VIEWPOINT

2023

COMPREHENSIVEMACHINE-AUGMENTED
ASSURANCE
How machine learning can improveassurance and make smart people
smarter

AUTHORS
Clive Adams
David Boulton

Augmented decision-making tools can improve

James Beckboth the efficiency and effectiveness of expert-led

Michael Eiden

assurance activities. In turn, these tools can deliversignificant business benefits, such as by avoiding
delays in acceptance of new systems, reducing
costs, and de-risking the system that is being
assured. In this Viewpoint, we share ways to utilize
such technology to the greater benefit.

Philippe Monnot
Armand Rotaru

ARTHUR D. LITTLE

VIEWPOINT

BRE AK ING THE TR ADITIONALAS SUR ANCE MOLD

Expert human knowledge and insights are

Traditional assurance activities require expert(ML) offers a solution to this problem. An ML toolengineers to undertake a detailed review ofcan be trained to make inferences about the data

extensive volumes of documentation, which issupplied to it based on inferences that it has

effort-intensive, time-consuming, and costly.

learned from past “training data.” Such a tool canHowever, carefully applied augmented decision-then make independent judgments on new data,

making tools can replace some of the laborious

without human intervention.

notoriously difficult to encode in conventional
computer algorithms. However, machine learning

manual checks normally required (see Figure 1).
The aim is not to replace human experts but toimprove the quality and efficiency of decisions
they make.
In this Viewpoint, we demonstrate how augmented
decision-making tools can be used in practice withindependent safety assessments (ISAs). However,
the tools and techniques are applicable to a wide
variety of other use cases and scenarios.

IT’ S ALL ABOUTREL ATIONSHIP S
Much of an ISA involves considering thecorrectness and completeness of therelationships between different data sets (e.g.,whether system design requirements fulfill safety
requirements or whether subsystem design
requirements correctly implement system design
requirements). Assessments sample check these

WHY USEMACHINE LE ARNING?

relationships. An expert assessor chooses a levelof sampling that allows him or her to reach a

ISA involves making an expert-led judgment on:(1) whether the requirements that have beenplaced on a system are sufficient to ensure its
safety and (2) whether the system has met thosereasonable conclusion on the quality of the data
concerned. The greater the degree of integritya system requires, the greater the breadth anddepth of the sample checking that is needed.

requirements. ISA therefore provides increased
assurance that a system can be deployed andoperated safely. It also typically requires use ofexpert engineers with many years’ experience
Figure 1. Traditional
vs. machine-augmented assurance
in the associated industrial domain.

Figure 1. Traditional vs. machine-augmented assurance

Further checks
are carried out onsuspicious leads

Suspicious issues identified & elevated for examination

Documents sampled based on expert opinion

Traditional assurance

Extended coverage

Comprehensive machine-augmented assurance

Source: Arthur D. Little

COMPREHENSIVE MACHINE-AUGMENTED ASSURANCE

Source: Arthur D. Little

2

ARTHUR D. LITTLE

VIEWPOINTMANAGING L ARGE VOLUMES
OF DOCUMENTATION
System development can involve huge volumesof documentation (data sets). One of the firstchallenges is for an ML model to ingest such
documentation in a manner that allows for easy
reasoning. Natural language processing (NLP)ONE OF THE FIRST
CHALLENGES IS FOR AN
ML MODEL TO INGEST
D O C U M E N TAT I O N I N A
M A N N E R T H AT A L L O W S
FOR E ASY RE ASONING

techniques are therefore used to extract therelevant data contained within each document,
as well as to capture the relationships betweenthem.

The extracted features and relationships
can then be represented semantically using
structured knowledge graphs (as illustrated in

Another key challenge is capturing the meaning

Figure 3). These transform the unstructured dataof each data item to improve the ability to reason

contained in the source documents into a data

about it. Feature engineering is therefore used

set that clearly and precisely embodies the manyto transform the raw data into attributes that

contextual relationships between the different

better represent the data (see an example ine 2. Feature engineering

data items.

Figure 2).

Figure 2. Feature engineering

Raw data

Distinct set of independent featurese 3. Representing data sets using knowledge graphs
Source: Arthur D. Little

Figure 3. Representing data sets using knowledge graphs

ur D. Little

NodeSystem specification
NodeSubsystem specification
Relationship
SATISFIED BY or SATISFIES

Source: Arthur D. Little

COMPREHENSIVE MACHINE-AUGMENTED ASSURANCE

3

ARTHUR D. LITTLE

VIEWPOINTLEARNING FROM EXPERIENCE

TUNING THE ML MODEL

Once the large volume of source data has been

The performance of the ML model is improved

structured into knowledge graphs, iterativein an iterative process, as shown in Figure 5. Inprocesses are used to develop and train an ML

particular, using human experts’ insight, themodel to implement given use cases. As noted

model can be trained on an increasing number

earlier, in the case of ISA, a typical use case isof training data sets. Such data sets are thosedetermining the correctness and completenessin which experts have already made correctof requirement traceability from one level ofinferences on the relationships between therequirements to another (e.g., from system-level

data that they contain. As the machine’s training

requirements to software-level requirements).

improves, it is increasingly able to emulate theA combination of different classes of features cansubjective decisions and insights that the expert

be used to train the ML model for such a use case,human engineers employ (decisions and insights

as illustrated in Figure 4.

that such experts may find difficult to articulate

In this way, the model can emulate the patternand/or which they may make subconsciously).

matching that is intuitively performed by expert
human engineers. This includes, for example,
evaluating the similarity of low-level data
(e.g., functions, variables, constants, expressions)
included in the documents. The structured
representation of data in knowledge graphs
facilitates the ML model to achieve such patternmatching.
The model learns from “past experience” by using
input from expert engineers. For example, such
input may reveal which relationships between

AS THE MACHINE’ S
TR AINING IMPROVES,I T I S I N C R E A S I N G LY
A B L E T O E M U L AT E T H E
SUBJECTIVE DECISIONSA N D I N S I G H T S T H AT
THE EXPERT HUMAN
E N G IN E E R S E M PLOY

system and software requirements experts havepreviously considered correct and which theynot.
ure 4. Training MLhave
modelsusing different classes oftures
Figure 4. Training ML models using different classes of features

Text-based features (NLP)
Use case–specific and driven by how ISA
is typically performed:•Entity similarity•

Expression similarity•

Complexity measures•

Keyword presence

QA-based features
Based on designer assessment, priorto ISA:
•Was the analyzed artifact internallyflagged as problematic?

•What were the reviewers’ comments?

Graph-based features
Graph algorithms to assess internal
structure of graph:•Community measures– Evaluate how groups of nodes
are clustered or partitioned•

Centrality measures– Determine importance ofdistinct nodes in a network

Artifact 1

Artifact 2
Artifact 3

Artifact 4

𝐹𝐹𝐹𝐹11
𝐹𝐹𝐹𝐹21
𝐹𝐹𝐹𝐹31
𝐹𝐹𝐹𝐹41

𝐹𝐹𝐹𝐹12
𝐹𝐹𝐹𝐹22
𝐹𝐹𝐹𝐹32
𝐹𝐹𝐹𝐹42

𝐹𝐹𝐹𝐹13
𝐹𝐹𝐹𝐹23
𝐹𝐹𝐹𝐹33
𝐹𝐹𝐹𝐹43

𝐹𝐹𝐹𝐹14
𝑌𝑌𝑌𝑌1
𝐹𝐹𝐹𝐹24𝑌𝑌𝑌𝑌2=𝐹𝐹𝐹𝐹34
𝑌𝑌𝑌𝑌3
𝐹𝐹𝐹𝐹44𝑌𝑌𝑌𝑌4ISA outcomes(PASS/FAIL)

Source: Arthur D. Little

Arthur D. Little

COMPREHENSIVE MACHINE-AUGMENTED ASSURANCE4ARTHUR D. LITTLE

VIEWPOINTFigure 5. Iterative tuning of performance of machine model
Figure 5. Iterative tuning of performance of machine model
1. Select features

Select a subset of NLP-,QA-, and graph-based features

1

2. Split data

2

4. Evaluate trainedmodel performance

Split data set intotraining set and test set

4

Evaluate model performanceto establish improvements needed

3
3. Train & test model

Train selected model using training
set; test trained model on test set
Source: Arthur D. Little

Industry-standard artificial intelligence (AI)
metrics, precision and recall, can also be usedSource: Arthur D. Little

to tailor the nature of the results provided bythe model. For example, precision and recall
are inherently linked, in the form of a trade-off.
An appropriate trade-off policy can therefore

A N A P P R O P R I AT E
TR ADE- OFF POLICY CAN
BE CHOSEN DEPENDING
O N T H E N AT U R E O F T H E
ASSESSMENT REQUIRED

be chosen depending on the nature of theassessment required:- Conservative policy (high precision, low

The ML tool can provide the expert assessor withrecall). If the model says the relationship is aa prioritized list of relationships for examination.

FAIL, you can almost be sure that it is an actual

By choosing the highest-priority relationships

FAIL. But you may miss many (less certain)in the list (those that are most likely to be true

actual FAILs. In this case, expert assessors

FAILs), those areas of the development thathave good confidence in the documentation

are incorrect are more readily identified. Thisand want to focus on identifying the likely

approach also has the advantage of identifying

small set of errors.

areas of the development that may be most prone- Liberal policy (low precision, high recall).
The model will wrongly label a lot ofrelationships as FAILs, but it will also detect
most of the actual FAILs. In this case, expert
assessors have lower confidence in the
documentation and need to “cast the net

to failure (by identifying anomalies in a commonarea), where more expert investigation may bewarranted. Such an approach may prove more
efficient than selecting a sample of relationships
for assessment based only on the expert’sintuition.

wide” to ensure they catch as many errors
as possible.

COMPREHENSIVE MACHINE-AUGMENTED ASSURANCE

5

ARTHUR D. LITTLE

VIEWPOINT

OTHER BENEFIT S- Expert integration. The more the machine
can incorporate the insights and tacit

Augmented decision-making tools provide

knowledge held by expert human engineers,

additional benefits as well, including:the better the potential efficiency and- Faster outcomes. Replacing some of themore effort-intensive and labor-intensive
manual checks of compliance with processes
guarantees faster outcomes, while stillproviding the same (or potentially higher)
levels of assurance.

- High scalability. Persistent and highly

reusable knowledge graphs can be readily
expanded with additional documents anduse cases.

- Timely delivery and de-risking. The

efficiencies that can be generated increase
the likelihood of on-time delivery of assurance
projects, thereby reducing the chanceof cost overruns and the need for use ofcontingencies. Increased assurance canassurance gains. Use of knowledge graphs
is a major enabler in this regard. As expert
human engineers reason about the data
sets by navigating the knowledge graph,the machine can learn the reasoning of
the experts.

- Applicable to a wide range of use cases.
One of the most exciting aspects of thistechnology is its applicability to manydifferent use cases, not just ISA. Requirements
verification, a simple extension of ISA, is
one such example. The technology has been
applied to the verification of compliance of
public service providers’ policy and procedures
to regulatory requirements, with promising
results.

also de-risk the system that is being assured.

COMPREHENSIVE MACHINE-AUGMENTED ASSURANCE

6

ARTHUR D. LITTLE

VIEWPOINT

CONCLUSION

FUTURE ASSURANCEMACHINE LE ARNING CAN IMPROVE
THE EFFECTIVENESS AND EFFICIENCYO F H U M A N E X P E R T- L E D A S S U R A N C E

Augmented decision-making tools and techniques are applicable toa wide variety of use cases and scenarios, offering opportunities for
improved quality and efficiency of human expert-led decisions. By
helping to avoid delays in integrating new systems, reducing costs,and de-risking the system that is being assured, machine learning
can improve the effectiveness and efficiency of human expert-led
assurance, offering several benefits for many businesses, including:1

Wide applicability. The tools and techniques employed havebroad applicability. Any activity where human experts mustreason about the relationships involved in large data sets isa candidate for the tool.

2

Making smart people smarter. The aim is not to replace humanexperts but to augment their decisions with the assistance of ML
tools and enhancing the quality and efficiency of the decisions
they make.

3

Application across a wide range of industrial domains. The
technology is domain-agnostic and, once the rules have beenlearned, is independent of the industry.

COMPREHENSIVE MACHINE-AUGMENTED ASSURANCE

7

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