Towards Enhancing IR-Based Bug Localization Leveraging Texts and Multimedia from Bug Reports

Software bug reports often miss critical information, delaying their resolution. The last decade has seen a growing trend of combining textual and multimedia information from software artifacts (e.g., bug reports, and programming questions) to support various software engineering tasks (e.g., duplicate bug report detection, and bug reproduction). However, none of the studies performs a fine-grained analysis of the multimedia information attached to bug reports. Hence, it is not clear what their attached images or videos contain or whether they could help identify software bugs or errors automatically. In this paper, we conduct a preliminary study that investigates the presence or prevalence of key elements in 1,469 images attached to 1,000 bug reports and demonstrate their potential to support IR-based bug localization. We have several interesting findings. First, our analysis suggests that the attached images to visual bug reports contain a mix of UI components, programming components, and regular text. Second, our analysis using an LLM (e.g., GPT4o) suggests that it can extract the key elements from the attached images effectively, posing a suitable alternative to human annotators. Finally, our experiments suggest that the multimedia information extracted from the attached images can enhance the performance of a traditional technique for bug localization by improving 34.06 % of its search queries.