  Issue resolution and bug-fixing processes are essential in the development of
machine-learning libraries, similar to software development, to ensure
well-optimized functions. Understanding the issue resolution and bug-fixing
process of machine-learning libraries can help developers identify areas for
improvement and optimize their strategies for issue resolution and bug-fixing.
However, detailed studies on this topic are lacking. Therefore, we investigated
the effectiveness of issue resolution for bug-fixing processes in six
machine-learning libraries: Tensorflow, Keras, Theano, Pytorch, Caffe, and
Scikit-learn. We addressed seven research questions (RQs) using 16,921 issues
extracted from the GitHub repository via the GitHub Rest API. We employed
several quantitative methods of data analysis, including correlation, OLS
regression, percentage and frequency count, and heatmap to analyze the RQs. We
found the following through our empirical investigation: (1) The most common
categories of issues that arise in machine-learning libraries are bugs,
documentation, optimization, crashes, enhancement, new feature requests,
build/CI, support, and performance. (2) Effective strategies for addressing
these problems include fixing critical bugs, optimizing performance, and
improving documentation. (3) These categorized issues are related to testing
and runtime and are common among all six machine-learning libraries. (4)
Monitoring the total number of comments on issues can provide insights into the
duration of the issues. (5) It is crucial to strike a balance between
prioritizing critical issues and addressing other issues in a timely manner.
Therefore, this study concludes that efficient issue-tracking processes,
effective communication, and collaboration are vital for effective resolution
of issues and bug fixing processes in machine-learning libraries.
