Title :
Data mining diagnostics and bug MRIs for HW bug localization
Author :
Farkash, Monica ; Hickerson, Bryan ; Samynathan, Balavinayagam
Author_Institution :
Univ. of Texas, Austin, TX, USA
Abstract :
This paper addresses the challenge of minimizing the time and resources required to localize bugs in HW dynamic functional verification. Our diagnostics solution eliminates the need to back trace from point of failure to its origin, decreasing the overall debugging time. The proposed solution dynamically analyses data extracted from sets of passing and failing tests to identify behavior discrepancies, which it expresses as source code lines, coverage events and timing during simulation. It also provides a visual diagnostic support, an image of the behavior discrepancies in time which we call a Machine Reasoning Image (MRI). This paper describes in detail our data mining solution based on coverage data, HDL hierarchies and time analysis of coverage events. Our approach brings a data mining solution to the problem of HW bug localization. It defines new concepts, provides in-depth analysis, presents supporting algorithms, and shows actual results on archetypical problems from PowerPC core verification as an industrial application.
Keywords :
computer debugging; data mining; inference mechanisms; program diagnostics; source code (software); HDL hierarchies; HW bug localization; HW dynamic functional verification; behavior discrepancies; bug MRI; coverage events; data mining diagnostics; debugging time; diagnostics solution; failing tests; failure point; machine reasoning image; passing tests; source code lines; visual diagnostic support; Data mining; Debugging; Decision trees; Magnetic resonance imaging; Monitoring; Timing; Visualization; EDA tools; bug localization; debugging; diagnostics; verification;
Conference_Titel :
Design, Automation & Test in Europe Conference & Exhibition (DATE), 2015
Conference_Location :
Grenoble
Print_ISBN :
978-3-9815-3704-8