Refine your search
Collections
Co-Authors
Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Motton, Suman
- Software Testing Using Artificial Intelligence : A State of Art
Abstract Views :150 |
PDF Views:0
Authors
Affiliations
1 Research Scholar, Department of Computer Science, Guru Nanak Dev University, Amritsar, IN
2 Associate Professor, Department of Computer Science, Guru Nanak Dev University, Amritsar, IN
1 Research Scholar, Department of Computer Science, Guru Nanak Dev University, Amritsar, IN
2 Associate Professor, Department of Computer Science, Guru Nanak Dev University, Amritsar, IN
Source
Research Cell: An International Journal of Engineering Sciences, Vol 35, No SP (2023), Pagination: 34-43Abstract
Artificial Intelligence (AI) emerges as the latest technology across all software industries as well as domains. It is being leveraged in the field of software testing to ease the automation testing process and deliver more quality outcomes. The application of AI in software testing will make the entire testing process faster, clearer, easier, and within budget. This paper makes an effort to elaborate on the significance of performance testing in the field of software testing using AI. This paper represents a comprehensive review of AI/ML techniques in software testing.Keywords
Software Testing, Artificial Intelligence, Machine Learning, Black-Box Testing, Performance Testing.Full Text
References
- M. Barenkamp, J. Rebstadt, and O. Thomas, “Applications of AI in classical software engineering,” AI Perspect., vol. 2, no. 1, pp. 1–15, 2020, doi: 10.1186/s42467-020-00005-4.
- M. A. Job, “Automating and Optimizing Software Testing using Artificial Intelligence Techniques,” vol. 12, no. 5, pp. 594–603, 2021.
- H. Hourani, A. Hammad, and M. Lafi, “The impact of artificial intelligence on software testing,” 2019 IEEE Jordan Int. Jt. Conf. Electr. Eng. Inf. Technol. JEEIT 2019 - Proc., pp. 565–570, 2019, doi: 10.1109/JEEIT.2019.8717439.
- N. Bhateja and S. Sikka, “Achieving quality in automation of software testing using ai based techniques,” Int. J. Comput. Sci. Mob. Comput., vol. 6, no. 5, pp. 50–54, 2017, [Online]. Available: www.ijcsmc.com
- T. M. King, J. Arbon, D. Santiago, D. Adamo, W. Chin, and R. Shanmugam, “AI for testing today and tomorrow: industry perspectives,” Proc. - 2019 IEEE Int. Conf. Artif. Intell. Testing, AITest 2019, pp. 81–88, 2019, doi: 10.1109/AITest.2019.000-3.
- V. H. S. Durelli, R. S. Durelli, S. S. Borges, A. T. Endo, and M. M. Eler, “Machine Learning Applied to Software Testing : A Systematic Mapping Study,” pp. 1–24, 2019, doi: 10.1109/TR.2019.2892517.
- N. Mulla and N. Jayakumar, “Role of Machine Learning & Artificial Intelligence Techniques in Software Testing,” vol. 12, no. 6, pp. 2913–2921, 2021.
- S. K. Alferidah and S. Ahmed, “Automated Software Testing Tools,” 2020 Int. Conf. Comput. Inf. Technol. ICCIT 2020, pp. 183–186, 2020, doi: 10.1109/ICCIT-144147971.2020.9213735.
- N. Ahmed, “Old Testing Automation techniques are lagging : Artificial Intelligence has the pace,” pp. 1–5, 2017.
- K. Sugali, C. Sprunger, and V. N. Inukollu, “Software Testing: Issues and Challenges of Artificial Intelligence & Machine Learning,” Int. J. Artif. Intell. Appl., vol. 12, no. 1, pp. 101–112, 2021, doi: 10.5121/ijaia.2021.12107.
- A. Sundaram, “Technology Based Overview on Software Testing Trends, Techniques, and Challenges,” Int. J. Eng. Appl. Sci. Technol., vol. 6, no. 1, pp. 0–5, 2021, doi: 10.33564/ijeast.2021.v06i01.011.
- D. Marijan and A. Gotlieb, “Software testing for machine learning,” AAAI 2020 - 34th AAAI Conf. Artif. Intell., pp. 13576–13582, 2020, doi: 10.1609/aaai.v34i09.7084.
- Z. Khaliq, S. U. Farooq, and K. D. Ashraf, “Artificial Intelligence in Software Testing : Impact, Problems, Challenges and Prospect,” 2022, [Online]. Available: http://arxiv.org/abs/2201.05371
- D. Larkman, M. Mahammadian, and B. Balachandran, “General Application of a Decision Support Framework for Software Testing Using Artificial Intelligence,” pp. 53–63, 2010.
- P. Srivastava and K. Tai-hoon, “Application of genetic algorithms in software testing,” Adv. Mach. Learn. Appl. Softw. Eng., no. November 2009, pp. 287–317, 2009, doi: 10.4018/978-1-59140-941-1.ch012.
- A. R. Lenz, A. Pozo, and S. R. Vergilio, “Engineering Applications of Arti fi cial Intelligence Linking software testing results with a machine learning approach,” Eng. Appl. Artif. Intell., vol. 26, no. 5–6, pp. 1631–1640, 2013, doi: 10.1016/j.engappai.2013.01.008.
- D. Chhillar and K. Sharma, “Proposed T-Model to cover 4S quality metrics based on empirical study of root cause of software failures,” Int. J. Electr. Comput. Eng., vol. 9, no. 2, p. 1122, 2019, doi: 10.11591/ijece.v9i2.pp1122-1130.
- J. Kahles, J. Torronen, T. Huuhtanen, and A. Jung, “Automating root cause analysis via machine learning in agile software testing environments,” Proc. - 2019 IEEE 12th Int. Conf. Softw. Testing, Verif. Validation, ICST 2019, no. June, pp. 379–390, 2019, doi: 10.1109/ICST.2019.00047.
- J. Hu, W. Yi, N. W. Chen, Z. J. Gou, and W. Shuo, “Artificial neural network for automatic test oracles generation,” Proc. - Int. Conf. Comput. Sci. Softw. Eng. CSSE 2008, vol. 2, no. 05, pp. 727–730, 2008, doi: 10.1109/CSSE.2008.774.
- Vineeta, A. Singhal, and A. Bansal, “Generation of test oracles using neural network and decision tree model,” Proc. 5th Int. Conf. Conflu. 2014 Next Gener. Inf. Technol. Summit, pp. 313–318, 2014, doi: 10.1109/CONFLUENCE.2014.6949311.
- F. Wang, L. W. Yao, and J. H. Wu, “Intelligent test oracle construction for reactive systems without explicit specifications,” Proc. - IEEE 9th Int. Conf. Dependable, Auton. Secur. Comput. DASC 2011, pp. 89–96, 2011, doi: 10.1109/DASC.2011.39.