Harnessing Artificial Intelligence and Machine Learning in Software Engineering: Transformative Approaches for Automation, Optimization, And Predictive Analysis

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@article{1702590,
      author = {Jyotipriya Das},
      title = {Harnessing Artificial Intelligence and Machine Learning in Software Engineering: Transformative Approaches for Automation, Optimization, And Predictive Analysis},
      journal = {Iconic Research And Engineering Journals},
      year = {2021},
      volume = {4},
      number = {8},
      pages = {92-101},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1702590.pdf},
      abstract = {Artificial Intelligence (AI) and Machine Learning (MI) have been changing at a very fast pace and for software engineering, such advancements provide new innovative methods to solve multiple pending problems. This paper focuses on how SE involves the adoption of AI and ML fully maximized in automating tasks, improving resources, and providing analytical predictions for decision-making.
It starts first with the analysis of conventional approaches for software development and the problems associated therewith, particularly in the development of large-scale dynamic and heterogeneous systems. This is followed by raising a discussion of Artificial Intelligence approaches, including natural language processing in requirements engineering, generative models in code generation, and reinforcement learning in testing. Moreover, resource allocation is investigated using ML algorithms and the results demonstrate an improved performance over the existing methods Using the same concept, generalizing ML techniques for known tasks such as defect prediction, and anomaly detection exhibit a far better performance than previous techniques.
The approach used is therefore systematic, through a combination of a literature review of the academic and industrial applications as well as case studies for the years 2015-2020. These include successful use cases involving debugging with IBM?s Watson and others and TensorFlow for optimizing deployment pipelines in Google. Metrics show a 30?50% improvement in automation steps as well as 70% accuracy of the prediction of maintenance.
To summarize, the presented results qualify AI and machine learning as the forces that can significantly advance software engineering practices. It was also observed that through these technologies understanding and development time for applications can be reduced along with costs while also enhancing reliability as well as adaptability of the generated software. In conclusion the paper highlights conclusion and suggestion for furture research, ethical impacts and strong AI governance in the software engineering.},
      month = {February},
  }