arXiv subjects

Jiangfan Shi

Publications and source records attributed to Jiangfan Shi.

3 recordsLinked to original sources

arXiv2026

SysPro: Reproducing System-level Concurrency Bugs from Bug Reports

Reproducing system-level concurrency bugs requires both input data and the precise interleaving order of system calls. This process is challenging because such bugs are non-deterministic, and bug reports often lack the detailed information needed. Additionally, the unstructured nature of reports written in natural language makes it difficult to extract necessary details. Existing tools are inadequate to reproduce these bugs due to their inability to manage the specific interleaving at the system call level. To address these challenges, we propose SysPro, a novel approach that automatically extracts relevant system call names from bug reports and identifies their locations in the source code. It generates input data by utilizing information retrieval, regular expression matching, and the category-partition method. This extracted input and interleaving data are then used to reproduce bugs through dynamic source code instrumentation. Our empirical study on real-world benchmarks demonstrates that SysPro is both effective and efficient at localizing and reproducing system-level concurrency bugs from bug reports.

cs.SE↗

arXiv2024

Feedback-Driven Automated Whole Bug Report Reproduction for Android Apps

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.

cs.SE↗

arXiv2024

AURORA: Navigating UI Tarpits via Automated Neural Screen Understanding

Nearly a decade of research in software engineering has focused on automating mobile app testing to help engineers in overcoming the unique challenges associated with the software platform. Much of this work has come in the form of Automated Input Generation tools (AIG tools) that dynamically explore app screens. However, such tools have repeatedly been demonstrated to achieve lower-than-expected code coverage - particularly on sophisticated proprietary apps. Prior work has illustrated that a primary cause of these coverage deficiencies is related to so-called tarpits, or complex screens that are difficult to navigate. In this paper, we take a critical step toward enabling AIG tools to effectively navigate tarpits during app exploration through a new form of automated semantic screen understanding. We introduce AURORA, a technique that learns from the visual and textual patterns that exist in mobile app UIs to automatically detect common screen designs and navigate them accordingly. The key idea of AURORA is that there are a finite number of mobile app screen designs, albeit with subtle variations, such that the general patterns of different categories of UI designs can be learned. As such, AURORA employs a multi-modal, neural screen classifier that is able to recognize the most common types of UI screen designs. After recognizing a given screen, it then applies a set of flexible and generalizable heuristics to properly navigate the screen. We evaluated AURORA both on a set of 12 apps with known tarpits from prior work, and on a new set of five of the most popular apps from the Google Play store. Our results indicate that AURORA is able to effectively navigate tarpit screens, outperforming prior approaches that avoid tarpits by 19.6% in terms of method coverage. The improvements can be attributed to AURORA's UI design classification and heuristic navigation techniques.

cs.SE↗