Detecting inaccurate stack traces in bug reports
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Date
2022
Authors
Bheree, Meher K.
University of Lethbridge. Faculty of Arts and Science
Journal Title
Journal ISSN
Volume Title
Publisher
Lethbridge, Alta. : University of Lethbridge, Dept. of Mathematics and Computer Science
Abstract
The generally held opinion in the software engineering community is that incorrect information
in bug reports is often found in non-structural fields such as bug descriptions
and steps to reproduce. However, structural information such as software stack traces can
be inaccurate, increasing the project costs due to wasted time in fixed faults. Regarding
the occurrence of inaccurate stack traces in bug reports, there is little empirical evidence.
Therefore, we seek to provide such evidence by conducting an empirical study on the bug
reports containing stack traces from the Eclipse and Apache projects.
We propose an approach to classify the stack traces as either “Accurate” or “Inaccurate”
by comparing the file names found in a stack trace in a bug report and the corresponding
commit history for its fix. Thus, we determine the occurrence of inaccurate stack traces and
identify the frequently occurring exception types that appears in the inaccurate stack traces
for each project.
Finally, we investigate training three supervised machine learning algorithms (Naive
Bayes, Support Vector Machines and Logistic Regression), on features extracted from stack
traces to create recommender that labels stack traces in bug reports as either ”Accurate” or
”Inaccurate”. The Logistic Regression algorithm was found to perform better with a F1-
score up to 87% for the investigated Eclipse projects and 96% for the investigated Apache
projects.
Description
Keywords
Stack traces , Bug reports , Bug report quality , Crash reports , Software debugging , Inaccurate information , Exceptions , Recommender creation