MM-IssueLoc: A Controlled Benchmark for Evaluating Visual Evidence in Multimodal Repository-Level Issue Localization
Researchers released MM-IssueLoc, a new benchmark designed to evaluate how well AI models use visual evidence like screenshots to locate code bugs in repositories.
- MM-IssueLoc is a controlled benchmark for multimodal repository-level issue localization.
- The dataset includes 652 issue-PR instances across 23 programming languages.
- It separates bug localization from code repair to isolate the value of visual input.
- The goal is to measure if visual evidence like screenshots improves model performance.
Current benchmarks for repository-level issue localization typically focus on text, ignoring the visual evidence such as screenshots and error logs that developers often include in bug reports. This creates a gap in understanding how multimodal models utilize visual data to find faults.
The new MM-IssueLoc benchmark addresses this by isolating the localization task from the patch generation process. This separation allows researchers to determine if visual inputs actually help, hinder, or are ignored by the model during analysis.
The dataset comprises 652 issue and pull request instances spanning 23 different programming languages. It provides a controlled environment to strictly assess the impact of visual evidence on the performance of software engineering tools.
Better evaluation of AI tools could lead to more effective debugging assistants that understand screenshots and logs.
Improved AI coding agents can reduce the time developers spend on troubleshooting and maintenance.
Highlights progress in evaluation methodologies for coding AI, a key sector for investment.
- Issue Localization
- The task of identifying the specific files or code segments responsible for a reported bug.
- Multimodal
- Involving or using multiple modes of communication or expression, such as text and images together.
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