Unlocking the Power of AI for Shift-Left Testing – A Game Changer in Automation |
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| © 2024 by IJCTT Journal | ||
| Volume-72 Issue-12 |
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| Year of Publication : 2024 | ||
| Authors : Karan Ratra, Gaurav Sharma, Dhruv Kumar Seth | ||
| DOI : 10.14445/22312803/IJCTT-V72I12P104 | ||
How to Cite?
Karan Ratra, Gaurav Sharma, Dhruv Kumar Seth, "Unlocking the Power of AI for Shift-Left Testing – A Game Changer in Automation," International Journal of Computer Trends and Technology, vol. 72, no. 12, pp. 25-37, 2024. Crossref, https://doi.org/10.14445/22312803/IJCTT-V72I12P104
Abstract
This paper explores the paradigm shift in the current software development era by test automation facilitated by artificial intelligence (AI). Integrating testing activities as early in the software development lifecycle as possible is the focus of the shift-left testing culture. This article explores AI-powered developments like automated test creation tools, intelligent prioritization of test cases, real-time anomaly detection and enhanced test reporting with AI integration. The advantages of AI in boosting coverage, cutting defect-related expenses, and increasing testing efficiency are also covered as case studies in this paper, with real-world examples from SaaS and automotive companies. Although there is no denying the advantages of automated AI systems, there are concerns about maintaining data accuracy, preventing biases, and controlling implementation costs. The paper concludes by pointing out the possible ways to use AI-enabled early-stage testing solutions, what, if any, benefits they could offer towards the development of software testing procedures, and why it is essential to include ethical and responsible AI in software testing strategies.
Keywords
Shift-left testing, AI-driven test automation, Software quality assurance, Intelligent test prioritization, Automated test generation, Machine learning in software Testing, Ethical AI in testing, AI-powered test coverage, Test automation challenges, Continuous Integration, Agile development, Automated test generation, Software testing innovation, Software testing predictive AI test analytics.
Reference
[1] Muhammad Adnan Khan et al., “Software Defect Prediction Using Artificial Neural Networks: A Systematic Literature Review,” Scientific Programming, vol. 2022, no. 1, pp. 1-10, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Srinivas Aditya Vaddadi et al., “Shift-Left Testing Paradigm Process Implementation for Quality of Software Based on Fuzzy,” Soft Computing, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Donald Firesmith, Four Types of Shift Left Testing, Software Engineering Institute, 2015. [Online]. Available: https://insights.sei.cmu.edu/blog/four-types-of-shift-left-testing/
[4] Prathyusha Nama, “Integrating AI in Testing Automation: Enhancing Test Coverage and Predictive Analysis for Improved Software Quality,” World Journal of Advanced Engineering Technology and Sciences, vol. 13, no. 1, pp. 769-782, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Parameshwar Reddy Kothamali, Vinod Kumar Karne, and Sai Surya Mounika Dandyala, “Integrating AI and Machine Learning in Quality Assurance for Automation Engineering,” International Journal for Research Publication and Seminar, vol. 15, no. 3, pp. 93-102, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Hussam Hourani, Ahmad Hammad, and Mohammad Lafi, “The Impact of Artificial Intelligence on Software Testing,” 2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology, Amman, Jordan, pp. 565-570, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Zeynep Özpolat, Özal Yıldırım, and Murat Karabatak, “Artificial Intelligence-Based Tools in Software Development Processes: Application of ChatGPT,” European Journal of Technique, vol. 13, no. 2, pp. 229-240, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[8] S.P. Udhayakumar, M. Sivasubramanian, “Shift Left: Strengthening the Requirements Elicitation Process for Improving Quality Software in Software Development Projects,” Research Square, pp. 1-13, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Kristian Bjerke-Gulstuen et al., “High Level Test Driven Development – Shift Left,” Agile Processes in Software Engineering and Extreme Programming, vol. 212, pp. 239-247, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Hong Liu et al., “Cleaning Framework for BigData: An Interactive Approach for Data Cleaning,” 2016 IEEE Second International Conference on Big Data Computing Service and Applications (BigDataService), Oxford, UK, pp. 174-181, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Armin Moin et al., “Enabling Automated Machine Learning for Model-Driven AI Engineering,” Arxiv, pp. 1-5, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Wasswa Shafik, “Toward a More Ethical Future of Artificial Intelligence and Data Science,” The Ethical Frontier of AI and Data Analysis, pp. 362-388, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Timothy James DeStefano et al., “What Determines AI Adoption?,” Academy of Management, vol. 2022, no. 1, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Giriraj Kiradoo, “Unlocking the Potential of AI in Business: Challenges and Ethical Considerations,” Recent Progress in Science and Technology, vol. 6, pp. 205-220, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Filippo Riccaa, Alessandro Marchettob, and Andrea Stocco, “A Multi-Year Grey Literature Review on AI-Assisted Test Automation,” Arxiv, pp. 1-20, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Nilofar Mulla, and Naveenkumar Jayakumar, “Role of Machine Learning & Artificial Intelligence Techniques in Software Testing,” Turkish Journal of Computer and Mathematics Education, vol. 12, no. 6, pp. 2913-2921, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Ajay K. Agrawal, Joshua S. Gans, and Avi Goldfarb, “AI Adoption and System-Wide Change,” National Bureau of Economic Research, pp. 1-20, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Jan Ole Johanssen et al., “How do Practitioners Capture and Utilize User Feedback During Continuous Software Engineering?,” 2019 IEEE 27th International Requirements Engineering Conference, Jeju, Korea (South), pp. 153-164, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Liudmila Alekseeva et al., “AI Adoption and Firm Performance: Management versus IT,” SSRN Electron Journal, pp. 1-40, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Bipin Gajbhiye, Anshika Aggarwal, and Shalu Jain, “Automated Security Testing in DevOps Environments Using AI and ML,” International Journal for Research Publication and Seminar, vol. 15, no. 2, pp. 259-271, 2024.
[CrossRef] [Publisher Link]
[21] Francisco G. de Oliveira Neto et al., “Improving Continuous Integration with Similarity-Based Test Case Selection,” Proceedings of the 13th International Workshop on Automation of Software Test, Gothenburg, Sweden, pp. 39-45, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Monika Steidl, Michael Felderer, and Rudolf Ramler, “The Pipeline for the Continuous Development of Artificial Intelligence Models—Current State of Research and Practice,” Journal of Systems and Software, vol. 199, pp. 1-26, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Emilio Ferrara, “Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies,” Sci, vol. 6, no. 1, pp. 1-15, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Bipin Balu Shinde, “Automated Testing in DevOps: Strategies and Tools,” International Journal of Advanced Research in Science, Communication and Technology, vol. 4, no. 4, pp. 550-554, 2024.
[CrossRef] [Publisher Link]
[25] Anna Arias-Duart et al., “Focus! Rating XAI Methods and Finding Biases,” 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Padua, Italy, pp. 1-8, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Michael B. Armstrong, and Richard N. Landers, “An Evaluation of Gamified Training: Using Narrative to Improve Reactions and Learning,” Simulation & Gaming, vol. 48, no. 4, pp. 513-538, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Adel M. Qatawneh, “The Role of Artificial Intelligence in Auditing and Fraud Detection in Accounting Information Systems: Moderating Role of Natural Language Processing,” International Journal of Organizational Analysis, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Roberto Verdecchia, June Sallou, and Luís Cruz, “A Systematic Review of Green AI,” Wires Data Mining and Knowledge Discovery, vol. 13, no. 4, pp. 1-26, 2023.
[CrossRef] [Google Scholar] [Publisher Link]