. Aloysius, A., Maheswar, K.: A review on component based software metrics. Int. J. Fuzzy Math. Arch. 7(2), 185–194 (2015)

    Google Scholar 

  22. Gandhi, P., Bhatia, P.K.: Reusability metrics for object-oriented system: an alternative approach. Int. J. Softw. Eng. (IJSE) 1(4), 63–72 (2010)

    Google Scholar 

  23. Huda, M., Arya, Y.D.S., Hasan Khan, M.: Quantifying reusability of object oriented design: a testability perspective. J. Softw. Eng. Appl. 8, 175–183 (2015)

    Article  Google Scholar 

  24. Kulkarni, U.L., Kalshetty, Y.R., Arde, V.G.: Validation of CK metrics for object oriented design measurement. In: 2010 3rd International Conference on Emerging Trends in Engineering and Technology (ICETET), Goa, pp. 646–651 (2010)

  25. Kumar, A.: Measuring software reusability using svm based classifier approach. Int. J. Inf. Technol. Knowl. Manage. 5(1), 205–209 (2012)

    Google Scholar 

  26. Etzkorn, L.H., Davis, C.G., Bowen, L.L., Wolf, J.C., Wolf, R.P., Yun, M.Y., Vinz, B.L., Orme, A.M., Lewis, L.W.: The program analysis tool for reuse: identifying reusable components. In: Proceedings of the Eleventh International FLAIRS Conference

  27. Zahara, S.I., Ilyas, M., Zia, T.: A study of comparative analysis of regression algorithms for reusability evaluation of object oriented based software components. In: International Conference on Open Source Systems and Technologies (ICOSST), Lahore, pp. 75–80 (2013)

  28. Singh, P.K., Sangwan, O.P., Singh, A.P., Pratap, A.: A framework for assessing the software reusability using fuzzy logic approach for aspect oriented software. Inf. Technol. Comput. Sci. 02, 12–20 (2015)

    Google Scholar 

  29. Kumar, V., Kumar, R., Sharma, A.: Applying neuro-fuzzy approach to build the reusability assessment framework across software component releases—an empirical evaluation. Int. J. Comput. Appl. 70(15) (2013)

    Article  Google Scholar 

  30. Singh, C., Pratap, A., Singhal, A.: Estimation of software reusability for component based system using soft computing techniques. In: 5th International Conference—Confluence The Next Generation Information Technology Summit (Confluence), Noida, pp. 788–794 (2014)

  31. Sandhu, P.S., Singh, H.: A reusability evaluation model for oo-based software components. Int. J. Electr. Comput. Eng. 1, 247–252 (2012)

    Google Scholar 

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

    Google Scholar 

  33. Kaur, M., Mahajan, M., Sandhu, P.S.: A k-NN based approach for reusability evaluation of object-oriented based software components. In: International Conference on Information and Communications Security (2011)

  34. Nair, T.R., Selvarani, R.: Estimation of software reusability: an engineering approach, association for computing machinery. SIGSOFT 35(1), 1–6 (2010)

    Article  Google Scholar 

  35. Guo, X.L., Wang, H.Y., Glass, D.H.: A growing bayesian self-organizing map for data clustering. In: Proceedings of the International Conference on Machine Learning and Cybernetics (ICMLC), vol. 2, pp. 708–713 (2012)

  36. Bhatia, P.K., Mann, R.: An approach to measure software reusability of oo design. In: Proceedings of 2nd National Conference on Challenges & Opportunities in Information Technology, pp. 26–30 (2008)

  37. Cho, E.S., Kim, M.S., Kim, S.D.: Component metrics to measure component quality. In: Proceedings of the 8th Asia Pacific Software Engineering Conference (APSEC), Macau, vol. 4–7, pp. 419–426 (2001)

  38. Dhand, P., Dhillon, P.K., Mago, J.: Estimating software reusability from oo metrics using fuzzy logic. Apeejay J. Comput. Sci. Appl. 3, 29–35 (2015)

    Google Scholar 

  39. Sametinger, J.: Software Engineering with Reusable Components. Springer, New York (1997)

    Book  Google Scholar 

  40. Rotaru, O.P., Dobre, M.: Reusability metrics for software components. In: Proceedings of the 3rd ACS/IEEE International Conference on Computer Systems and Applications, Cairo, pp. 24–29 (2005)

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

  42. Padhy N., Satapathy S., Singh R.P.: State-of-the-art object-oriented metrics and its reusability: a decade review. In: Satapathy S., Bhateja V., Das S. (eds.) Smart Computing and Informatics. Smart Innovation, Systems and Technologies, vol 77. Springer, Singapore. https://doi.org/10.1007/978-981-10-5544-7-42

  43. Padhy, N., et al.: Software reusability metrics estimation: algorithms, models and optimization techniques. Comput. Electr. Eng. (2017). https://doi.org/10.1016/j.compeleceng.2017.11.022

    Article  Google Scholar 

  44. Padhy, N., Satapathy, S., Singh, R.: Utility of an object oriented reusability metrics and estimation complexity. Indian J. Sci. Technol. (2017). https://doi.org/10.17485/ijst/2017/v10i3/107289

    Article  Google Scholar 

  45. Pant, M. A brief overview of firefly algorithm. In: Soft Computing: Theories and Applications, pp. 727–738. Springer, Singapore (2018)

  46. Rahmanian, A.A., Ghobaei-Arani, M., Tofighy, S.: A learning automata-based ensemble resource usage prediction algorithm for cloud computing environment. Future Gener. Comput. Syst. 79, 54–71 (2018)

    Article  Google Scholar 

  47. Padhy, N., Singh, R.P., Satapathy, S.C.: Utility of an object-oriented metrics component: examining the feasibility of .Net and C# object-oriented program from the perspective of mobile learning. Int. J. Mob. Learn. Org. (in press)

  48. Padhy, N., Singh, R.P., Satapathy, S.C.: Enhanced evolutionary computing based artificial intelligence model for web-solutions software reusability estimation. Clust. Comput. https://doi.org/10.1007/s10586-017-1558-0 (2017)

    Google Scholar 

  49. Padhy, N., Satapathy, S.C., Singh, R.P.: Estimation of complexity by using an object oriented metrics approach and its proposed algorithm and models. Int. J. Netw. Virtual Org. (in press)

  50. Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques. Morgan Kaufmann, Burlington (2011)

    MATH  Google Scholar 

  51. Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms. Wiley, Hoboken (2004)

    Book  Google Scholar 

  52. Ting, K.M.: An instance-weighting method to induce cost-sensitive trees. IEEE Trans. Knowl. Data Eng. 14(3), 659–665 (2002)

    Article  MathSciNet  Google Scholar 

  53. J. C, Software quality in 2010: a survey of the state of the art,” in Founder and Chief Scientist Emeritus (2010)

  54. Goyal, N., Gupta, D.: Reusability calculation of object oriented software model by analyzing CK metric. Int. J. Adv. Res. Comput. Eng. Technol. (IJARCET) 3(7), 2466–2470 (2014)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Sri Satya Sai University of Technology and Medical Science, SSSUTM, Sehore, India

    Neelamadhab Padhy

  2. Sri Satya Sai University of Technology and Medical Science (SSSUTM), Sehore, India

    R. P. Singh

  3. PV Siddhartha Institute of Engineering and Technology, Kanuru,Vijayawada-520 007, India

    Suresh Chandra Satapathy

Authors
  1. Neelamadhab Padhy
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  2. R. P. Singh
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  3. Suresh Chandra Satapathy
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Corresponding author

Correspondence to Neelamadhab Padhy.

Appendix

Appendix

Sl/no

Keyword

Meaning

1

OO-SM

Object oriented software metrics

2

DT

Decision tree

3

ELM

Extreme learning machine

4

AGA

Adaptive genetic algorithm

5

MARS

Multivariate adaptive regression spline

6

ANN

Artificial neural network algorithms

7

OO-CK

Object-oriented CK metrics

8

NTC

Number of template children

9

DTT

Depth of template tree

10

CC

Cyclomatic complexity

11

RFC

Response for class

12

WMC

Weighted method per class

13

NOC

Number of children

14

LCOM

Lack of cohesion in methods

15

SVM

Support vector machine

16

CBO

Coupling between object

17

LOGR

Logarithmic regression

18

NB

Naive Bayes

19

LM

Levenberg–Marquardt

20

RMSE

Root means square error

21

TN

True negative

22

FP

False positive

23

FN

False negative

24

MAE

Mean absolute error

25

MMRE

Mean magnitude of the relative error

26

SEM

Standard error of the mean

27

MTIF

Method template inheritance factor

28

ATIF

Attribute template inheritance factor

29

CBSD

Component based software design

30

SOM

Self-organizing map

31

LOC

Line of counts

32

OOP

Object-oriented programming

33

WoS

Web of service

34

CKM

Chidamber and kemerer metrics

35

WSDL

Web services description language

36

CKJM

CK java machine based CK metrics retrieval

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Padhy, N., Singh, R.P. & Satapathy, S.C. Cost-effective and fault-resilient reusability prediction model by using adaptive genetic algorithm based neural network for web-of-service applications. Cluster Comput 22 (Suppl 6), 14559–14581 (2019). https://doi.org/10.1007/s10586-018-2359-9

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