Boyi Xie

BUGMINER: Software Reliability Analysis Via Data Mining of Bug Reports

Software bugs reported by human users and automatic error reporting software are often stored in some bug tracking tools (e.g., Bugzilla and Debbugs). These accumulated bug reports may contain valuable information that could be used to improve the quality of the bug reporting, reduce the quality assurance effort and cost, analyze software reliability, and predict future bug report trend. In this paper, we present BUGMINER, a tool that is able to derive useful information from historic bug report database using data mining, use these information to do completion check and redundancy check on a new or given bug report, and to estimate the bug report trend using statistical analysis. Our empirical studies of the tool using several real-world bug report repositories show that it is effective, easy to implement, and has relatively high accuracy despite low quality data.

Analytics for Power Grid Distribution Reliability in New York City

We summarize the first major effort to use analytics for preemptive maintenance and repair of an electrical distribution network. This is a large-scale multiyear effort between scientists and students at Columbia University and the Massachusetts Institute of Technology and engineers from the Consolidated Edison Company of New York Con Edison, which operates the worlds oldest and largest underground electrical system. Con Edisons preemptive maintenance programs are less than a decade old and are made more effective with the use of analytics developing alongside them. Some of the data we used for our projects are historical records dating as far back as the 1880s, and some of the data are free-text documents typed by Con Edison dispatchers. The operational goals of this work are to assist with Con Edisons preemptive inspection and repair program and its vented-cover replacement program. This has a continuing impact on the public safety, operating costs, and reliability of electrical service in New York City.

Named Entity Recognition from Financial Press Releases

This paper explores a previous model’s use of named entity recognition to predict the changes in stock prices from financial news. Detecting company mentions in the articles is crucial for this task, and we modified these methods to gain additional mentions. We first expanded upon the rules of the named entity recognition from the original model. We also incorporated coreference resolution and modified an existing toolkit to be compatible with our specific domain. After these two adjustments, the number of instances captured increased significantly. Although this did not necessarily improve the overall prediction performance, the results give us an opportunity to explore reasons why the scores stayed around the same, and a full analysis will allow us to achieve our goals.

Company Mention Detection for Large Scale Text Mining

Text mining on a large scale that addresses actionable prediction needs to contend with noisy information in documents, and with interdependencies between the NLP techniques applied and the data representation. This paper presents an initial investigation of the impact of improved company mention detection for financial analytics using Named Entity recognition and coreference. Coverage of company mention detection improves dramatically. Improvement for prediction of stock price varies, depending on the data representation.