ImageR: Enhancing Bug Report Clarity by Screenshots

TL;DR

ImageR is an AI-powered tool that assesses and recommends relevant screenshots in bug reports to improve clarity, reduce communication delays, and streamline issue resolution, supported by a new labeled dataset and empirical validation.

Contribution

We introduce ImageR, an AI model that predicts when and what type of screenshots to include in bug reports, along with a curated dataset for benchmarking image processing in developer communication.

Findings

F1-score of 0.76 in image necessity detection

75% user approval for recommendations

Curated dataset of 6,235 labeled bug reports

Abstract

In issue-tracking systems, incorporating screenshots significantly enhances the clarity of bug reports, facilitating more efficient communication and expediting issue resolution. However, determining when and what type of visual content to include remains challenging, as not all attachments effectively contribute to problem-solving; studies indicate that 22.5% of images in issue reports fail to aid in resolving the reported issues. To address this, we introduce ImageR, an AI model and tool that analyzes issue reports to assess the potential benefits of including screenshots and recommends the most pertinent types when appropriate. By proactively suggesting relevant visuals, ImageR aims to make issue reports clearer, more informative, and time-efficient. We have curated and publicly shared a dataset comprising 6,235 Bugzilla issues, each meticulously labeled with the type of image attachment, providing a valuable resource for benchmarking and advancing research in image processing within developer communication contexts. To evaluate ImageR, we conducted empirical experiments on a subset of these reports from various Mozilla projects. The tool achieved an F1-score of 0.76 in determining when images are needed, with 75% of users finding its recommendations highly valuable. By minimizing the back-and-forth communication often needed to obtain suitable screenshots, ImageR streamlines the bug reporting process. Furthermore, it guides users in selecting the most effective visual documentation from ten established categories, potentially reducing resolution times and improving the quality of bug documentation. ImageR is open-source, inviting further use and improvement by the community. The labeled dataset offers a rare resource for benchmarking and exploring image processing in the context of developer communication.