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Reproducing System-Level Concurrency failures is hard because (1) concurrency failures at the system level often involve multiple processes or event handlers (e.g., software signals), which cannot be handled by existing tools for reproducing intra-process (thread-level) failures; (2) detailed field data, such as user input, file content and interleaving schedule, may not be available to developers; and (3) the debugging environment may differ from the deployed environment, which further complicates failure reproduction. To address these problems, we develped automated teqniques to reproduce system-level concurrency bugs by using the default console logs of an application
Besides input information, interleaving information is required to reproduce system-level concurrency bugs. Therefore, developers need to extract both of this information from bug reports which are mostly written in natural language. Due to the non-deterministic nature and the requirements for extra information to reproduce the system-level concurrency bugs, it is very difficult and sometimes impossible to reproduce these bugs manually from bug reports. Current bug reproduction tools are not compatible with this type of bug as we need to know the specific interleaving schedule operated at the system call level to reproduce these bugs
Localizing concurrency faults that occur in production is hard because, (1) detailed field data, such as user input, file content and interleaving schedule, may not be available to developers to reproduce the failure; (2) it is often impractical to assume the availability of multiple failing executions to localize the faults using existing techniques; (3) it is challenging to search for buggy locations in an application given limited runtime data; and, (4) concurrency failures at the system level often involve multiple processes or event handlers (e.g., software signals), which cannot be handled by existing tools for diagnosing intra-process (thread-level) failures
Programs often follow implicit programming rules, such as, function call A must be followed by function call B. Nevertheless, programming rules play an important role in software testing and maintenance. For example, the rules can be used as test oracles to detect violations. If a programmer can be notified of these rules before updating the source code, the chances of generating defects due to rule violations might be minimized.
With the explosive growth of data availability on the World Wide Web, web usage mining becomes very essential for improving designs of websites, analyzing system performance as well as network communications, understanding user reaction, motivation and building adaptive websites. Web Access Pattern mining (WAP-mine) is a sequential pattern mining technique for discovering frequent web log access sequences. It first stores the frequent part of original web access sequence database on a prefix tree called WAP-tree and mines the frequent sequences from that tree according to a user given minimum support threshold. Therefore, this method is not applicable for incremental and interactive mining
Many Automated debug-ging techniques are developed and proposed by researchers. However, there is noresearch on the real-world usage of these techniques. To develop an user friendlydebugging technique we set up research questions for developers. The answers willshow the path to develop an user-interactive debugging technique.
Published in GSTF Journal on Computing (JoC), 2014
Recommended citation: Zaman, T.S., Islam, N., Ahmed, C.F., Jeong, B.S.: iWAP: a single pass approach for web access sequential pattern mining. GSTF J. Comput. (JoC) 2(1), 1–6 (2018)." http://dl6.globalstf.org/index.php/joc/article/view/691
Published in Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering, 2017
Recommended citation: Tingting Yu, Tarannum S. Zaman, and Chao Wang. 2017. DESCRY: reproducing system-level concurrency failures. In Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering (ESEC/FSE 2017). Association for Computing Machinery, New York, NY, USA, 694–704. DOI:https://doi.org/10.1145/3106237.3106266. https://dl.acm.org/doi/abs/10.1145/3106237.3106266
Published in Grand Challenges of Traceability: The Next Ten Years, 2017
Recommended citation: Zhao, Yu, Tarannum S. Zaman, Tingting Yu, and Jane Huffman Hayes. "Using Deep Learning to Improve the Accuracy of Requirements to Code Traceability." https://arxiv.org/pdf/1710.03129.pdf#page=22
Published in SoftwareMining 2018: Proceedings of the 7th International Workshop on Software Mining, 2018
Recommended citation: Tarannum Shaila Zaman and Tingting Yu. 2018. Extracting implicit programming rules: comparing static and dynamic approaches. In Proceedings of the 7th International Workshop on Software Mining (SoftwareMining 2018). Association for Computing Machinery, New York, NY, USA, 1–7. DOI:https://doi.org/10.1145/3242887.3242889 https://dl.acm.org/doi/abs/10.1145/3242887.3242889
Published in 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), 2019
Recommended citation: T. S. Zaman, X. Han and T. Yu, "SCMiner: Localizing System-Level Concurrency Faults from Large System Call Traces," 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), 2019, pp. 515-526, doi: 10.1109/ASE.2019.00055. https://ieeexplore.ieee.org/abstract/document/8952396
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High School course, Holy Cross Girls' High School & College, 2009
I worked as a part time instructor for the math class (Fall 2009 and Fall 2010) of the high school kids. I taught them arithmetic, algebra and geometry.
High School course, Holy Cross Girls' High School & College, 2010
I worked as a part time instructor for the chemistry class (Spring 2010) of the high school kids. I taught them organic and inorganic chemistry..
Undergraduate course, University of Kentucky, Department of Computer Science , 2016
I worked as a teaching assistant for this course (Fall 2016 & Spring 2017) and taught basic programming concept by solving problems with Matlab.
Undergraduate course, University of Kentucky, Department of Computer Science, 2018
I worked as a teaching assistant in this course (Fall 2018 & Spring 2019) and taught fundamental elements of data structures and algorithm design.
Undergraduate course, University of Kentucky, Department of Computer Science, 2019
I worked as a parttime instructor for this course (Fall 2019) and helped the course instructor to take some classes.
Graduate Course, University of Kentucky, Department of Computer Science, 2020
I worked as a parttime instructor for this course (Spring 2020) and helped my advisor to take some classes.
Undergraduate course, University of Kentucky, Department of Computer Science, 2020
I worked as a teaching assistant in this course (Fall 2020 & Spring 2021) where I introduced the student to the basics of software engineering and the Unix platform and taught them the implementation of large programming projects using object-oriented design techniques.