A methodology for the capture and analysis of hybrid data: a case study of program debugging

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A methodology for producing reliable software, volume 1

An investigation into the areas having an impact on producing reliable software including automated verification tools, software modeling, testing techniques, structured programming, and management techniques is presented. This final report contains the results of this investigation, analysis of each technique, and the definition of a methodology for producing reliable software

Numerical aerodynamic simulation facility preliminary study: Executive study

A computing system was designed with the capability of providing an effective throughput of one billion floating point operations per second for three dimensional Navier-Stokes codes. The methodology used in defining the baseline design, and the major elements of the numerical aerodynamic simulation facility are described

Parallel software tools at Langley Research Center

This document gives a brief overview of parallel software tools available on the Intel iPSC/860 parallel computer at Langley Research Center. It is intended to provide a source of information that is somewhat more concise than vendor-supplied material on the purpose and use of various tools. Each of the chapters on tools is organized in a similar manner covering an overview of the functionality, access information, how to effectively use the tool, observations about the tool and how it compares to similar software, known problems or shortfalls with the software, and reference documentation. It is primarily intended for users of the iPSC/860 at Langley Research Center and is appropriate for both the experienced and novice user

Complaint-driven Training Data Debugging for Query 2.0

As the need for machine learning (ML) increases rapidly across all industry sectors, there is a significant interest among commercial database providers to support "Query 2.0", which integrates model inference into SQL queries. Debugging Query 2.0 is very challenging since an unexpected query result may be caused by the bugs in training data (e.g., wrong labels, corrupted features). In response, we propose Rain, a complaint-driven training data debugging system. Rain allows users to specify complaints over the query's intermediate or final output, and aims to return a minimum set of training examples so that if they were removed, the complaints would be resolved. To the best of our knowledge, we are the first to study this problem. A naive solution requires retraining an exponential number of ML models. We propose two novel heuristic approaches based on influence functions which both require linear retraining steps. We provide an in-depth analytical and empirical analysis of the two approaches and conduct extensive experiments to evaluate their effectiveness using four real-world datasets. Results show that Rain achieves the highest recall@k among all the baselines while still returns results interactively.Comment: Proceedings of the 2020 ACM SIGMOD International Conference on Management of Dat

BNP Paribas: Systems Analysis and Concept Development

This project focuses on the re-engineering of the operations within BNP Paribas\u27 internal investment deals, workflow and risk analysis programs. This includes evaluating their current strategies for data storage and delivery and working to recognize obstacles in the current infrastructure. The goal is to redesign the valuation process for spreadsheet deals with a focus on security and documentation and to develop a proof-of-concept prototype for a new information system using components from the previous analysis