Mining developer communication data streams

This paper explores the concepts of modelling a software development project as a process that results in the creation of a continuous stream of data. In terms of the Jazz repository used in this research, one aspect of that stream of data would be developer communication. Such data can be used to create an evolving social network characterized by a range of metrics. This paper presents the application of data stream mining techniques to identify the most useful metrics for predicting build outcomes. Results are presented from applying the Hoeffding Tree classification method used in conjunction with the Adaptive Sliding Window (ADWIN) method for detecting concept drift. The results indicate that only a small number of the available metrics considered have any significance for predicting the outcome of a build

SCREEN: Learning a Flat Syntactic and Semantic Spoken Language Analysis Using Artificial Neural Networks

In this paper, we describe a so-called screening approach for learning robust processing of spontaneously spoken language. A screening approach is a flat analysis which uses shallow sequences of category representations for analyzing an utterance at various syntactic, semantic and dialog levels. Rather than using a deeply structured symbolic analysis, we use a flat connectionist analysis. This screening approach aims at supporting speech and language processing by using (1) data-driven learning and (2) robustness of connectionist networks. In order to test this approach, we have developed the SCREEN system which is based on this new robust, learned and flat analysis. In this paper, we focus on a detailed description of SCREEN's architecture, the flat syntactic and semantic analysis, the interaction with a speech recognizer, and a detailed evaluation analysis of the robustness under the influence of noisy or incomplete input. The main result of this paper is that flat representations allow more robust processing of spontaneous spoken language than deeply structured representations. In particular, we show how the fault-tolerance and learning capability of connectionist networks can support a flat analysis for providing more robust spoken-language processing within an overall hybrid symbolic/connectionist framework.Comment: 51 pages, Postscript. To be published in Journal of Artificial Intelligence Research 6(1), 199

Seismic Response of a Tall Building to Recorded and Simulated Ground Motions

Seismological modeling technologies are advancing to the stage of enabling fundamental simulation of earthquake fault ruptures, which offer new opportunities to simulate extreme ground motions for collapse safety assessment and earthquake scenarios for community resilience studies. With the goal toward establishing the reliability of simulated ground motions for performance-based engineering, this paper examines the response of a 20-story concrete moment frame building analyzed by nonlinear dynamic analysis under corresponding sets of recorded and simulated ground motions. The simulated ground motions were obtained through a larger validation study via the Southern California Earthquake Center (SCEC) Broadband Platform (BBP) that simulates magnitude 5.9 to 7.3 earthquakes. Spectral shape and significant duration are considered when selecting ground motions in the development of comparable sets of simulated and recorded ground motions. Structural response is examined at different intensity levels up to collapse, to investigate whether a statistically significant difference exists between the responses to simulated and recorded ground motions. Results indicate that responses to simulated and recorded ground motions are generally similar at intensity levels prior to observation of collapses. Collapse capacities are also in good agreement for this structure. However, when the structure was made more sensitive to effects of ground motion duration, the differences between observed collapse responses increased. Research is ongoing to illuminate reasons for the difference and whether there is a systematic bias in the results that can be traced back to the ground motion simulation techniques

JWalk: a tool for lazy, systematic testing of java classes by design introspection and user interaction

Popular software testing tools, such as JUnit, allow frequent retesting of modified code; yet the manually created test scripts are often seriously incomplete. A unit-testing tool called JWalk has therefore been developed to address the need for systematic unit testing within the context of agile methods. The tool operates directly on the compiled code for Java classes and uses a new lazy method for inducing the changing design of a class on the fly. This is achieved partly through introspection, using Java’s reflection capability, and partly through interaction with the user, constructing and saving test oracles on the fly. Predictive rules reduce the number of oracle values that must be confirmed by the tester. Without human intervention, JWalk performs bounded exhaustive exploration of the class’s method protocols and may be directed to explore the space of algebraic constructions, or the intended design state-space of the tested class. With some human interaction, JWalk performs up to the equivalent of fully automated state-based testing, from a specification that was acquired incrementally