Open Access   Article Go Back

A Systematic Literature Survey for Detecting Ambiguity in SRS Using Artificial Intelligence

Shruti Mishra1 , Vijay Birchha2 , Bhawna Nigam3

Section:Survey Paper, Product Type: Journal Paper
Volume-6 , Issue-12 , Page no. 383-387, Dec-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i12.383387

Online published on Dec 31, 2018

Copyright © Shruti Mishra, Vijay Birchha, Bhawna Nigam . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

View this paper at   Google Scholar | DPI Digital Library

How to Cite this Paper

  • IEEE Citation
  • MLA Citation
  • APA Citation
  • BibTex Citation
  • RIS Citation

IEEE Style Citation: Shruti Mishra, Vijay Birchha, Bhawna Nigam, “A Systematic Literature Survey for Detecting Ambiguity in SRS Using Artificial Intelligence,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.383-387, 2018.

MLA Style Citation: Shruti Mishra, Vijay Birchha, Bhawna Nigam "A Systematic Literature Survey for Detecting Ambiguity in SRS Using Artificial Intelligence." International Journal of Computer Sciences and Engineering 6.12 (2018): 383-387.

APA Style Citation: Shruti Mishra, Vijay Birchha, Bhawna Nigam, (2018). A Systematic Literature Survey for Detecting Ambiguity in SRS Using Artificial Intelligence. International Journal of Computer Sciences and Engineering, 6(12), 383-387.

BibTex Style Citation:
@article{Mishra_2018,
author = {Shruti Mishra, Vijay Birchha, Bhawna Nigam},
title = {A Systematic Literature Survey for Detecting Ambiguity in SRS Using Artificial Intelligence},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {12 2018},
volume = {6},
Issue = {12},
month = {12},
year = {2018},
issn = {2347-2693},
pages = {383-387},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=3348},
doi = {https://doi.org/10.26438/ijcse/v6i12.383387}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i12.383387}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=3348
TI - A Systematic Literature Survey for Detecting Ambiguity in SRS Using Artificial Intelligence
T2 - International Journal of Computer Sciences and Engineering
AU - Shruti Mishra, Vijay Birchha, Bhawna Nigam
PY - 2018
DA - 2018/12/31
PB - IJCSE, Indore, INDIA
SP - 383-387
IS - 12
VL - 6
SN - 2347-2693
ER -

VIEWS PDF XML
372 238 downloads 113 downloads
  
  
           

Abstract

Research in recent years has shown integration amongst the significant and dynamic areas of software engineering and semantic web engineering. The success of any software system is depending on how well it meets the requirements of the stakeholders. A software requirement specification written in natural languages, are basically ambiguous, which makes the documentation unclear. Due to unclear requirements, software developers develop software, which is different from the expected software based on the customer needs. Therefore, well documented requirements should be unambiguous and it is possible only when it has only one meaning.The main purpose of this research is to propose a technique that is able to detect ambiguity in software requirements specification document automatically using artificial intelligence. To validate the outcome of the proposed work, generated result of the proposed work will be evaluated and validated by making the comparison between the proposed prototype results, previous ambiguity detection framework and human-generated results to decide how the proposed work is more efficient and reliable for ambiguity detection.

Key-Words / Index Term

Software Requirements Specification, Artificial Intelligence, Deep Learning, Ambiguity Detection

References

[1] R. Beniwal. "Analysis of Testing Metrics for Object Oriented Applications." In Computational Intelligence & Communication Technology (CICT), 2015 IEEE International Conference on, pp. 41-46. IEEE, 2015.
[2] K. Sharma, R. Garg, C. K. Nagpal, and R. K. Garg. "Selection of optimal software reliability growth models using a distance-based approach." Reliability, IEEE Transactions on 59, no. 2, pp. 266-276, 2010.
[3] K. S. Kaswan, S. Choudhary, and K. Sharma. "Software Reliability Modeling using Soft Computing Techniques: Critical Review." J Inform Tech SoftwEng 5, no. 144, 2015.
[4] R. Studer, R. Benjamins, and D. Fensel, “Knowledge engineering: Principles and methods,” Data & Knowledge Engineering 25, no.1, pp. 161–198, 1998.
[5] HJ Happel and S Seedorf. "Applications of ontologies in software engineering." In Proc. of Workshop on Sematic Web Enabled Software Engineering"(SWESE) on the ISWC, pp. 5-9. 2006.
[6] Y Zhao, J Dong and T Peng, “Ontology classification for semantic-webbased software engineering,Services Computing, IEEE Transactions on Services Computing”, Vol. 2, No. 4, pp. 303-317, 2009.
[7] D Gaševiü, N Kaviani and M Milanoviü. "Ontologies and software engineering." In Handbook on Ontologies, pp. 593-615. Springer Berlin Heidelberg, 2009.
[8] M.P.S Bhatia, A Kumar, and R Beniwal, “Ontologies for Software Engineering: Past, Present, and Future,”pp 232-238 IEEE , 2016.
[9] M.P.S. Bhatia, R. Beniwal and A. Kumar, "An ontology-based framework for automatic detection and updation of requirement specifications." In Contemporary Computing and Informatics (IC3I), 2014 International Conference on, pp. 238-242. IEEE, 2014.
[10] M.P.S. Bhatia, A. Kumar, and R. Beniwal, "Ontology Based Framework for Automatic Software’s Documentation." In Computing for Sustainable Global Development, 2015 2nd International Conference on, pp. 725-728. IEEE. 2015.
[11] B S. Dogra, K Kaur, and D Kaushi. Enterprise Information Systems in 21st Century: Opportunities and Challenges. New Delhi: Deep and Deep Publications, 2009.
[12] S Armitage, “Software Requirement Specification.” 1996. http://www4.informatik.tu-muenchen.de/proj/va/SRS.pdf (Last accessed date: October, 2015)
[13] Navarro-Almanza, Guillermo Licea "Towards Supporting Software Engineering Using Deep Learning: A Case of Software Requirements Classification" Software Engineering Research and Innovation (CONISOFT), 2017 5th International Conference in, IEEE 2017
[14] Yu Kai, Jia Lei, Chen Yuqiang et al., "Deep Learning: Yesterday Today and Tomorrow[J]", Journal of Computer Research and Development, vol. 50, no. 9, pp. 1799-1804, 2013.
[15] SC. Levinson, "Pragmatics (Cambridge textbooks in linguistics)." 1983.
[16] A Nigam, N Arya, B Nigam and D Jain. "Tool for Automatic Discovery of Ambiguity in Requirements," IEEE 2012.
[17] Sandhu, G. and S. Sikka. State-of-art practices to detect inconsistencies and ambiguities from software requirements. in Computing, Communication & Automation (ICCCA), International Conference in,IEEE 2015.
[18] A. Aamodt, E. Plaza, "Case-Based Reasoning: Foundational Issues Methodological Variations and System Approaches", Artificial Intelligence Comm., vol. 7, no. 1, pp. 39-59, 1994.
[19] Hagal, M.A. and S.F. Alshareef. A systematic approach to generate and clarify consistent requirements. in IT Convergence and Security (ICITCS), International Conference in,IEEE 2013.
[20] D.M. Berry, E. Kamsties and M.M. Krieger "From contract drafting to software specification: Linguistic sources of ambiguity-a handbook version 1.0."(2003). http://cs.uwaterloo.ca/~dberry/handbook/ambiguityHandbook.pdf