Abstract:
"Prediction of the software bug fixing time is a crucial task during sprint planning and
preplanning in the software development world. If more than the required time is allocated the
software developers may be idle else if less time is allocated the developers may not have
enough time to complete the task and this may hinder the other tasks as well. Knowing the
exact bug fixing time not only helps software developers but also program managers and senior
management as they can get a clear idea about how much time it will take to solve all the
software bugs in that particular development cycle, which may in return help them plan their
costs and resources better. This will also help them to plan the deadlines better.
With the help of machine learning techniques, the author wants to automate the process of
software bug fixing time. Using natural language processing techniques for the bug titles the
author plans to create a deep learning model. Sentence embeddings will be used to capture the
semantic meanings of the bug titles and will be used with a Convolutional neural networks
(CNN) model for the predictions. The aim of the research was to build a model to predict the
bug fixing time making the lives easier for anyone who wants to predict a software bug fixing
time.
The assessments of the bug fixing time prediction's implementation yield high numbers,
proving that the strategy was successful. This widens the scope of software issues while also
relieving people of the effort of manually estimating the time needed to resolve bugs."
