Verifying Properties of Process Definitions

Verifying Properties of Process Definitions

Jamieson M. Cobleigh, Lori A. Clarke, and Leon J. OsterweilLaboratory for Advanced Software Engineering Research

University of Massachusetts Amherst

http://laser.cs.umass.edu/

Thanks to Aaron Cass, Sandy Wise, and Hyungwon Lee

Outline Process Example Process Analysis of the Process Conclusions

What is a Process?

Complex Task

Complex Task

Resources

Artifacts

Agents

Examples:•Design•Configuration Management•e-commerce

Example: An Auction Need to coordinate bidders and

auctioneer These may be distributed over a

network May be human users or computer

programs Want an process definition that

describes how to conduct an auction

A process definition language

Graphical language Has rigorous formal semantics specified Supports

Concurrency Resource Management Exceptions Choice steps to give humans users flexibility Pre- and post-requisites

Little-JILLittle-JIL

Little-JIL Step

Step Name

Interface Resources Used Exceptions Thrown Parameters

Pre-requisite

Post-requisite

Exception Handling Control Flow

Substep Sequencing

Accept One BidSubmit Bid

BidIsHigher

Accept Bids From Bidder

Update Best BidBidIsBetter

Sequencing Badges:

Sequential Parallel Choice

Try

Open-Cry Auction

AuctionNotClosed Accept One Bid Accept Bids From Bidder

Close Auction

NoMoreBiddersAuctionClosed

Accept One BidSubmit Bid

BidIsHigher

Accept Bids From Bidder

Update Best BidBidIsBetter

Sequencing Badges:

Sequential Parallel Choice

Try

Open-Cry Auction

AuctionNotClosed Accept One Bid Accept Bids From Bidder

Exception Badges: Rethrow Continue Complete Restart

NoMoreBidders

AuctionClosedBidNotHigherBidNotBetterDeadlineExpired

Close Auction

Modeling Processes This process is intuitively easy to

understand However, it still has complicated

control structures These constructs can mask erroneous

behavior Even high-level process definitions

need to be validated

Auction Concerns Are late bids considered? Does the highest bidder win the

auction? Is the auction vulnerable to fraud?

FLow Analysis for VERification of Systems Can verify concurrent and sequential

software Uses an efficient state propagation

algorithm Worst case bounds: O(N2·S) Relatively language independent: Ada, Java, C+

+, Jovial Can incrementally add information to the

analysis to improve precision

FLAVERS

FLAVERS Overview

Property Specification

Software Software Translator

TFG State Propagation

Property Translator

Property FSA

Constraint FSA

. . .

Results

s

Little-JIL Human Translator

FLAVERS Model A Trace Flow Graph (TFG)

Derived from labeled Control Flow Graphs (CFG)

Labels represent events of interest

Need CFG models for Little-JIL constructs

Leaf Step Model

A Choice Step

Do B

Choice

Do A Do C

Do A

Do C

Do B

A Completed

Do B

Do C

Choice

A Terminated

Choice Complete

d … …

… …

Choice Step Model

Properties Checked No Late Bids Accepted

Checked on the Open-Cry Auction Inconclusive Results

Several process experts studied the example in detail without noticing the fault

Need to add an “AuctionNotClosed” prerequisite to “Update Best Bid”

Race Condition Property Another property involved data flow There is a variable best that keeps

track of the best bid seen so far Can be used by multiple steps

concurrently Want to ensure there is no race

condition

Race Condition Can Exist Determined a race condition can

exist Auctioneer could be considering two

bids at the same time Two updates to best occur The final value of best depends on

the order of the updates

No Race Condition Need to ensure proper access to

variable best Requires knowledge of agent

behavior Proved that if no access control, a

race condition can occur Proved that with a lock on best, no

race condition can occur

Analysis Results

Property TFG Nodes TFG Edges Result Time (s)No Late Bids Accepted 216 11,837 Inconclusive - fault 6.56No Late Bids Accepted 316 30,881 Conclusive 41.10Possible Race Condition 327 35,788 Inconclusive - fault 143.25No Race Condition (no lock) 189 7,710 Inconclusive - fault 15.07No Race Condition (with lock) 269 20,910 Conclusive 17.52

The Little-JIL program had 8 steps

Conclusions Process models have strengths and weaknesses

Leads to intuitive understanding Can mislead people into believing they understand the

process Our example illustrates how important it it to

validate processes FLAVERS successfully analyzed the Little-JIL

process There is a tension between expressiveness and

analyzability Humans require flexibility, leading to more complex

analysis