Fatima Aziz obtained a Master’s degree. She successfully defended her thesis, titled “A Lexicon-based Approach for Software Bug Severity Classification“, conducted under the supervision of Asst. Prof. Dr. Martin Žnidaršič.

Congratulations!

Abstract:

Bug severity classification is a critical and time-consuming aspect of the software bug resolution process, as the severity of a reported bug influences its urgency to fix it. This task is often automated using supervised machine learning methods; however, there is a notable lack of dedicated lexicons for this purpose. In this thesis, we assessed the potential usefulness of developing such a resource, implemented it and compared its performance with classic machine learning approaches and general purpose lexicons used in text classification. We also proposed and tested a novel lexicon development approach based in the domain of software bug reports. In our empirical assessment, we used publicly available datasets of bug reports for Firefox and Eclipse. The results indicate that our lexicon approach achieved a comparable F1-score to classic machine learning models. Our experiments revealed that lexicon-based approaches were effective in most of the experiments, while machine learning methods performed better in the experiment that had a more balanced data distribution. The findings highlighted the dataset-dependent nature of classification performance and also indicated the unexpected usefulness of general sentiment analysis lexicons. These results suggest that tailored lexicon-based approaches are a valuable alternative to machine learning techniques for bug severity classification and could potentially reduce the need for large labeled training sets.