Howdy, I'm Trung Hieu Tran (Harry)

PhD Candidate at The University of Texas at Dallas.

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About Me

Hi, I am a Ph.D. candidate in Software Engineer at The University of Texas at Dallas (UTD) under supervisor Dr. I-Ling Yen, and had cooperated with Dr. Tien Nguyen in some projects. I am original from Vietnam. I spent 5 years for doing Bachelor in Software Enginner in Russia at Volgograd State Technical University (VSTU).

My research interests: Software Engineer, Data Mining, Program Analysis, IoT Data Discovery, Machine Learning, Big Data;


January 2018 - Present

Doctor of Philosophy in Software Engineering

The University of Texas at Dallas (UTD)

2018 - 2020

Master of Science in Computer Science

The University of Texas at Dallas (UTD)

2011 - 2016

Bachelor in Software Engineering

Volgograd State Technical University (VSTU)

Selected Publications

  1. [ICSE 2019] Hieu Tran, Ngoc Tran, Son Nguyen, Hoan Nguyen and Tien N.Nguyen, "Recovering Variable Names for Minified Code with Usage Contexts", in Proceedings of the 41th ACM/IEEE International Conference on Software Engineering (ACM/IEEE ICSE 2019), May 25 - 31, 2019. IEEE CS Press, 2019 (PDF | ACM Library).
  2. [ICSE 2019] Hoan Nguyen, Tien N. Nguyen, Danny Dig, Son Nguyen, Hieu Tran and Michael Hilton, "Graph-based Mining of In-the-Wild, Fine-grained, Semantic Code Change Patterns", in Proceedings of the 41th ACM/IEEE International Conference on Software Engineering (ACM/IEEE ICSE 2019), May 25 - 31, 2019. IEEE CS Press, 2019 (PDF | ACM Library).
  3. [ICPC 2019] Ngoc Tran, Hieu Tran, Son Nguyen, Hoan Nguyen and Tien N.Nguyen, "Does BLEU Score Work for Code Migration?", in Proceedings of the 27th IEEE International Conference on Program Comprehension (ACM/IEEE ICPC 2019), May 25 - 26, 2019. IEEE CS Press, 2019 (PDF | ACM Library).
  4. [ASE 2019] Son Nguyen, Hoan Nguyen, Ngoc Tran, Hieu Tran and Tien N.Nguyen, "Feature-Interaction Aware Configuration Prioritization for Configurable Code", in Proceedings of the 34th ACM/ IEEE International Conference on Automated Software Engineering (ACM/IEEE ASE 2019), November 11 - 15, 2019 (PDF | IEEE Library).
  5. [ResearchGate] Hieu Tran "Survey of Machine Learning and Data Mining Techniques used in Multimedia System", DOI: 10.13140/RG.2.2.20395.49446/1, Researchgate, September 2019 (PDF | Research Gate Library).
  6. [CSoNet 2016] Hieu Tran, Maxim Sherbakov. Detection and Prediction of Users Attitude Based on Real-Time and Batch Sentiment Analysis of Facebook Comments. In Proc. of The 5th International Conference on Computational Social Networks (CSoNet 2016), LNCS 9795, pp. 273-284. Springer (PDF | Springer Library).
  7. [VSTU research 2016] Hieu Tran. Implementation of a Sentiment Analysis Method on Social Networks. The Competition of Scientific, Design and Technological Research for Students in Volgograd State Technical University 2016.

Selected Projects

Space Efficiency in IoT Data Discovery Network (2019 - present).

Designing technique to store data efficiently in IoT Network based on summarization for purpose of data discovery.

[ be updated]

Recovering Variable Names for Minified Code with Usage Contexts (2018 - 2019)

In modern Web technology, JavaScript (JS) code plays an important role. To avoid the exposure of original source code, the variable names in JS code deployed in the wild are often replaced by short, meaningless names, thus making the code extremely difficult to manually understand and analysis. This paper presents JSNeat, an information retrieval (IR)-based approach to recover the variable names in minified JS code. JSNeat follows a data-driven approach to recover names by searching for them in a large corpus of open-source JS code. We use three types of contexts to match a variable in given minified code against the corpus including the context of properties and roles of the variable, the context of that variable and relations with other variables under recovery, and the context of the task of the function to which the variable contributes. We performed several empirical experiments to evaluate JSNeat on the dataset of more than 322K JS files with 1M functions, and 3.5M variables with 176K unique variable names. We found that JSNeat achieves a high accuracy of 69.1%, which is the relative improvements of 66.1% and 43% over two state-of-the-art approaches JSNice and JSNaughty, respectively. The time to recover for a file or for a variable with JSNeat is twice as fast as with JSNice and 4x as fast as with JNaughty, respectively.

Sentiment Analysis of Facebook comments (2016)

The most of the people have their account on social networks (e.g. Facebook, Vkontakte) where they express their attitude to different situations and events. Facebook provides only the positive mark as a like button and share. However, it is important to know the position of a certain user on posts even though the opinion is negative. Positive, negative and neutral attitude can be extracted from the comments of users. Overall information about positive, negative and neutral opinion can bring understanding how people react in a position. Moreover, it is important to know how attitude is changing during the time period. The contribution of the paper is a new method based on sentiment text analysis for detection and prediction negative and positive patterns for Facebook comments which combines (i) real-time sentiment text analysis for pattern discovery and (ii) batch data processing for creating opinion forecasting algorithm. To perform forecast we propose two-steps algorithm where: (i) patterns are clustered using unsupervised clustering techniques and (ii) trend prediction is performed based on finding the nearest pattern from the certain cluster. Case studies show the efficiency and accuracy (Avg. MAE = 0.008) of the proposed method and its practical applicability. Also, we discovered three types of users attitude patterns and described them.

Blocks Supervised (2015)

This block allows teachers to plan and start supervised sessions in particular classroom, with particular group and of particular lesson type. This block is devoted to track supervised sessions, where students work on the site in classroom under teacher (or staff) supervision. The session can have it's type (lesson type), classroom and group of students. The classroom is defined via IP subnet, so students off class won't be recorded as participating in sessions. The block allows you to see logs on what is going in each particular session. More importantly, you can control students permissions, given them special abilities (like attempting exam quiz) only during supervised sessions. For now there is quiz access control rule, allowing you to restrict quiz access to supervised sessions. There will be more options later.


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