AI-T: Software Testing Ontology for AI-based Systems
J. I. Olszewska
School of Computing and Engineering, University of West Scotland, U.K.
Keywords:
Intelligent Systems, Software Testing, Software Engineering Ontology, Ontological Domain Analysis and
Modeling, Knowledge Engineering, Knowledge Representation, Interoperability, Decision Support Systems,
Transparency, Accountability, Unbiased Machine Learning, Explainable Artificial Intelligence (XAI).
Abstract:
Software testing is an expanding area which presents an increasing complexity. Indeed, on one hand, there is
the development of technologies such as Software Testing as a Service (TaaS), and on the other hand, there is
a growing number of Artificial Intelligence (AI)-based softwares. Hence, this work is about the development
of an ontological framework for AI-softwares’ Testing (AI-T), which domain covers both software testing
and explainable artificial intelligence; the goal being to produce an ontology which guides the testing of AI
softwares, in an effective and interoperable way. For this purpose, AI-T ontology includes temporal interval
logic modelling of the software testing process as well as ethical principle formalization and has been built
using the Enterprise Ontology (EO) methodology. Our resulting AI-T ontology proposes both conceptual and
implementation models and contains 708 terms and 706 axioms.
1 INTRODUCTION
Artificial Intelligence (AI) systems are nowadays per-
vasive in our Society, from face recognition (Raji
et al., 2020) to cloud robotic systems (Pignaton de
Freitas et al., 2020a). However, the black-box nature
of many of these systems (Guidotti et al., 2018) leads
to growing public concerns (Koene, 2017). There-
fore, appropriate testing of such AI-based softwares
is crucial to provide people and industry with de-
pendable and ethical intelligent systems. Indeed,
new-generation softwares are expected to be effi-
cient, whilst transparent, safe, and secure (Olszewska,
2019b).
Software testing consists usually in a range of
activities from manual testing to automated testing
(Prasad, 2008), or Testing as a Service (TaaS) (Yu
et al., 2010). They are applied throughout the soft-
ware development life-cycle (SDLC) (Sommerville,
2015) and can be grouped into three processes,
namely, the Organizational Test Process (OTP), the
Test Management Process (TMP), and the Dynamic
Test Process (DTP), which corresponding documen-
tations report about (Alaqail and Ahmed, 2018).
Thus, OTP is utilized for the development and
management of the organizational test specifications
and includes three phases: to develop organizational
test specification (OTP1); monitor and control the use
of organizational test specifications (OTP2); and up-
date organizational test specifications (OTP3). TMPs
are used at the project level, and the three TMPs are
defined as: test planning (TMP1); test monitoring and
control (TMP2); and test completion (TMP3). On the
other hand, the four DTPs comprise: test design &
implementation (DTP1); test environment set-up &
maintenance (DTP2); test execution (DTP3); and test
incident reporting (DTP4). These DTPs are used to
carry out dynamic testing within a particular level of
testing (i.e. unit testing, integration testing, system
testing, and acceptance testing) or type of testing (e.g.
performance testing, security testing, usability test-
ing) (IEEE, 2013a), (IEEE, 2013b), (IEEE, 2013c),
(IEEE, 2015b), (IEEE, 2016).
In particular, ontologies are a convenient approach
for capturing a conceptualization of the software test-
ing domain (Tebes et al., 2019). Indeed, ontologies
define terms, properties, relationships, and axioms ex-
plicitly, unambiguously, and in an interoperable way
which could be understood by both humans and ma-
chines, since ontologies directly support automated
reasoning about the domain knowledge representation
(Chianese et al., 2009).
Hence, some ontologies have been developed for
software testing (Ferreira de Souza et al., 2013a).
These ontologies share common software testing
knowledge, but differ among other, by their termino-
Olszewska, J.
AI-T: Software Testing Ontology for AI-based Systems.
DOI: 10.5220/0010147902910298
In Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineer ing and Knowledge Management (IC3K 2020) - Volume 2: KEOD, pages 291-298
ISBN: 978-989-758-474-9
Copyright
c
 2020 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
291

logical variations, since their terms are extracted from
different body of knowledge, such as SoftWare Engi-
neering Body of Knowledge (SWEBOK) (Freitas and
Vieira, 2014), International Software Testing Qualifi-
cations Board (ISTQB) (Arnicans et al., 2013), and
standards such as IEEE 610-1990 (Ferreira de Souza
et al., 2013b), ISO/IEC 12207-1995 (Barbosa et al.,
2006), ISO/IEC 9126-2001 (Cai et al., 2009), or IEEE
829-2008 (Vasanthapriyan et al., 2017).
Howbeit, many of these ontologies lack of axiom-
atization, e.g. (Guo et al., 2011), (Sapna and Mo-
hanty, 2011), (Iliev et al., 2012), and some are rather
mere taxonomies like (Cai et al., 2009) or (Anandaraj
et al., 2011).
On the other hand, most of the software testing on-
tologies have a limited domain coverage, such as Web
Service testing (Huo et al., 2003), Graphical User In-
terface (GUI) testing (Li et al., 2009), or Linux testing
(Bezerra et al., 2018).
Moreover, no specific ontological methodologies
have been applied to the development of ontologies
such as Test Ontology Model (TOM) (Bai et al.,
2008), Testing Maturing Model (TMM) ontology
(Ryu et al., 2008), or Regression Test Execution
(RTE) ontology (Campos et al., 2017).
These developed ontologies are useful, e.g. for de-
termining dependability (Looker et al., 2005), prob-
ing interoperability (Yu et al., 2005), performing test
reuse (Li and Zhang, 2012), test case generation
(Crapo and Moitra, 2019), or test automation (Pay-
dar and Kahani, 2010), but many of them do not have
an implementation such as the Testing as a Service
(TaaS) ontology (Yu et al., 2009).
Actually, very few software testing ontologies
present both conceptual and implementation models,
and these ontologies formalize a number of terms,
e.g. 45 terms in the Reference Ontology on Software
Testing (ROoST) (Ferreira de Souza et al., 2013b),
52 terms in OntoTest (Barbosa et al., 2006), 63
terms in the Software Testing Ontology for Web Ser-
vice (STOWS) (Huo et al., 2003), and 194 terms in
the Software Test Ontology (SwTO) (Bezerra et al.,
2018).
Besides, ontologies have been designed for Arti-
ficial Intelligence (AI) domain (Bayat et al., 2016)
and focus on intelligent agents (IA) (Valente et al.,
2010), autonomous systems (AS) (Bermejo-Alonso
et al., 2010), internet of things (IoT) (Ma et al., 2014),
cyber-physical systems (CPS) (Dai et al., 2017), smart
production systems (Terkaj and Urgo, 2014), or cloud
robotic systems (CRS) (Pignaton de Freitas et al.,
2020b).
However, no dedicated ontology has been devel-
oped for testing AI-based softwares.
In this paper, we propose a novel domain ontology
for AI-softwares’ Testing (AI-T).
Its core knowledge includes the software testing
domain as well as the ethical artificial intelligence do-
main.
AI-T ontology has been coded in Web Ontology
Language Descriptive Logic (OWL DL), which is
considered as the international standard for express-
ing ontologies and data on the Semantic Web (Guo
et al., 2007), and using the Protege tool in conjunc-
tion with the FaCT++ reasoner (Tsarkov, 2014).
The resulting expert system provides an interoper-
able solution to test AI-based softwares.
Thus, the contributions of this paper is manyfold.
Indeed, as far as we know, we propose the first ontol-
ogy which purpose is to aid the testing of AI-based
softwares. For that, we introduce the software ethical
principle testing concepts along with the mathemati-
cal formalization of the software testing process using
temporal-interval descriptive logic (DL). Besides, it is
the first time Enterprise Ontology (EO) methodology
is applied to develop a software testing ontology.
The paper is structured as follows. Section 2
presents the purpose and the building of our ontology
for AI-based-software Testing (AI-T), while its eval-
uation and documentation are described in Section 3.
Conclusions are drawn up in Section 4.
2 PROPOSED AI-T ONTOLOGY
To develop the AI-T ontology, we followed the
ontological development life cycle (Gomez-Perez
et al., 2004) based on the Enterprise Ontology (EO)
Methodology (Dietz and Mulder, 2020), since EO is
well suited for software engineering applications (van
Kervel et al., 2012).
The adopted ontological development methodol-
ogy consists of four main phases (Olszewska and Al-
lison, 2018), which cover the whole development cy-
cle, as follows:
1. identifications of the purpose of the ontology
(Section 2.1);
2. ontology building which consists of three parts:
the capture to identify the domain concepts and
their relations; the coding to represent the ontol-
ogy in a formal language; and the integration to
share ontology knowledge (Section 2.2);
3. evaluation of the ontology to check that the de-
veloped ontology meets the scope of the project
(Section 3.1);
4. documentation of the ontology (Section 3.2).
KEOD 2020 - 12th International Conference on Knowledge Engineering and Ontology Development
292

Table 1: Artificial-Intelligence-based software’ Testing (AI-T) Body of Knowledge (BoK) summary.
Sample Concepts Terminology Sources
Software Testing Strategy SWEBOK, IEEE 29119-1-2013
Software Testing Process IEEE 29119-2-2013
Organizational Test Process
Test Management Process
Dynamic Test Process
Software Testing Documentation IEEE 29119-3-2013
Test Plan
Software Testing Level ISTQB
Unit Testing
Integration Testing
System Testing
Acceptance Testing
Software Testing Technique IEEE 29119-4-2015, IEEE 24765-2017
Dynamic Testing
Specification-Based Technique
Structure-Based Technique
Static Testing
Software Testing Type
Software Quality Testing IEEE 29119-4-2015, (Sommerville, 2015)
Software Safety Testing IEEE 1228-1994, (Leveson, 2020)
Software Ethical Principle Testing IEEE EADv2-2019, VDEv1-2020, IEEE 70xx
Application-Based Testing (e.g. AI Software) IEEE 1872-2015, (Leonard et al., 2017), (Olszewska, 2019a)
Software Comparison Testing (Sommerville, 2015)
Software Testing Measure & Metric (Pressman, 2010), (Olszewska, 2019b)
2.1 Ontology Purpose
The scope of this AI-T domain ontology is to assist
testers with the testing of AI-based softwares. Indeed,
software testing originates from professional prac-
tices that have led to a variety of testing approaches
and generated different nomenclatures of the testing
types and techniques. On the other hand, the testing
of AI-based softwares is a new area without estab-
lished models yet (Hutchinson and Mitchell, 2019).
Therefore, the AI-T ontology aims to contribute to
the elicitation of the Software Testing knowledge and
the formalization of concepts for AI-based softwares’
testing.
Since ontologies intrinsically allow to represent
knowledge unambiguously, to share information in an
interoperable way, and to perform automated reason-
ing, the AI-T ontology purpose is to contribute to (i)
support human tester(s) in managing software testing;
(ii) help human testers in testing AI-based softwares;
(iii) guide intelligent agent(s) in generating/reusing
test cases; (iv) facilitate intelligent agent(s)’ learning
about software testing; (v) aid collaborative mixed
human-intelligent agent teams in testing software
agents.
2.2 Ontology Building
The knowledge capture consists in the identification
of concepts and relations of both Software Testing
and Explainable Artificial Intelligence domains as
summed up in Table 1.
Hence, AI-T body of knowledge for software en-
gineering and in particular software testing comprises
the SWEBOK guide (Bourque and Fairley, 2014),
the ISTQB terminology (ISTQB, 2020), and soft-
ware engineering standards such as IEEE 24765-2017
(IEEE, 2017) for the software engineering vocab-
ulary, IEEE 29119-1-2013 (IEEE, 2013a) for soft-
ware testing concepts and definitions, IEEE 29119-2-
2013 (IEEE, 2013b) for the software testing test pro-
cesses, IEEE 29119-3-2013 (IEEE, 2013c) for soft-
ware testing test documentation, IEEE29119-4-2015
(IEEE, 2015b) for software testing test techniques,
IEEE29119-5-2016 (IEEE, 2016) for software test-
ing keyword-driven testing, (IEEE, 1994), (Leveson,
2020) for software safety testing as well as books such
as (Sommerville, 2015) for software quality testing
and (Pressman, 2010) for software measures and met-
rics.
On the other hand, AI-T body of knowledge for
ethical AI general principles (i.e. human rights,
well-being, accountability, transparency, awareness
AI-T: Software Testing Ontology for AI-based Systems
293

of misuse) includes the IEEE Ethically Aligned De-
sign guidelines (EADv2) (IEEE, 2019), the inter-
disciplinary framework for AI ethical practice (e.g.
transparency, accountability, privacy, justice, reliabil-
ity, environmental sustainability) (Hallensleben and
Hustedt, 2020), the ethical standards in robotics and
AI (Koene et al., 2018b), (Winfield, 2019), (Bryson
and Winfield, 2017), (Olszewska et al., 2018), (Ol-
szewska et al., 2020) and subsequent standards such
as IEEE P7000 (Spiekermann, 2017), IEEE P7001
for transparency (Wortham et al., 2016), IEEE P7003
(Koene et al., 2018a) for accountability, or IEEEE
P7010 (IEEE, 2020) for well-being measurement.
The knowledge coding is done in Descriptive
Logic (DL) and uses temporal-interval logic relations
as introduced in (Olszewska, 2016). Thence, within
the Software Testing Process which has been de-
scribed in Section 1, the concept of ‘Organizational
Test Process (OTP)’ is defined in DL, as follows:
Organizational Test Process v So ftware Testing Process
u ∃hasProcess
={OTP
1
=Develop OTS
n
}
u ∃hasProcess
={OTP
2
=Monitor OTS
n
}
u ∃hasProcess
={OTP
3
=U pdate OT S
n
}
,
(1)
with OT S
n
, an organizational test specification.
Furthermore, the OTP processes could be for-
malised in temporal DL, as follows:
OT P Implementation v Organizational Test Process
u (t
1
...t
3
)
(OT P
1
mOT P
2
)...(OT P
1
< OT P
3
)
· (OT P
1
@t
1
u ... u OT P
3
@t
3
),
(2)
with be f ore and meet, the temporal-interval relations
as defined respectively in temporal DL:
P
i
< P
j
≡ be f ore(P
i
@t
i
, P
j
@t
j
) v Temporal Relation
u (t
i
)(t
j
)
(t
i
+
< t
j
−
)
· (P
i
@t
i
u P
j
@t
j
),
(3)
P
i
mP
j
≡ meet(P
i
@t
i
, P
j
@t
j
) v Temporal Relation
u (t
i
)(t
j
)
(t
i
+
= t
j
−
)
· (P
i
@t
i
u P
j
@t
j
),
(4)
where the temporal DL symbol  represents
the temporal existential qualifier, and where
a time interval is an ordered set of points
T = {t} defined by end-points t
−
and t
+
, such
as (t
−
,t
+
) : (∀t ∈ T )(t > t
−
) ∧ (t < t
+
).
The concept of ‘Test Management Process
(TMP)’ could be represented in DL, as follows:
Figure 1: Main classes of the Software Testing domain.
Figure 2: Main properties of the Ethical AI domain.
Test Management Process v So f tware Testing Process
u ∃hasProcess
={T MP
1
=Test Planning}
u ∃hasProcess
={T MP
2
=Test Monitoring and Control}
u ∃hasProcess
={T MP
3
=Test Completion}
.
(5)
Moreover, the TMP processes could be formalised
in temporal DL, as follows:
T MP Implementation v Test Management Process
u (t
1
...t
3
)
(T MP
1
< T MP
3
)...(T MP
2
< T MP
3
)
· (T MP
1
@t
1
u ... u T MP
3
@t
3
).
(6)
The concept of ‘Dynamic Test Process (DTP)’
could be represented in DL, as follows:
Dynamic Test Process v So f tware Testing Process
u ∃hasProcess
={DT P
1
=Test Design and Im plementation}
u ∃hasProcess
={DT P
2
=Test Environment Setu p and Maintenance}
u ∃hasProcess
={DT P
3
=Test Execution}
u ∃hasProcess
={DT P
4
=Test Incident Reporting}
.
(7)
The DTP processes could be also formalised in
temporal DL, as follows:
KEOD 2020 - 12th International Conference on Knowledge Engineering and Ontology Development
294

DT P Implementation v Dynamic Test Process
u (t
1
...t
4
)
(DT P
1
mDT P
2
)(DT P
1
mDT P
3
)(DT P
2
mDT P
3
)
(DT P
3
< DT P
4
)
· (DT P
1
@t
1
u ... u DT P
4
@t
4
).
(8)
It is worth noting that the temporal-interval rela-
tions be f ore and meet in Eq. 6 and Eq. 8 are as de-
fined in Eqs. 3-4, respectively.
On the other hand, ethical AI properties such as
hasWellBeingTesting could be formalized in DL, as
follows:
hasWellBeingTesting v Ethical Testing Property
u ∃hasWellBeing
={W BI1
i
}
u ∃hasWellBeing
={W BI1
j
}
u ∃hasWellBeingValue
={W BI1
j
}≥{W BI1
i
}
.
(9)
with W BI1
i
, a well-being indicator before using
the AI-based software and W BI1
j
, this well-being
indicator after using the AI-based software.
The ontology is implemented in the Web Ontol-
ogy Language (OWL) language, which is the lan-
guage of all the software testing ontologies (Fer-
reira de Souza et al., 2013a), and uses Protege v4.0.2
Integrated Development Environment (IDE) with the
inbuilt FaCT++ v1.3.0 reasoner (Tsarkov, 2014) to
check the internal consistency and to perform auto-
mated reasoning on the terms and axioms. An excerpt
of the encoded concepts is presented in Fig. 1, while
some properties are shown in Fig. 2.
3 VALIDATION AND
DISCUSSION
The developed AI-T ontology has been evaluated both
quantitatively and qualitatively in a series of exper-
iments as described in Sections 3.1, while its docu-
mentation is mentioned in 3.2.
3.1 Ontology Evaluation
The ontology evaluation ensures that the developed
ontology meets all the requirements (Dobson et al.,
2005).
For this purpose, experiments have been carried
out using Protege v4.0.2 IDE and applying FaCT++
v1.3.0 reasoner. In particular, an example including
the mathematical model and OWL implementation
has been provided for testers to get automated guid-
ance during the management process through running
Figure 3: Some values of the AI-T ontology metrics.
AI-T ontology (Fig. 1). On the other hand, an exam-
ple of testers being supported in testing AI-based soft-
wares, e.g. for the well-being assessment, has been
formalized in Section 2.2 and illustrated in Fig. 2,
leading to patterns for reusing test cases and facili-
tating the software testing learning and interoperable
knowledge sharing. In qll these experiments, AI-T
ontology provided 100% correct answers, and no in-
consistency has been observed.
The evaluation of AI-T used metrics such as pre-
sented in (Tartir et al., 2018) and (Hlomani and
Stacey, 2014). The computed values by Protege are
presented in Fig. 3. We can observe that the DL ex-
pressivity is S R OI Q (D). It is worth noting the rea-
soner works under the open-world assumption, i.e. if
for a question, there is no information in the ontology,
then the answer of the system is ‘does not know’, and
not ’does not exist’. To obtain the latter one, infor-
mation should be explicitly provided, but adding all
these closure-type assertions can slow down the rea-
soner. So, in practice, a trade-off should be achieved
between computational efficiency and completeness.
Actually, AI-T performs in real time and contains a
total of 707 terms and 705 axioms. This is much
higher compared to other state-of-art software testing
ontologies that own in average 61 ± 44 terms (Tebes
et al., 2019). Moreover, AI-T cohesion could be as-
sessed using the number of root classes which is equal
to 1, the number of leaf classes which is equal to
521, and the average depth which is equal to 4. All
these measures indicate AI-T shows promising per-
formance for real-world deployment.
3.2 Ontology Documentation
The AI-T ontology has been documented in Sec-
tion 2. It is a middle-out, domain ontology
which has been developed from scratch using
non-ontological resources such as primary sources,
e.g. IEEE standards, as recapped in Table 1.
AI-T is not dependent of any particular soft-
ware/system/agent/service/application/project, but it
is rather focused on the software testing knowledge
as well as the AI system’s ethical principles.
AI-T: Software Testing Ontology for AI-based Systems
295

It is worth noting the AI-T has not reused any
existing software testing ontology, and it is the first
ontology in its kind for the AI-softwares’ testing do-
main. Moreover, the AI-T ontology could be used
in conjunction with other ontologies such as the core
ontology for autonomous systems (CORA) (IEEE,
2015a) or other robotics and automation ontologies
(Fiorini et al., 2017) for further integration.
4 CONCLUSIONS
With the increasing usage of artificial intelligence
(AI) and especially machine learning (ML) within
softwares as well as the growing number of au-
tonomous intelligent agents actioning outside sys-
tems, the effective testing of AI-based softwares is
crucial and should have both a wide test coverage and
a rigorous formalization. Hence, we propose an on-
tological approach for testing AI-based softwares. In
particular, we proposed a new domain ontology which
is called AI-T and which encompasses software test-
ing knowledge and ethical AI principles. AI-T pur-
pose is to guide AI-based softwares’ testing, to au-
tomate their testing, and also facilitate the learning
about software testing. AI-T ontology has both con-
ceptual and implementation models that are helpful
for the interoperable sharing of the AI-based software
testing terms and relations, and that have been suc-
cessfully applied for whatever manual or automated
testing.
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