Thesis (M.S. in Modeling, Virtual Environments and Simulation (MOVES))--Naval Postgraduate School, March 2010. / Thesis Advisor(s): Brutzman, Don ; McGregor, Don. "March 2010." Description based on title screen as viewed on April 23, 2010. Author(s) subject terms: Extensible Markup Language (XML), Efficient XML Interchange (EXI), Compression. Includes bibliographical references (p. 337-353). Also available in print.

Thesis (M.S. in computer science)--Washington State University, May 2010. / Title from PDF title page (viewed on June 23, 2010). "School of Engineering and Computer Science." Includes bibliographical references (p. 79-80).

Abstract not available

Thesis (M.S.)--Ohio University, November, 1999. / Title from PDF t.p.

Thesis (Ph. D.)--University of Texas at Austin, 2008. / Vita. Includes bibliographical references.

Thesis (M.S.)--University of North Carolina Wilmington, 2008. / Title from PDF title page (viewed September 22, 2008) Includes bibliographical references (p. 72-74)

This dissertation highlights that existing performance diagnostic tools often become less effective due to their inherent inaccuracies in modern software. To overcome these inaccuracies and effectively identify the root causes of performance issues, it is necessary to incorporate supplementary runtime information into these tools. Within this context, the dissertation integrates specific runtime information into two typical performance diagnostic tools: profilers and causal tracing tools. The integration yields a substantial enhancement in the effectiveness of performance diagnosis. Among these tools, gprof stands out as a representative profiler for performance diagnosis. Nonetheless, its effectiveness diminishes as the time cost calculated based on CPU sampling fails to accurately and adequately pinpoint the root causes of performance issues in complex software. To tackle this challenge, the dissertation introduces an innovative methodology called value-assisted cost profiling (vProf). This approach incorporates variable values observed during runtime into the profiling process. By continuously sampling variable values from both normal and problematic executions, vProf refines function cost estimates, identifies anomalies in value distributions, and highlights potentially problematic code areas that could be the actual sources of performance is- sues. The effectiveness of vProf is validated through the diagnosis of 18 real-world performance is- sues in four widely-used applications. Remarkably, vProf outperforms other state-of-the-art tools, successfully diagnosing all issues, including three that had remained unresolved for over four years. Causal tracing tools reveal the root causes of performance issues in complex software by generating tracing graphs. However, these graphs often suffer from inherent inaccuracies, characterized by superfluous (over-connected) and missed (under-connected) edges. These inaccuracies arise from the diversity of programming paradigms. To mitigate the inaccuracies, the dissertation proposes an approach to derive strong and weak edges in tracing graphs based on the vertices’ semantics collected during runtime. By leveraging these edge types, a beam-search-based diagnostic algorithm is employed to identify the most probable causal paths. Causal paths from normal and buggy executions are differentiated to provide key insights into the root causes of performance issues. To validate this approach, a causal tracing tool named Argus is developed and tested across multiple versions of macOS. It is evaluated on 12 well-known spinning pinwheel issues in popular macOS applications. Notably, Argus successfully diagnoses the root causes of all identified issues, including 10 issues that had remained unresolved for several years. The results from both tools exemplify a substantial enhancement of performance diagnostic tools achieved by harnessing runtime information. The integration can effectively mitigate inherent inaccuracies, lend support to inaccuracy-tolerant diagnostic algorithms, and provide key insights to pinpoint the root causes.

Thesis (Ph. D.)--University of California, Riverside, 2009. / Includes abstract. Title from first page of PDF file (viewed March 11, 2010). Available via ProQuest Digital Dissertations. Includes bibliographical references (p. 223-234). Also issued in print.

Following natural language instructions requires transforming language into situated conditional procedures; robustly following instructions, despite the director's natural mistakes and omissions, requires the pragmatic combination of language, action, and domain knowledge. This dissertation demonstrates a software agent that parses, models and executes human-written natural language instructions to accomplish complex navigation tasks. We compare the performance against people following the same instructions. By selectively removing various syntactic, semantic, and pragmatic abilities, this work empirically measures how often these abilities are necessary to correctly navigate along extended routes through unknown, large-scale environments to novel destinations. To study how route instructions are written and followed, this work presents a new corpus of 1520 free-form instructions from 30 directors for 252 routes in three virtual environments. 101 other people followed these instructions and rated them for quality, successfully reaching and identifying the destination on only approximately two-thirds of the trials. Our software agent, MARCO, followed the same instructions in the same environments with a success rate approaching human levels. Overall, instructions subjectively rated 4 or better of 6 comprise just over half of the corpus; MARCO performs at 88% of human performance on these instructions. MARCO's performance was a strong predictor of human performance and ratings of individual instructions. Ablation experiments demonstrate that implicit procedures are crucial for following verbal instructions using an approach integrating language, knowledge and action. Other experiments measure the performance impact of linguistic, execution, and spatial abilities in successfully following natural language route instructions.

One of the main objectives of software development in scientific computing is efficiency. Being focused on highly specialized application domain, important software quality metrics, e.g., usability, extensibility ,etc may not be amongst the list of primary objectives. In this research, we have studied the design structures and iterative releases of scientific research software using Design Structure Matrix(DSM). We implemented a DSM partitioning algorithm using sparse matrix data structure Compressed Row Storage(CRS), and its timing was better than those obtained from the most widely used C++ library boost. Secondly, we computed several architectural complexity metrics, compared releases and total release costs of a number of open source scientific research software. One of the important finding is the absence of circular dependencies in studied software which attributes to the strong emphasis on computational performance of the code. Iterative release analysis indicates that there might be a correspondence between “clustering co-efficient” and “release rework cost” of the software. / x, 87 leaves : ill. ; 29 cm