2 research outputs found
Using Word Embedding and Convolution Neural Network for Bug Triaging by Considering Design Flaws
Resolving bugs in the maintenance phase of software is a complicated task.
Bug assignment is one of the main tasks for resolving bugs. Some Bugs cannot be
fixed properly without making design decisions and have to be assigned to
designers, rather than programmers, to avoid emerging bad smells that may cause
subsequent bug reports. Hence, it is important to refer some bugs to the
designer to check the possible design flaws. Based on our best knowledge, there
are a few works that have considered referring bugs to designers. Hence, this
issue is considered in this work. In this paper, a dataset is created, and a
CNN-based model is proposed to predict the need for assigning a bug to a
designer by learning the peculiarities of bug reports effective in creating bad
smells in the code. The features of each bug are extracted from CNN based on
its textual features, such as a summary and description. The number of bad
samples added to it in the fixing process using the PMD tool determines the bug
tag. The summary and description of the new bug are given to the model and the
model predicts the need to refer to the designer. The accuracy of 75% (or more)
was achieved for datasets with a sufficient number of samples for deep
learning-based model training. A model is proposed to predict bug referrals to
the designer. The efficiency of the model in predicting referrals to the
designer at the time of receiving the bug report was demonstrated by testing
the model on 10 projects