Events for May 09, 2025
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PhD Dissertation Defense - Zhaoxu Zhang
Fri, May 09, 2025 @ 10:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
University Calendar
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. -
PhD Thesis Proposal - James Huang
Fri, May 09, 2025 @ 03:00 PM - 04:30 PM
Thomas Lord Department of Computer Science
University Calendar
Title: Collaborative Decision-Making of Language Models
Date and Time: Friday, May 9th, 2025 | 3:00p - 4:30p
Location: GCS 502C
Committee Members: Muhao Chen, Fred Morstatter, Laurent Itti, Robbin Jia, Dan O'Leary
Abstract: While general-purpose language models have demonstrated strong performance on a wide range of tasks, they still have their own weaknesses such as biases, misalignment, lack of task-specific knowledge, etc. One promising way of addressing these challenges is to combine the strengths of different language models. In this proposal, I will outline my research exploring various strategies to facilitate collaborative decision making of language models. Specifically, I will present 1) a shortcut mitigation method via ensemble-based attention debiasing, 2) a decoding-time alignment framework that uses model-based reward functions to guide model generation, and 3) an unlearning method that removes sensitive knowledge by learning a logit offset. Finally, I will discuss future directions for language model collaboration.Location: Ginsburg Hall (GCS) - 502C
Audiences: Everyone Is Invited
Contact: James Huang
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.
