Browsing by Author "Krishnamurthy, Rahul"

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    Framework for Automatic Translation of Hardware Specifications Written in English to a Formal Language
    Krishnamurthy, Rahul (Virginia Tech, 2022-11-01)
    The most time-consuming component of designing and launching hardware products to market is the verification of Integrated Circuits (IC). An effective way of verifying a design can be achieved by adding assertions to the design. Automatic translation of hardware specifications from natural language to assertions in a formal representation has the potential to improve the verification productivity of ICs. However, natural language specifications have the characteristics of being imprecise, incomplete, and ambiguous. An automation framework can benefit verification engineers only if it is designed with the right balance between the ease of expression and precision of meaning allowed for in the input natural language specifications. This requirement introduces two major challenges for designing an effective translation framework. The first challenge is to allow the processing of expressive specifications with flexible word order variations and sentence structures. The second challenge is to assist users in writing unambiguous and complete specifications in the English language that can be accurately translated. In this dissertation, we address the first challenge by modeling semantic parsing of the input sentence as a game of BINGO that can capture the combinatorial nature of natural language semantics. BINGO parsing considers the context of each word in the input sentence to ensure high precision in the creation of semantic frames. We address the second challenge by designing a suggestion and feedback framework to assist users in writing clear and coherent specifications. Our feedback generates different ways of writing acceptable sentences when the input sentence is not understood. We evaluated our BINGO model on 316 hardware design specifications taken from the documents of AMBA, memory controller, and UART architectures. The results showed that highly expressive specifications could be handled in our BINGO model. It also demonstrated the ease of creating rules to generate the same semantic frame for specifications with the same meaning but different word order. We evaluated the suggestion and rewriting framework on 132 erroneous specifications taken from AMBA and memory controller architectures documents. Our system generated suggestions for all the specs. On manual inspection, we found that 87% of these suggestions were semantically closer to the intent of the input specification. Moreover, automatic contextual analysis of the rewritten form of the input specification allowed the translation of the input specification with different words and different order of words that were not defined in our grammar.
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    Sentiment and Topic Analysis
    Bartolome, Abigail; Bock, Matthew; Vinayagam, Radha Krishnan; Krishnamurthy, Rahul (Virginia Tech, 2017-05-03)
    The IDEAL (Integrated Digital Event Archiving and Library) and Global Event and Trend Archive Research (GETAR) projects have collected over 1.5 billion tweets, and webpages from social media and the World Wide Web and indexed them to be easily retrieved and analyzed. This gives researchers an extensive library of documents that reflect the interests and sentiments of the public in reaction to an event. By applying topic analysis to collections of tweets, researchers can learn the topics of most interest or concern to the general public. Adding a layer of sentiment analysis to those topics will illustrate how the public felt in relation to the topics that were found. The Sentiment and Topic Analysis team has designed a system that joins topic analysis and sentiment analysis for researchers who are interested in learning more about public reaction to global events. The tool runs topic analysis on a collection of tweets, and the user can select a topic of interest and assess the sentiments with regard to that topic (i.e., positive vs. negative). This submission covers the background, requirements, design and implementation of our contributions to this project. Furthermore, we include data, scripts, source code, a user manual, and a developer manual to assist in any future work.