Automatic extraction of assertions from execution traces of behavioural models

Several approaches exist for specification mining of hardware designs. Most of them work at RTL and they extract assertions in the form of temporal relations between Boolean variables. Other approaches work at system level (e.g., TLM) to mine assertions that specify the behaviour of the communication protocol. However, these techniques do not generate assertions addressing the design functionality. Thus, there is a lack of studies related to the automatic mining of assertions for capturing the functionality of behavioural models, where logic expressions among more abstracted (e.g., numeric) variables than bits and bit vectors are necessary. This paper is intended to fill in the gap, by proposing a tool for automatic extraction of temporal assertions from execution traces of behavioural models by adopting a mix of static and dynamic techniques.

[1]  Ilan Beer,et al.  FoCs: Automatic Generation of Simulation Checkers from Formal Specifications , 2000, CAV.

[2]  George S. Avrunin,et al.  Patterns in property specifications for finite-state verification , 1999, Proceedings of the 1999 International Conference on Software Engineering (IEEE Cat. No.99CB37002).

[3]  Masahiro Fujita,et al.  Dynamic property mining for embedded software , 2012, CODES+ISSS.

[4]  Franco Fummi,et al.  HIFSuite: Tools for HDL Code Conversion and Manipulation , 2010, 2010 IEEE International High Level Design Validation and Test Workshop (HLDVT).

[5]  Manuvir Das,et al.  Perracotta: mining temporal API rules from imperfect traces , 2006, ICSE.

[6]  William G. Griswold,et al.  Dynamically discovering likely program invariants to support program evolution , 1999, Proceedings of the 1999 International Conference on Software Engineering (IEEE Cat. No.99CB37002).

[7]  Sanjit A. Seshia,et al.  Scalable specification mining for verification and diagnosis , 2010, Design Automation Conference.

[8]  Graziano Pravadelli,et al.  Automatic generation of compact formal properties for effective error detection , 2013, 2013 International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS).

[9]  David Lo,et al.  Specification mining of symbolic scenario-based models , 2008, PASTE '08.

[10]  David Tcheng,et al.  GoldMine: Automatic assertion generation using data mining and static analysis , 2010, 2010 Design, Automation & Test in Europe Conference & Exhibition (DATE 2010).

[11]  Chao Liu,et al.  Efficient mining of iterative patterns for software specification discovery , 2007, KDD '07.

[12]  Hannu Toivonen,et al.  TANE: An Efficient Algorithm for Discovering Functional and Approximate Dependencies , 1999, Comput. J..

[13]  Shobha Vasudevan,et al.  A Coverage Guided Mining Approach for Automatic Generation of Succinct Assertions , 2014, 2014 27th International Conference on VLSI Design and 2014 13th International Conference on Embedded Systems.

[14]  Amer Diwan,et al.  Discovering Algebraic Specifications from Java Classes , 2003, ECOOP.

[15]  Shobha Vasudevan,et al.  Word level feature discovery to enhance quality of assertion mining , 2012, 2012 IEEE/ACM International Conference on Computer-Aided Design (ICCAD).

[16]  Shobha Vasudevan,et al.  Automatic generation of assertions from system level design using data mining , 2011, Ninth ACM/IEEE International Conference on Formal Methods and Models for Codesign (MEMPCODE2011).

[17]  Stephen McCamant,et al.  The Daikon system for dynamic detection of likely invariants , 2007, Sci. Comput. Program..

[18]  James R. Larus,et al.  Mining specifications , 2002, POPL '02.