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A Frame Work for Business Defect Predictions in Mobiles

by N. Gayatri, S. Nickolas, A. V. Reddy
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 81 - Number 1
Year of Publication: 2013
Authors: N. Gayatri, S. Nickolas, A. V. Reddy
10.5120/13979-1975

N. Gayatri, S. Nickolas, A. V. Reddy . A Frame Work for Business Defect Predictions in Mobiles. International Journal of Computer Applications. 81, 1 ( November 2013), 39-44. DOI=10.5120/13979-1975

@article{ 10.5120/13979-1975,
author = { N. Gayatri, S. Nickolas, A. V. Reddy },
title = { A Frame Work for Business Defect Predictions in Mobiles },
journal = { International Journal of Computer Applications },
issue_date = { November 2013 },
volume = { 81 },
number = { 1 },
month = { November },
year = { 2013 },
issn = { 0975-8887 },
pages = { 39-44 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume81/number1/13979-1975/ },
doi = { 10.5120/13979-1975 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:54:59.042738+05:30
%A N. Gayatri
%A S. Nickolas
%A A. V. Reddy
%T A Frame Work for Business Defect Predictions in Mobiles
%J International Journal of Computer Applications
%@ 0975-8887
%V 81
%N 1
%P 39-44
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Software quality is very highly inflammable area where enormous research has been done and still being continued for effective and exclusive product outcome. Identification of defects or issues in the early phase makes the system to rectify the issues earlier and helps to manage better product outcome within given budget and time which is ultimate goal of every company or project. The issues occurred while coding can be identified by the tools already developed. But if the issues occurred after delivery of the product cannot be identified before and these are called business defects, i. e. , while using the system defects occurs. This paper proposes a frame work for identifying the business defects and prediction for mobiles. The data used here is from Android mobiles. This frame work identifies the information, warnings, errors occurred while using the mobile. And also it gives the percentage of faulty status of the product if these errors occur regularly, so these issues can be rectified and corrected in the future developments of the product. Identification of business defects for mobile logs is the novelty of the paper.

References
  1. K. Ganesan, T. M. Khoshgoftaar, and E. B. Allen, "Case Based Software Quality Prediction," Inter National Journal of Software Engineering and Knowledge Engineering, vol. 10, no. 2,pp. 139-152, 2000.
  2. L Guo,Y. Ma, B. Cukic, and H. Singh,"Robust prediction of fault proneness by Randome Forests", Proceedinds of international sysmposium , Software Reliability Engineering, 2004.
  3. T. M. Khoshgoftaar, and N Seliya , "Comparative assessment of software quality classification Techniques:An empirical case study", Emperical software engineering, vol. 9,no. 3,pp. 229-257,2004.
  4. Iker Gondra,"Applying machine learning to software fault proneness prediction," the journal of system software , vol. 81,pp. 186-195,2008.
  5. N. F. Schneidwind,"Methodology for validating software metrics",IEEE Transactions. Software Engineering, vol. 18,no. 5,pp. 410-422',May 19992.
  6. NASA promise repository http://promise. site. uottawa. ca/SERepository.
  7. Akiyama, F. ,"An example of software system debugging", Information processing, no. 71,pp. 353-379,1971
  8. Halstead ,M. ,"Elements of Software science",Elsiveir,1977.
  9. P. Bellini,I. Bruno,P. Nesi, and D. Rogai,"Comparing fault proneness estimation models",ICECCS,pp. 205-215,2005.
  10. Quah, T. S. , & Thwin, M. (2004). Prediction of software development faults in PL/SQL files using neural network models. Information and Software Technology, 46(8), 519–523.
  11. Stefan Lessmann,B Baesens,Christophe Mues and Swantje Pietsch," Benchmarkinh Classification Models for software defect prediction:A proposed Frame work and Novel Findings", IEEE transactions on Software Engineering, Vol 34, No 4 ,pp 485-496 ,July/August (2008)
  12. Ostrand, T. , Weyuker, E. , & Bell, R. (2005). Predicting the location and number of faults in large software systems. IEEE Transactions on Software Engineering, 31(4), 340–355.
  13. Lanubile, F. , Lonigro, A. , & Visaggio, G. (1995). Comparing models for identifying fault-prone software components. In: Proceedings of the 7th inter international conference on software engineering and knowledge engineering (pp. 312–319), Washington, DC, June.
  14. Ajeet Kumar Pandey and N. K. Goyal , " Predicting Fault-prone Software Module Using Data Mining Technique and Fuzzy Logic", International Journal of Computer and Communication Technology (Special Issue) Vol. 2, Issue 2-4, pp. 56-63 (2010)
  15. Fenton, N. , & Neil, M. (1999). A critique of software defect prediction models. IEEE Transactions on Software Engineering, 25(5), 675–689
Index Terms

Computer Science
Information Sciences

Keywords

Mobile logs threshold defect prediction business defects

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