arXiv subjects

Hoa Khanh Dam

Publications and source records attributed to Hoa Khanh Dam.

21 records · Page 2Linked to original sources

arXiv2016

A deep language model for software code

Existing language models such as n-grams for software code often fail to capture a long context where dependent code elements scatter far apart. In this paper, we propose a novel approach to build a language model for software code to address this particular issue. Our language model, partly inspired by human memory, is built upon the powerful deep learning-based Long Short Term Memory architecture that is capable of learning long-term dependencies which occur frequently in software code. Results from our intrinsic evaluation on a corpus of Java projects have demonstrated the effectiveness of our language model. This work contributes to realizing our vision for DeepSoft, an end-to-end, generic deep learning-based framework for modeling software and its development process.

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arXiv2016

DeepSoft: A vision for a deep model of software

Although software analytics has experienced rapid growth as a research area, it has not yet reached its full potential for wide industrial adoption. Most of the existing work in software analytics still relies heavily on costly manual feature engineering processes, and they mainly address the traditional classification problems, as opposed to predicting future events. We present a vision for \emph{DeepSoft}, an \emph{end-to-end} generic framework for modeling software and its development process to predict future risks and recommend interventions. DeepSoft, partly inspired by human memory, is built upon the powerful deep learning-based Long Short Term Memory architecture that is capable of learning long-term temporal dependencies that occur in software evolution. Such deep learned patterns of software can be used to address a range of challenging problems such as code and task recommendation and prediction. DeepSoft provides a new approach for research into modeling of source code, risk prediction and mitigation, developer modeling, and automatically generating code patches from bug reports.

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arXiv2014

Towards rational and minimal change propagation in model evolution

A critical issue in the evolution of software models is change propagation: given a primary change that is made to a model in order to meet a new or changed requirement, what additional secondary changes are needed to maintain consistency within the model, and between the model and other models in the system? In practice, there are many ways of propagating changes to fix a given inconsistency, and how to justify and automate the selection between such change options remains a critical challenge. In this paper, we propose a number of postulates, inspired by the mature belief revision theory, that a change propagation process should satisfy to be considered rational and minimal. Such postulates enable us to reason about selecting alternative change options, and consequently to develop a machinery that automatically performs this task. We further argue that a possible implementation of such a change propagation process can be considered as a classical state space search in which each state represents a snapshot of the model in the process. This view naturally reflects the cascading nature of change propagation, where each change can require further changes to be made.

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