Nadeeshan De Silva

Substantial research effort has been devoted to developing techniques for automated program repair (APR) that suggest patches for localized buggy code -- and more recent techniques have begun to leverage the capabilities of code-centric large language models (LLMs). However, the scope and diversity of bugs to which these techniques have historically been applied are limited. In particular, the research community currently lacks a comprehensive understanding of the performance of APR techniques on bugs that arise in UI-centric programs, such as mobile apps. Bugs in UI centric programs carry with them unique challenges, including (i) the need to reason across interconnected subroutines that connect presentation and program logic, (ii) event-driven programming paradigms, and (iii) the need to reason about program state through cues in the UI. In this paper, we investigate the effectiveness of existing APR techniques when applied to fix bugs in UI-centric programs - specifically Android applications. To explore this phenomenon, we conduct a comprehensive empirical study with five existing program repair techniques (including those that utilize LLMs) on a hybrid dataset including 46 synthetic bugs, generated via MDroid+, an Android-specific mutation tool, and 50 real bugs systematically mined from issue reports of 23 popular Android applications. Our findings illustrate important current limitations in resolving UI-related issues in mobile apps. We synthesize these results to form a taxonomy of the limitations of existing program repair techniques. This taxonomy outlines key limitations and can inform future research efforts in designing automated program repair tools for UI-centric bugs in mobile applications.

One of the most important tasks related to managing bug reports is localizing the fault so that a fix can be applied. As such, prior work has aimed to automate this task of bug localization by formulating it as an information retrieval problem, where potentially buggy files are retrieved and ranked according to their textual similarity with a given bug report. However, there is often a notable semantic gap between the information contained in bug reports and identifiers or natural language contained within source code files. For user-facing software, there is currently a key source of information that could aid in bug localization, but has not been thoroughly investigated - information from the graphical user interface (GUI). In this paper, we investigate the hypothesis that, for end user-facing applications, connecting information in a bug report with information from the GUI, and using this to aid in retrieving potentially buggy files, can improve upon existing techniques for text retrieval-based bug localization. To examine this phenomenon, we conduct a comprehensive empirical study that augments four baseline text-retrieval techniques for bug localization with GUI interaction information from a reproduction scenario to (i) filter out potentially irrelevant files, (ii) boost potentially relevant files, and (iii) reformulate text-retrieval queries. To carry out our study, we source the current largest dataset of fully-localized and reproducible real bugs for Android apps, with corresponding bug reports, consisting of 80 bug reports from 39 popular open-source apps. Our results illustrate that augmenting traditional techniques with GUI information leads to a marked increase in effectiveness across multiple metrics, including a relative increase in Hits@10 of 13--18%. Additionally, through further analysis, we find that our studied augmentations largely complement existing techniques, pushing additional buggy files into the top-10 results while generally preserving top ranked files from the baseline techniques.