Junayed Mahmud

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.

Bug report management is a costly software maintenance process comprised of several challenging tasks. Given the UI-driven nature of mobile apps, bugs typically manifest through the UI, hence the identification of buggy UI screens and UI components (Buggy UI Localization) is important to localizing the buggy behavior and eventually fixing it. However, this task is challenging as developers must reason about bug descriptions (which are often low-quality), and the visual or code-based representations of UI screens. This paper is the first to investigate the feasibility of automating the task of Buggy UI Localization through a comprehensive study that evaluates the capabilities of one textual and two multi-modal deep learning (DL) techniques and one textual unsupervised technique. We evaluate such techniques at two levels of granularity, Buggy UI Screen and UI Component localization. Our results illustrate the individual strengths of models that make use of different representations, wherein models that incorporate visual information perform better for UI screen localization, and models that operate on textual screen information perform better for UI component localization – highlighting the need for a localization approach that blends the benefits of both types of techniques. Furthermore, we study whether Buggy UI Localization can improve traditional buggy code localization, and find that incorporating localized buggy UIs leads to improvements of 9%-12% in Hits@10.

In automated testing, test oracles are used to determine whether software behaves correctly on individual tests by comparing expected behavior with actual behavior, revealing incorrect behavior. Automatically creating test oracles is a challenging task, especially in domains where software behavior is difficult to model. Mobile apps are one such domain, primarily due to their event-driven, GUI-based nature, coupled with significant ecosystem fragmentation. This paper takes a step toward automating the construction of GUI-based test oracles for mobile apps, first by characterizing common behaviors associated with failures into a behavioral taxonomy, and second by using this taxonomy to create automated oracles. Our taxonomy identifies and categorizes common GUI element behaviors, expected app responses, and failures from 124 reproducible bug reports, which allow us to better understand oracle characteristics. We use the taxonomy to create app-independent oracles and report on their generalizability by analyzing an additional dataset of 603 bug reports. We also use this taxonomy to define an app-independent process for creating automated test oracles, which leverages computer vision and natural language processing, and apply our process to automate five types of app-independent oracles. We perform a case study to assess the effectiveness of our automated oracles by exposing them to 15 real-world failures. The oracles reveal 11 of the 15 failures and report only one false positive. Additionally, we combine our oracles with a recent automated test input generation tool for Android, revealing two bugs with a low false positive rate. Our results can help developers create stronger automated tests that can reveal more problems in mobile apps and help researchers who can use the understanding from the taxonomy to make further advances in test automation.

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.