Test Programming by Program Composition and Symbolic Simulation
Name
AI_WP_280.pdf
Description
Main article
Size
1.4 MB
Format
Adobe PDF
Checksum (MD5)
fd75ec0d61e6a3c1ab60b7ec0c297bca
Author(s)
Shirley, Mark H.
Date Issued
November 1985
Publisher
MIT Artificial Intelligence Laboratory
Series/Report no.
MIT Artificial Intelligence Laboratory Working Papers, WP-280
Abstract
Classical test generation techniques rely on search through gate-level circuit descriptions, which results in long runtimes. In some instances, classical techniques cannot be used because they would take longer than the lifetime of the product to generate tests which are needed when the first devices come off the assembly line. Despite these difficulties, human experts often succeed in writing test programs for very complex circuits. How can we account for their success?
We take a knowledge engineering approach to this problem by trying to capture in a program techniques gleaned from working with experienced test programmers. From these talks, we conjecture that expert test programming performance relies in part on two aspects of human problem solving.
First, the experts remember many cliched solutions to test programming problems. The difficulty lies in formalizing the notion of a cliche for this domain. For test programming, we propose that cliches contain goal to subgoal expansions, fragments of test program code, and constraints describing how program fragments fit together. We present an algorithm which uses testing cliches to generate test programs. Second, experts can simulate a circuit at various levels of abstraction and recognize patterns of activity in the circuit which are useful for solving test problems. We argue that symbolic simulation coupled with recognition of which simulated events solve our goals is an effective planning strategy in certain cases. We present a second algorithm which simulates circuit behavior on symbolic inputs at roughly the register transfer level and generates fragments of test programs suitable for use by our first algorithm.
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