Development and Testing is a very important part of any product development
cycle. There exist numerous modules that need to be tested in a product or software after
each build. Addition, modification or deletion of new procedures requires thorough testing
of the complete product. Test scripts are generated for the ease of testing. It is observed
that most of the procedures in test scripts are repeated. Converting natural language query
into test scripts reduces the effort of the test engineer by finding relevant procedures in
already existing database. The proposed system accepts a natural language query and
converts the query into an executable test code using various NLP techniques. This paper
explain two methods that are used to generate test script from Natural language query.
Index Terms: Natural Language query; Test Script generation; Intent Recognition
1. Mark Nederhof, Giorgio Satta. “Probabilistic Parsing”, International Journal on Advanced Research in Computer Science and Software Engineering,
Volume 3, Issue 8, August 2012, pp 45-49.
2. Barr Hirrel. “Probabilistic Parsing with Random Variables and Probability Spaces”, International Journal of Computer Sciences and Statistics, Vol 11,
Issue 2, March 2014, pp 16-20.
3. William Chen, Sebastian Chu. “Dependency Parser using Neural Networks”, Journal of Machine Learning Research, Vol 25, January 2015, pp 7-13.
4. McCulloch, Rosenblatt. “Deep Learning Techniques for Syntactic Parsing”, International Conference on Machine Learning, Vol 13. Issue 3. April 2013,
5. Alexander Grothendieck. “Attention for Neural Parsing”, Neural Computation Society, Vol 8, Issue 2, July 2009, pp 53-60.
6. Christopher Manning. “A Maximum Entropy Model for Part of Speech Tagging”, Journal of the ACM, Vol 16, Issue 4, December 2011, pp 267-283.
7. Hugo Larochelle, Pedro Domingos. “Part of Speech Tagging using Lexicons and Feed Forward Neural Networks”, Engineering Applications of Artificial
Intelligence, Vol 32, Issue 3, August 2012, pp 541-553.
8. Yoshua Bengio, Michael Forcada, “Improving Part of Speech Tagging using Recurrent Neural Networks”, Neural Information Proceeding Systems, Vol
14, December- 2016, pp 78-97.
9. Oshita, M. (2010). Generating animation from natural language texts and semantic analysis for motion search and scheduling. The Visual Computer,
10. Harding, James A., and Jonathan I. McCormack. "Method and apparatus for the modeling and query of database structures using natural language-like
constructs." U.S. Patent No. 5, 27 Feb. 1996, pp 495-604.
11. Zhang, Fan, et al. "Real-time implementation of an intent recognition system for artificial legs." Engineering in Medicine and Biology Society, EMBC,
2011 Annual International Conference of the IEEE. IEEE, 2011.