STREAMLINING SOFTWARE DEVELOPMENT: A COMPARATIVE STUDY OF AI-DRIVEN AUTOMATION TOOLS IN MODERN TECH
The study examines a range of popular AI tools, focusing on their capabilities in code generation, testing, debugging, and project management.By analyzing the features, advantages, and limitations of these tools, the paper provides insights into their effectiveness and their role in shaping the future.Traditional methods of developing software are confronted with limitations in their correctness and efficiency, even if the size and complexity of software systems are constantly growing.New opportunities for automating tasks like testing, debugging, and project management have opened up thanks to artificial intelligence.These are only a few examples of the kind of tasks that may be mechanized with the help of AI, which would open up new avenues for automation.An automated system might also carry out testing and maintenance.With an eye towards improving productivity, decreasing the likelihood of human error, and speeding up the development process, this article will analyze the pros and cons of various technologies.An important takeaway from the study is the evidence that AI technologies are dramatically altering the software development process.These findings are derived on the research's thorough examination of the usability, scalability, and usefulness of such technologies.The findings show that AI has the ability to improve operational efficiency, but they also highlight the limitations due to implementation costs, a lack of learnt knowledge, and other factors.Software development projects will be more efficient and of higher quality if this study is successful in its long-term objective of providing developers and companies with a roadmap to AI automation solutions.Because of this, the study will be able to accomplish its final goal.
Authors
- K. Shah
- Abhishek Trehan
Publication Details
- Journal
- INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & TECHNOLOGY
- Published
- 2024-12-21
- DOI
- https://doi.org/10.34218/ijcet_15_06_138
- Citations
- 2
- Primary Topic
- Big Data and Business Intelligence
- Type
- article
- Field-Weighted Citation Impact
- 1.06