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PhD Dissertation Defense - Zhaoxu Zhang

Dissertation Title: Automated Reproduction of Bug Reports for Mobile Applications  
 
Date/Time: Friday, May 9th, 10:00 am-12:00 pm
 
Location: Ginsburg Hall (GCS) - 202C - 2nd Floor   Location: GCS 202C  
 
Committee: William G.J. Halfond (chair), Nenad Medvidovic, Chao Wang, Jesse Thomason, and Sandeep Gupta.  
 
Abstract:Mobile app developers need to reproduce the failures described in bug reports submitted by app users in order to fix the bug. However, due to the often low quality of bug reports and the complexity of modern applications, this manual reproduction process can be challenging and time-consuming. As a result, there is a significant demand for automated solutions that can assist in reproducing mobile app bug reports. Unfortunately, existing methods for reproducing mobile app bug reports have several limitations. They typically handle only limited forms of natural language text in the bug report, struggle to reproduce bugs when the report lacks accurate and complete reproduction steps, and are unable to reproduce non-crash bugs. In my dissertation, I developed and implemented several techniques to address the limitations of existing approaches and enhance the automated reproduction process for mobile app bug reports. First, I developed an approach that leverages a set of Natural Language Processing analyses to extract step information from bug reports, handling a wider variety of text than existing methods. Second, I introduced two algorithms designed to identify UI events to reproduce the reproduction steps, specifically aimed at addressing the challenges posed by incomplete and inaccurate steps. Third, I designed an approach that automatically recognizes buggy behaviors based on bug reports, enabling the automated reproduction of non-crash bug reports. I evaluated the effectiveness of each technique using real-world bug reports and assessed the overall reproduction performance by integrating them into end-to-end reproduction approaches. The results demonstrated that each individual technique achieved high accuracy, and the combined reproduction approach significantly outperformed state-of-the-art approaches in reproducing mobile app bug reports.

Location: Ginsburg Hall (GCS) - 202C

Audiences: Everyone Is Invited

Contact: Zhaoxu Zhang

This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor.

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