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.

Software bug reports are important project artifacts that evolve throughout the life of a software project. Software bugs are issues that are reported by users when these issues hinder their work. Software projects evolve over time as bugs are addressed and new features are added. Managing bugs can be a significant challenge as a project manager generally needs to be aware of all the bug reports for the current version, and this can be even more challenging when the number of bug reports becomes large. It is preferable that a developer new to a project improves her knowledge with the project along with the bug reports during working on it, which is likely to help her avoid or handle the reported issues. In this paper, we propose a prototype that assists developers review a project's bug reports by interactively visualizing insightful information regarding the bug reports using topic analysis. In addition, in order to reduce developers' time and efforts when studying a bug report, the proposed prototype also provides an extractive summary visualization of each bug report. In this research, it is shown that our proposed prototype performs better in terms of precision, recall, and F-measure than a baseline approach that uses time-sensitive keyword extraction.