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Analysis Study Research Paper | Computer Science and Information Technology | India | Volume 11 Issue 7, July 2022 | Rating: 5.2 / 10
Utilizing Software Analytics and Delving into Software Repositories to Enhance Processes in AI Development
Abstract: In the rapidly evolving domain of artificial intelligence (AI) development, the imperative for efficient and effective processes reigns supreme. To meet these pressing demands, the emergence of software analytics and mining software repositories (MSR) stands as indispensable tools for enhancing the process of AI development. Software analytics entails the systematic analysis of software data to derive insights and make data-driven decisions. It encompasses a range of techniques including data collection, preprocessing, analysis, visualization, predictive modeling, and machine learning. Through the utilization of software analytics, organizations can unveil concealed patterns, pinpoint bottlenecks, and refine their development methodologies. Mining software repositories involves the extraction and analysis of data from various software development artifacts such as version control systems, bug tracking systems, mailing lists, and code repositories. These repositories harbor invaluable information concerning the development lifecycle, including source code, bug reports, and developer discussions. By delving into this trove of data, organizations can gain profound insights into their development practices and pinpoint areas ripe for improvement. Process improvement is of paramount importance in AI development due to the inherent complexities of AI projects involving intricate algorithms, vast datasets, and multidisciplinary teams. Effectively managing and optimizing the development process is crucial. Leveraging software analytics and MSR techniques empowers organizations to streamline workflows, identify inefficiencies, and bolster the overall quality and productivity of AI development endeavors. Our exploration will encompass the fundamental components and methodologies of software analytics, delve into the diverse array of data sources and techniques employed in mining software repositories, and underscore the relevance of these practices in AI development. Additionally, we will scrutinize the significance of process enhancement in AI initiatives and elucidate how software analytics and MSR can be harnessed to elevate development methodologies. Real-world case studies and examples will be presented to elucidate the practical implementation and benefits of these approaches. By harnessing the capabilities of software analytics and mining software repositories, organizations stand to unearth valuable insights, refine their AI development processes, and ultimately propel innovation and success in the realm of artificial intelligence.
Keywords: Artificial Intelligence (AI), Software Analytics, Mining Software Repositories, Process Improvement, Data-driven Decisions
Edition: Volume 11 Issue 7, July 2022,
Pages: 1953 - 1957