In the context of mobile apps, bug report management tasks have been shown to be among the most time-consuming and intellectually intensive software maintenance activities. As such, researchers have developed tools to automate the reproduction, validation, and localization of reported bugs. However, one complex, time-consuming, and important task that lacks automated support is the creation of test oracles for reported functional failures that manifest through the GUI. This is challenging task – requiring nuanced, multi-modal reasoning about bug descriptions, affected GUI components, and the characteristics of the related erroneous program state(s).To explore the feasibility of automating this task, we conduct a empirical investigation into how the multi-modal (i.e., text and GUI-related code) reasoning capabilities of Large Language Models (LLMs) can be used to automatically generate assertion-based test oracles for non-crashing, functional failures described in Android app bug reports. Building upon the findings of this study, we construct and evaluate AndroB2O, an automated, LLM-based approach that, given a bug report and the GUI screen associated with the reported failure as inputs, generates failure-based oracles (FBOs) in the form of test assertions. The approach first identifies the GUI elements related to the failure and then defines assertions that aim to confirm the absence of the failure based on the elements’ properties. To evaluate AndroB2O, we create the first dataset of Android bug reports containing test cases with GUI interactions and test oracles that reveal reported failures. The results of our evaluation on 152 failures show that AndroB2O is able to generate FBOs that successfully identify the failure (and hence can confirm its absence) in 61.2% of the cases. We integrated AndroB2O with ReBL, a failure reproduction tool, to evaluate its effectiveness in automated generation of test cases complete with oracles for reported failures, and obtained promising results.

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 key tasks related to ensuring mobile app quality is the reporting, management, and resolution of bug reports. As such, researchers have committed considerable resources toward automating various tasks of the bug management process for mobile apps, such as reproduction and triaging. However, the success of these automated approaches is largely dictated by the characteristics and properties of the bug reports they operate upon. As such, understanding mobile app bug reports is imperative to drive the continued advancement of report management techniques. While prior studies have examined high-level statistics of large sets of reports, we currently lack an in-depth investigation of how the information typically reported in mobile app issue trackers relates to the specific details generally required to reproduce the underlying failures. In this paper, we perform an in-depth analysis of 180 re-producible bug reports systematically mined from Android apps on GitHub and investigate how the information contained in the reports relates to the task of reproducing the described bugs. In our analysis, we focus on three pieces of information: the environment needed to reproduce the bug report, the steps to reproduce (S2Rs), and the observed behavior. Focusing on this information, we characterize failure types, identify the modality used to report the information, and characterize the quality of the information within the reports. We find that bugs are reported in a multi-modal fashion, the environment is not always provided, and S2Rs often contain missing or non-specific enough information. These findings carry with them important implications on automated bug reproduction techniques as well as automated bug report management approaches more generally.