Lei Wu

Coping with legacy system migration complexity

During the last three decades, a considerable amount of software has been developed based on obsolete technologies (such as using procedural languages). This type of systems has undergone severe code revisions during a long time period. As a consequence, the high level of entropy combined with imprecise documentation about the design and architecture make the maintenance more difficult, time consuming, and costly. On the other hand, these systems have important economical value; many of them are crucial to their owners (Bennett, 1995). For the high cost of lost former investment and business knowledge that embedded in those systems, in many cases, simply abandon legacy systems and re-develop new systems based on new technology is not the choice. Migrating legacy system toward new emerging technology is an appropriate solution. However, migrating legacy system towards new technology is a complex system engineering work. In this paper, we propose a novel approach to reduce the migration complexity. We apply dynamic program analysis, software visualization, knowledge recovery, and divide-and-conquer techniques to cope with the complexity issue in legacy software migration project.

Accommodating software development collaboration

With the rapid progress of Internet technology, more and more software projects adopt e-development to facilitate the software development process in a world wide context. However, collaborative software e-development activity itself is a complex orchestration. It involves many people working together without the barrier of time and space difference. Therefore, how to efficiently monitor and control software e-development in a global perspective becomes an important issue for any Internet-based software e-development project. In this paper, we present a novel approach to tackle this crucial issue by means of controlling e-development process, collaborative task progress and communication quality. Meanwhile, we also present our e-development supporting environment prototype: Caribou, to demonstrate the viability of our approach.

Supporting Web Collaboration for Cooperative Software Development

With the rapid growth of web technology, more and more software development projects use web collaborations to facilitate the development process. However, web collaboration activity is a complex orchestration. It involves many people work together without the barrier of time and space difference. Therefore, how to efficiently monitor and control web collaboration activity becomes a critical issue in a web-based collaborative software development project. In this paper, we present a novel approach to tackle this difficult problem by means of monitoring collaboration task progress. In addition, we also provide solutions to automate the dynamic control of cooperation, thus to improve the web collaboration performance.

Dual Long Short-Term Memory Networks for Sub-Character Representation Learning

Characters have commonly been regarded as the minimal processing unit in Natural Language Processing (NLP). But many non-latin languages have hieroglyphic writing systems, involving a big alphabet with thousands or millions of characters. Each character is composed of even smaller parts, which are often ignored by the previous work. In this paper, we propose a novel architecture employing two stacked Long Short-Term Memory Networks (LSTMs) to learn sub-character level representation and capture deeper level of semantic meanings. To build a concrete study and substantiate the efficiency of our neural architecture, we take Chinese Word Segmentation as a research case example. Among those languages, Chinese is a typical case, for which every character contains several components called radicals. Our networks employ a shared radical level embedding to solve both Simplified and Traditional Chinese Word Segmentation, without extra Traditional to Simplified Chinese conversion, in such a highly end-to-end way the word segmentation can be significantly simplified compared to the previous work. Radical level embeddings can also capture deeper semantic meaning below character level and improve the system performance of learning. By tying radical and character embeddings together, the parameter count is reduced whereas semantic knowledge is shared and transferred between two levels, boosting the performance largely. On 3 out of 4 Bakeoff 2005 datasets, our method surpassed state-of-the-art results by up to 0.4%. Our results are reproducible; source codes and corpora are available on GitHub (https://github.com/hankcs/sub-character-cws).

Exploring Slice-Energy Saving on an Video Processing FPGA Platform with Approximate Computing

This paper proposes a scalable video processing platform on Field Programmable Gate Array (FPGA), providing several slice-energy cost solutions corresponding to different application constrains. Specifically three approximations of multipliers and two approximations of adders, along with the exact designs as well, are presented and integrated as twelve benchmarks to implement RGB to grayscale conversion as a case study. Experimental results show that the minimum slice-energy cost, integrated with approximate#2 adder and approximate#3 multiplier, achieves 25.17% slice-energy saving compared with the exact design by sacrificing the quality of results as 5.69% error for multiplier and 2.85% for adder.

Software reengineering with architecture decomposition

Software reengineering involves the activities of studying target systems architecture. However, enterprise legacy software systems tend to be large and complex. The analysis of system architecture therefore becomes a difficult task. To solve the problem, we propose an approach that decomposes software architecture to reduce the complexity associated with analyzing large scale architecture artifacts. Our study has shown that architecture decomposition is an efficient way to limit the complexity and risk associated with the re-engineering activities of a large legacy system. It divides the system into a collection of meaningful modular parts with low coupling, high cohesion, and minimizes the interface, thus to facilitate the incremental approach to implement the progressive software re-engineering process. To fulfill this goal, we have developed two major techniques to decompose legacy system architecture. In this paper, we present them in detail. The approach is also supported by our automated reverse engineering tools, and the preliminary experimental result shows our approach is very promising.

Comparative Analytical Study of Cutting-Edge Dependency Parsing for Nature Language Processing

Dependency parsing is a task of automatically parsing dependency grammar for a sentence in natural language, which sustains advanced applications such as machine translation, question answering. This paper thoroughly studies influential and state-of-the-art works of the two major classes of approaches: transition-based parsing and graph-based parsing. This in-depth comparative analytical study mostly focuses on fundamental concepts and current trends and has comprehensively analyzed state-of-the-art implementations of different approaches.

Caribou: a supporting environment for software e-development

With the rapid progress of Internet technology, more and more software projects adopt e-development to facilitate the software development process in a world wide context. However, collaborative software e-development activity itself is a complex orchestration. It involves many people working together without the barrier of time and space difference. Therefore, how to efficiently monitor and control software e-development in a global perspective becomes an important issue for any Internet-based software e-development project. In this paper, we present a novel approach to tackle this crucial issue by means of controlling e-development process and communication quality. Meanwhile, we also present our e-development supporting environment prototype: Caribou, to demonstrate the viability of our approach.