Automated bug reproduction is a challenging task, with existing tools typically relying on textual steps-to-reproduce, videos, or crash logs in bug reports as input. However, images provided in bug reports have been overlooked. To address this gap, this paper presents an empirical study investigating the necessity of including images as part of the input in automated bug reproduction. We examined the characteristics and patterns of images in bug reports, focusing on (1) the distribution and types of images (e.g., UI screenshots), (2) documentation patterns associated with images (e.g., accompanying text, annotations), and (3) the functional roles they served, particularly their contribution to reproducing bugs. Furthermore, we analyzed the impact of images on the performance of existing tools, identifying the reasons behind their influence and the ways in which they can be leveraged to improve bug reproduction. Our findings reveal several key insights that demonstrate the importance of images in supporting automated bug reproduction. Specifically, we identified six distinct functional roles that images serve in bug reports, each exhibiting unique patterns and specific contributions to the bug reproduction process. This study offers new insights into tool advancement and suggests promising directions for future research.

Numerous approaches employing various strategies have been developed to test the graphical user interfaces (GUIs) of mobile apps. However, traditional GUI testing techniques, such as random and model-based testing, primarily focus on generating test sequences that excel in achieving high code coverage but often fail to act as effective test oracles for noncrash functional (NCF) bug detection. To tackle these limitations, this study empirically investigates the capability of leveraging large language models (LLMs) to be test oracles to detect NCF bugs in Android apps. Our intuition is that the training corpora of LLMs, encompassing extensive mobile app usage and bug report descriptions, enable them with the domain knowledge relevant to NCF bug detection. We conducted a comprehensive empirical study to explore the effectiveness of LLMs as test oracles for detecting NCF bugs in Android apps on 71 welldocumented NCF bugs. The results demonstrated that LLMs achieve a 49% bug detection rate, outperforming existing tools for detecting NCF bugs in Android apps. Additionally, by leveraging LLMs to be test oracles, we successfully detected 24 previously unknown NCF bugs in 64 Android apps, with four of these bugs being confirmed or fixed. However, we also identified limitations of LLMs, primarily related to performance degradation, inherent randomness, and false positives. Our study highlights the potential of leveraging LLMs as test oracles for Android NCF bug detection and suggests directions for future research.

In software development, bug report reproduction is a challenging task. This paper introduces ReBL, a novel feedback-driven approach that leverages GPT-4, a large-scale language model (LLM), to automatically reproduce Android bug reports. Unlike traditional methods, ReBL bypasses the use of Step to Reproduce (S2R) entities. Instead, it leverages the entire textual bug report and employs innovative prompts to enhance GPT’s contextual reasoning. This approach is more flexible and context-aware than the traditional step-by-step entity matching approach, resulting in improved accuracy and effectiveness. In addition to handling crash reports, ReBL has the capability of handling non-crash functional bug reports. Our evaluation of 96 Android bug reports (73 crash and 23 non-crash) demonstrates that ReBL successfully reproduced 90.63% of these reports, averaging only 74.98 seconds per bug report. Additionally, ReBL outperformed three existing tools in both success rate and speed.

Continuous integration (CI) has become a popular method for automating code changes, testing, and software project delivery. However, sufficient testing prior to code submission is crucial to prevent build breaks. Additionally, testing must provide developers with quick feedback on code changes, which requires fast testing times. While regression test selection (RTS) has been studied to improve the cost-effectiveness of regression testing for lower-level tests (i.e., unit tests), it has not been applied to the testing of user interfaces (UI) in application domains such as mobile apps. UI testing at the UI level requires different techniques such as impact analysis and automated test execution. In this paper, we examine the use of RTS in CI settings for UI testing across various open-source mobile apps. Our analysis focuses on using Frequency Analysis to understand the need for RTS, Cost Analysis to evaluate the cost of impact analysis and test case selection algorithms, and Test Reuse Analysis to determine the reusability of UI test sequences for automation. The insights from this study will guide practitioners and researchers in developing advanced RTS techniques that can be adapted to CI environments for mobile apps.