Enhancing quality of assertion generation: methods for automatic assertion generation and evaluation

We present methods for automatically generating and evaluating register transfer level (RTL) assertions. We detail the GoldMine methodology and each of its data mining algorithms. We introduce the Best-Gain Decision Forest algorithm to mine concise RTL assertions. We develop an assertion ranking methodology. We define assertion importance, complexity, rank and ideality and we detail methods to compute each of them. We present a case study and experimental results to demonstrate the effectiveness of assertion rank. We develop an assertion rank aggregation methodology. We define assertion coverage and expectedness. We aggregate rankings for assertion importance, complexity, coverage and expectedness. We present experimental results to demonstrate the value of these metrics and the rank aggregation methodology. We rigorously analyze the performance of each data mining algorithm in GoldMine. We present experimental results that demonstrate each algorithm's performance with respect to various metrics.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2013

Author dc:creator

• Hertz, Samuel

Contributors dc:contributor

• Vasudevan, Shobha

• assertion
• assertion generation
• assertion evaluation
• assertion ranking
• GoldMine
• static analysis
• data mining
• evaluation
• ranking
• verification
• validation

Statement dc:rights

• Copyright 2013 Samuel Hertz

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/55156

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/55156