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1. Automated Recognition of Buggy Behaviors from Mobile Bug Reports

   https://doi.org/10.1145/3729370  

   Zhang, Zhaoxu; Ryu, Komei; Yu, Tingting; Halfond, William GJ (June 2025, Proceedings of the ACM on Software Engineering)

   Bug report reproduction is a crucial but time-consuming task to be carried out during mobile app maintenance. To accelerate this process, researchers have developed automated techniques for reproducing mobile app bug reports. However, due to the lack of an effective mechanism to recognize different buggy behaviors described in the report, existing work is limited to reproducing crash bug reports, or requires developers to manually analyze execution traces to determine if a bug was successfully reproduced. To address this limitation, we introduce a novel technique to automatically identify and extract the buggy behavior from the bug report and detect it during the automated reproduction process. To accommodate various buggy behaviors of mobile app bugs, we conducted an empirical study and created a standardized representation for expressing the bug behavior identified from our study. Given a report, our approach first transforms the documented buggy behavior into this standardized representation, then matches it against real-time device and UI information during the reproduction to recognize the bug. Our empirical evaluation demonstrated that our approach achieved over 90% precision and recall in generating the standardized representation of buggy behaviors. It correctly identified bugs in 83% of the bug reports and enhanced existing reproduction techniques, allowing them to reproduce four times more bug reports. 

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2. Bug Localization via Supervised Topic Modeling

   https://doi.org/10.1109/ICDM.2018.00076  

   Wang, Yaojing; Yao, Yuan; Tong, Hanghang; Huo, Xuan; Li, Min; Xu, Feng; Lu, Jian (November 2018, 2018 IEEE International Conference on Data Mining (ICDM))

   Bug tracking systems, which help to track the reported software bugs, have been widely used in software development and maintenance. In these systems, recognizing relevant source files among a large number of source files for a given bug report is a time-consuming and labor-intensive task for software developers. To tackle this problem, information retrieval methods have been widely used to capture either the textual similarities or the semantic similarities between bug reports and source files. However, these two types of similarities are usually considered separately and the historical bug fixings are largely ignored by the existing methods. In this paper, we propose a supervised topic modeling method (STMLOCATOR) for automatically locating the relevant source files for a given bug report. In particular, the proposed model is built upon three key observations. First, supervised modeling can effectively make use of the existing fixing histories. Second, certain words in bug reports tend to appear multiple times in their relevant source files. Third, longer source files tend to have more bugs. By integrating the above three observations, the proposed STMLOCATOR utilizes historical fixings in a supervised way and learns both the textual similarities and semantic similarities between bug reports and source files. We further consider a special type of bug reports with stack-traces in bug reports, and propose a variant of STMLOCATOR to tailor for such bug reports. Experimental evaluations on three real data sets demonstrate that the proposed STMLOCATOR can achieve up to 23.6% improvement in terms of prediction accuracy over its best competitors, and scales linearly with the size of the data. Moreover, the proposed variant further improves STMLOCATOR by up to 76.2% on those bug reports with stack-traces. 

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3. ReCDroid+: Automated End-to-End Crash Reproduction from Bug Reports for Android Apps

   https://doi.org/10.1145/3488244  

   Zhao, Yu; Su, Ting; Liu, Yang; Zheng, Wei; Wu, Xiaoxue; Kavuluru, Ramakanth; Halfond, William G.; Yu, Tingting (July 2022, ACM Transactions on Software Engineering and Methodology)

   The large demand of mobile devices creates significant concerns about the quality of mobile applications (apps). Developers heavily rely on bug reports in issue tracking systems to reproduce failures (e.g., crashes). However, the process of crash reproduction is often manually done by developers, making the resolution of bugs inefficient, especially given that bug reports are often written in natural language. To improve the productivity of developers in resolving bug reports, in this paper, we introduce a novel approach, called ReCDroid+, that can automatically reproduce crashes from bug reports for Android apps. ReCDroid+ uses a combination of natural language processing (NLP) , deep learning, and dynamic GUI exploration to synthesize event sequences with the goal of reproducing the reported crash. We have evaluated ReCDroid+ on 66 original bug reports from 37 Android apps. The results show that ReCDroid+ successfully reproduced 42 crashes (63.6% success rate) directly from the textual description of the manually reproduced bug reports. A user study involving 12 participants demonstrates that ReCDroid+ can improve the productivity of developers when resolving crash bug reports. 

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4. CooBa: Cross-project Bug Localization via Adversarial Transfer Learning

   https://doi.org/10.24963/ijcai.2020/493  

   Zhu, Ziye; Li, Yun; Tong, Hanghang; Wang, Yu (July 2020, IJCAI)

   null (Ed.)

   Bug localization plays an important role in software quality control. Many supervised machine learning models have been developed based on historical bug-fix information. Despite being successful, these methods often require sufficient historical data (i.e., labels), which is not always available especially for newly developed software projects. In response, cross-project bug localization techniques have recently emerged whose key idea is to transferring knowledge from label-rich source project to locate bugs in the target project. However, a major limitation of these existing techniques lies in that they fail to capture the specificity of each individual project, and are thus prone to negative transfer.To address this issue, we propose an adversarial transfer learning bug localization approach, focusing on only transferring the common characteristics (i.e., public information) across projects. Specifically, our approach (CooBa) learns the indicative public information from cross-project bug reports through a shared encoder, and extracts the private information from code files by an individual feature extractor for each project. CooBa further incorporates adversarial learning mechanism to ensure that public information shared between multiple projects could be effectively extracted. Extensive experiments on four large-scale real-world data sets demonstrate that the proposed CooBa significantly outperforms the state of the art techniques. 

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5. DinoDroid: Testing Android Apps Using Deep Q-Networks

   https://doi.org/10.1145/3652150  

   Zhao, Yu; Harrison, Brent; Yu, Tingting (June 2024, ACM Transactions on Software Engineering and Methodology)

   The large demand of mobile devices creates significant concerns about the quality of mobile applications (apps). Developers need to guarantee the quality of mobile apps before it is released to the market. There have been many approaches using different strategies to test the GUI of mobile apps. However, they still need improvement due to their limited effectiveness. In this article, we propose DinoDroid, an approach based on deep Q-networks to automate testing of Android apps. DinoDroid learns a behavior model from a set of existing apps and the learned model can be used to explore and generate tests for new apps. DinoDroid is able to capture the fine-grained details of GUI events (e.g., the content of GUI widgets) and use them as features that are fed into deep neural network, which acts as the agent to guide app exploration. DinoDroid automatically adapts the learned model during the exploration without the need of any modeling strategies or pre-defined rules. We conduct experiments on 64 open-source Android apps. The results showed that DinoDroid outperforms existing Android testing tools in terms of code coverage and bug detection. 

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