arXiv subjects

Vanessa Nava-Camal

Publications and source records attributed to Vanessa Nava-Camal.

2 recordsLinked to original sources

arXiv2026

Is Your Private Information Logged? An Empirical Study on Android App Logs

With the rapid growth of mobile apps, users' concerns about their privacy have become increasingly prominent. Android app logs serve as crucial computer resources, aiding developers in debugging and monitoring the status of Android apps, while also containing a wealth of software system information. Previous studies have acknowledged privacy leaks in software logs and Android apps as significant issues without providing a comprehensive view of the privacy leaks in Android app logs. In this study, we build a comprehensive dataset of Android app logs and conduct an empirical study to analyze the status and severity of privacy leaks in Android app logs. Our study comprises three aspects: (1) Understanding real-world developers' concerns regarding privacy issues related to software logs; (2) Studying privacy leaks in the Android app logs; (3) Investigating the characteristics of privacy-leaking Android app logs and analyzing the reasons behind them. Our study reveals five different categories of concerns from real-world developers regarding privacy issues related to software logs and the prevalence of privacy leaks in Android app logs, with the majority stemming from developers' unawareness of such leaks. Additionally, our study provides developers with suggestions to safeguard their privacy from being logged.

cs.SE↗

arXiv2025

An Empirical Study on the Capability of LLMs in Decomposing Bug Reports

Background: Bug reports are essential to the software development life cycle. They help developers track and resolve issues, but are often difficult to process due to their complexity, which can delay resolution and affect software quality. Aims: This study investigates whether large language models (LLMs) can assist developers in automatically decomposing complex bug reports into smaller, self-contained units, making them easier to understand and address. Method: We conducted an empirical study on 127 resolved privacy-related bug reports collected from Apache Jira. We evaluated ChatGPT and DeepSeek using different prompting strategies. We first tested both LLMs with zero-shot prompts, then applied improved prompts with demonstrations (using few-shot prompting) to measure their abilities in bug decomposition. Results: Our findings show that LLMs are capable of decomposing bug reports, but their overall performance still requires further improvement and strongly depends on the quality of the prompts. With zero-shot prompts, both studied LLMs (ChatGPT and DeepSeek) performed poorly. After prompt tuning, ChatGPT's true decomposition rate increased by 140\% and DeepSeek's by 163.64\%. Conclusions: LLMs show potential in helping developers analyze and decompose complex bug reports, but they still need improvement in terms of accuracy and bug understanding.

cs.SE↗