=Paper=
{{Paper
|id=Vol-3804/paper6o
|storemode=property
|title=An ontology-based meta-modelling approach for software test cases
|pdfUrl=https://ceur-ws.org/Vol-3804/paper6o.pdf
|volume=Vol-3804
|authors=Nehemiah Mung'Au,Emanuele Laurenzi
|dblpUrl=https://dblp.org/rec/conf/bir/MungAuL24
}}
==An ontology-based meta-modelling approach for software test cases==
https://ceur-ws.org/Vol-3804/paper6o.pdf
An ontology-based meta-modelling approach for software test
cases
Nehemiah Mung’au† and Emanuele Laurenzi∗,†
FHNW University of Applied Sciences and Arts Northwestern Switzerland, Riggenbachstrasse 16, 4600 Olten,
Switzerland1
Abstract
Software testing plays a crucial role in the software development lifecycle, ensuring the reliability and
quality of software programs. Despite the advancements in the field, software test cases still suffer of
poor specifications, leading to communication issues, inefficiencies, and increased costs. This study
investigates the suitability of an ontology-based meta-modelling approach, aiming to support the design
of adequate software test cases. The approach promotes the human and machine-interpretability of
domain-specific models representing the software test cases. This has the advantage of using automated
reasoning services to support the creation of adequate test cases. A new domain-specific modelling
language, ontoST, has been developed and implemented in the tool AOAME4STC for the proof of concept.
Keywords
software test cases, ontology-based meta-modelling, ontology-based DSML, ontoST, AOAME4STC
1. Introduction
In the domain of software engineering and quality assurance, test cases are vital for ensuring
software dependability and efficiency, aligning with reliability standards and customer needs [1,
2]. Despite their importance, software test cases face challenges such as standardization,
interoperability, and adaptability across various testing environments [3]. Complex test cases are
difficult to manage and modify, risking obsolescence and loss due to decentralized storage [4].
Most importantly, test cases still suffer from poor specifications, which lead to communication
issues, inefficiencies, and increased costs [5, 6]. Thus, software test cases need to be adequately
specified. According to [7], an adequate test case has the following benefits: it effectively reveals
defects with minimal effort, delivers accurate results, improves system performance at a lower
cost, and has a strong likelihood of uncovering unknown defects.
In this work, we propose an ontology-based meta-modelling approach to support the design
of adequate software test cases. This includes a new ontology-based domain-specific modelling
language (DSML), called ontoST. To ensure rigor and extensibility, ontoST has been engineered
by supplementing the Design Science Research (DSR) strategy [8] with the Agile Modelling
Method Engineering (AMME) methodology [58].
The paper is structured as follows. Section 2 describes the background and related work,
ending with the problem statement. Section 3 introduces the artifact requirements. Section 4
discusses the proposed ontology-based DSML ontoST. Section Error! Reference source not
found. shows a running example of how ontoST has been implemented in the modeling tool
AOAME4STC and subsequently used for the proof of concept. The paper concludes with section
6.
BIR-WS 2024: BIR 2024 Workshops and Doctoral Consortium, 23rd International Conference on Perspectives in Business
Informatics Research (BIR 2024), September 11-13, 2024, Prague, Czech Rep.
∗ Corresponding author.
† These authors contributed equally.
nehemiamkubamungau@gmail.com (N. Mung’au); emanuele.laurenzi@fhnw.ch (E. Laurenzi)
0000-0001-9142-7488 (E. Laurenzi)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
2. Background and related work
Software test cases are work products of test analysis and design phase of software testing as
illustrated in Error! Reference source not found. by [9]. “Test Case” (TC) has been recognized
as a building block for describing testing items, widely used as a work unit, metric and
documentation entity” [3].
Figure 1: Phases of the fundamental test process and activities of the test planning and test
analysis & design phases [9].
Software testing faces some challenges due to the complexities of software, financial and time
constraints, and the need for high quality standards [10]. The increasing complexity of modern
applications and competitive pressures further raise quality assurance standards [11]. Agile
development introduces frequent requirement changes, complicating test case management [11,
12]. Agile methodology's reliance on people over processes presents specific challenges during
the software development lifecycle (SDLC) [14]. One major issue is the lack of traceability of test
cases to other artifacts and source code [14, 15, 16]. This is exacerbated by inconsistent,
incomplete, and inaccurate requirements [18]. Agile methods also lead to inconsistent and
inadequate test cases [15], making it difficult for test cases to effectively validate software
behaviours [19] and avoid errors [9]. Semantic clarity and consistency in test cases are often
lacking, causing misunderstandings due to varying participant knowledge and experiences [9],
[20]. Additionally, insufficient tool integration for software test cases hampers seamless testing
processes [21]. These challenges highlight the need for improved test case design to ensure high-
quality software.
According to [22] Model-Based Testing (MBT) strives to automatically create tests (test cases)
based on a model that describes specific behaviours of the system being tested (SUT). MBT offers
several key motivations, including facilitating automated test case generation, providing
comprehensive test coverage, and simplifying defect discovery. According to [23] MBT is
becoming more and more recognized in the market as a cost-cutting strategy that automates test
case generation, reducing the need for manual test suite creation and improving test cycle
efficiency. Additionally, [23] also emphasize MBT's potential to lower costs and enhance test
effectiveness. Compared to manual case generation, [25] assert that a complete abstraction test
model allows for more comprehensive testing. According to [26], MBT can test a wider range of
scenarios compared to record-based testing. Additionally, [25] demonstrate that MBT
automation outperforms manual methods in error detection, citing a case study where MBT
found significantly more faults than manual techniques.
Despite these benefits, MBT has some drawbacks too, such as the requirement for specialized
skills, initial labour intensity, and model complexity. According to [22] testers need to be familiar
with state machines, formal languages, and automata theory, as well as tools and scripts for test
automation. Furthermore, [27] highlights the significant initial investment and labour needed for
MBT, as careful selection of model types and division of software portions are necessary for
effective modelling. The complexity of MBT models is underscored by the state-space explosion,
which complicates maintenance and presents significant challenges, particularly for beginners
[24, 26].
Ontologies [27], given their both conceptualization and automation power, can overcome the
issues posed by MBT approaches. For example, automated software test case generation is
significantly enhanced by combining ontology-based requirement specification with learning-
based methods, as proposed by [29]. An Ontology-based framework was proposed by [29] that
automates test case generation, execution, and verdict construction using a knowledge-based
system and learning-based testing algorithm. Similar approaches are suggested by [30, 31]. In
test scenario management, the authors in [32, 33] discuss the use of ontologies to generate
relevant test scenarios for complex systems, emphasizing the need for detailed and specific entity
descriptions. An ontology-based approach for test case prioritization was suggested by [34]
which deemed particularly useful during retesting after software updates. The importance of
domain knowledge and knowledge representation in efficient software testing is emphasized in
[35, 36], which proposed that ontologies can solve problems such as uneven knowledge
representation and focused expertise by creating semantic links between data and knowledge.
These insights collectively advance the field of software testing through improved automation,
management, and knowledge sharing. However, pure ontology-based approaches in software test
cases face some limitations too. According to [37] the development of ontology-based software
test case generation tools is often manual and costly due to the lack of supporting tools.
Furthermore, [38] emphasize the need for user-friendly ontology representations that fit the
workflow of domain experts, who may not be skilled in ontology development or formal
languages. Additionally, [38] highlight that ontologies require input from domain knowledge
experts, who may not be familiar with the formal languages or logic needed for ontology
development, making the process dependent on both domain and ontology experts.
Using domain-specific modelling language (DSMLs) in software test cases holds the promise
to address such non-usability and non-understandability issues raised by ontology-based
approaches. According to [38], DSML is a modelling language built for a specific area of discourse,
enriching general modelling notions with domain-specific terminology and concepts
reconstructed from that domain. Improved communication is one key advantage; DSMLs are
expressive and concise, effectively representing concepts and relationships within a specific
domain [39, 40]. Empirical studies by [41] compare DSMLs and general-purpose languages
(GPMLs) (e.g. UML Class Diagram) based on cognitive dimensions outlined by [42] show that
DSMLs perform better in areas such as abstraction gradient, consistency, and error-proneness.
This would make test cases created with DSMLs easier for stakeholders to understand and
validate, ensuring alignment with domain requirements. Additionally, DSMLs promote increased
productivity and consistency from the early stages of development, enhancing the quality of
models produced [43]. Additionally, [44] also noted that DSMLs are quickly learnable by domain
experts, improving the language's applicability.
To take the full advantage of DSMLs and ontologies, [45, 46], describe an ontology-based meta-
modelling approach as being interpretable by both humans and machines. They elaborate by
explaining that within the realm of information systems, human interpretability pertains to meta-
models, while machine interpretability primarily concerns the formal semantic aspects of models.
To mitigate the requirement for specialized skills in both MBT and ontology-based
approaches, the ontology-based meta-modelling approach was extended in this work. Graphics
depictions of models are useful for humans, while ontologies make knowledge in models’
machine interpretable. The ontology-based metamodeling technique was extended and
implemented by [46] with the introduction of AOAME, an Agile and Ontology-Aided Modelling
Environment. In this work we extended AOAME to accommodate the new ontology-based DSML
for software test cases.
3. Artifacts requirements
To tackle step 1 of the development process for a DSML, as outlined by [47,58], we collected
requirements through semi-structured interviews with at least five experts in software testing—
such as test engineers, test managers, and developers with testing experience. We also reviewed
literature on best practices and existing industry standards such as test cases. An excerpt of the
requirements, including their descriptions and sources, is presented in Table 1.
Table 1
List of Requirements for the software test case DSML.
Number
Requirement Description Source of elicitation
#
6 Test Case Specifications 2 - This ensures Interview/Questionnaires with
that we cover the general DSML software testing professionals
requirement of the concepts of a modeling Literature [48].
language should correspond to concepts
prospective users are familiar with as state
by [57]
7 Integration with software requirements – Interviews/questionnaires with
This serves as the most important quality software testing industry experts
factors for a software test case. Literature review of best
practices for software test case
[49, 50, 51].
8 The DSML should support the software Interviews/questionnaires with
testing techniques by default or be software testing industry experts
extensible Literature review of best
practices for software test cases
[52].
9 The DSML should support best Interviews/questionnaires with
communication and collaboration software testing industry
techniques for software test cases. For experts.
example, Behavior-driven development Literature review of best
Gherkin syntax practices for software test cases
case [49, 51, 53].
10 The DSML should support reusability of Interviews/questionnaires with
test cases software testing industry
experts.
Literature review of [54, 55].
11 The DSML should support organization and Interviews/questionnaires with
prioritization of software test cases software testing industry
experts.
Literature review [49, 56].
12 The DSML should support other testing This was derived from the
tools by providing an easy way to export interviews.
test cases
13 The DSML should ensure software test This was derived from the
cases designed with it are consistent interviews.
2 https://doi.org/10.1109/IEEESTD.1983.81615
4. ontoST: The proposed ontology-based DSML
This section aims to show "How can ontology-based DSML of software test cases be
conceptualized?" and fulfil the suggestion phase of the Design Science Research methodology.
The approach follows [47] DSML development process steps by creating a DSML through three
steps: creating concrete syntax, creating abstract syntax, and defining language semantics. The
abstract syntax, represented by a metamodel, depicts the language concepts and their
relationships, while the concrete syntax explains how these concepts are visually and textually
represented as domain-specific modelling elements. Language semantics impose structural and
features to govern syntax and semantics. Error! Reference source not found. illustrates
suggested abstract syntax. The meta model builds on [48] specifications for software test cases
in addition to requirements gathered from additional literature and consultations with industry
experts as discussed in 3. Test case, Test suite, Test expectation, Test results, and Test inputs are
inferred from [48] while Message flow, Sequence flow, Gateways are additional elements added
to facilitate the modelling of software test cases. This abstract syntax can be extended to
customize the DSML. Table 2 represents the concrete syntax of the DSML and it visually
represents the default abstract syntax conceptual elements.
Figure 2: Suggested ontoST abstract syntax.
Table 2
Suggested concrete syntax of ontoST.
Element Graphical notation Description
Test Suite Represents a set of test cases that will be used
to test a particular functionality.
Test case Represents instructions for testers to follow to
ensure programs are functioning properly
Exclusive Represents a decision point where only one
Gateway outgoing path can be taken based on
(XOR) conditions. It’s a mutually exclusive choice.
Parallel Splits the flow into multiple parallel paths or
Gateway synchronizes multiple incoming paths. All
outgoing paths are taken simultaneously.
Inclusive Splits the flow into one or more paths based on
Gateway conditions, and all active paths must be
completed before merging.
Test Step Represents test case step. This is a step that
should be executed and observed for results.
Input Represents input data to a test step.
Result Represents the actual result received after the
test.
Expectation Represents the expectation of a test step after
the test.
Assertion Represents test step asserts.
Sequence flow Used for connecting Test steps, Gateways and
Expectation.
Message flow Used for showing input or output message flow
from input and result elements.
Start Used to show the beginning of software test
cases.
Error! Reference source not found. shows the three main ontologies of AOAME that were
extended to design our software test case DSML. Specifically, the Meta-Model Ontology (MMO)
that mirrors the abstract syntax, the Domain Ontology (DO) captures the semantic domain;
concepts originating from the MMO are aligned with those from the DO. The Palette Ontology
(PO) represents the graphical notations of the language and is directly associated with concepts
within the abstract syntax. The PO contains concepts and relations regarding the modelling
language's graphical notations, as well as information of how to position the graphical notations
on the palette. Thus, the ME palette is supplied by the PO concepts. The MMO contains classes and
characteristics that describe a modelling language's abstract syntactic elements, such as
modelling elements and modelling relations, as well as the taxonomy and object properties
associated with them. MMO consists of one or more modelling languages, either distinct or
merged. The DO contains classes and properties that describe the semantic domain.
Figure 3: The Ontology-based Meta-modelling Architecture for ontoST. Adapted from [57].
5. Proof of concept
In this section, we evaluate the utility of the approach in two ways: first, we prove that the models
can be created with the new ontology-based DSML ontoST, then we show how the ontology can
be used to support the design of adequate software test cases. In both cases, a real-world scenario
is considered.
5.1. Evaluation of the new ontology-based DSML ontoST
In this section, we will demonstrate the use of ontoST modelling language within the AOAME4STC
tool to model test cases created for evaluating the login functionality of a bank website. Error!
Reference source not found. shows the current record-based software test case representation
used by some test case designers. Error! Reference source not found. shows the model created
using ontoST representing the test cases shown in Error! Reference source not found..
Figure 4: Sample Software test case for testing login functionality [56].
Figure 5: Representation of the sample Test case in Figure 4 using ontoST in AOAME4STC.
5.2. Validation of the modeled test cases for conformance with test case specifications
In this section we evaluated the test case model created against one of [56] test case specifications
constraint that states that every test case must have at least one test step. In Error! Reference
source not found. we have 2 test cases in our model where one does not meet the requirement
of having a test step. We have manually labelled it as wrong. As seen in Error! Reference source
not found., when we run the SPARQL query against the ontology created for the model, we
receive an incorrect testcase triple.
Figure 6: Sample test cases modelled with the ontology-based DSML
Figure 7: Result of evaluating test cases missing test steps
6. Conclusion
This paper demonstrated the suitability of an ontology-based meta-modelling approach for the
support of the design of adequate software test cases. For this, a new ontology-based domain-
specific modelling language, ontoST, was created. The latter was implemented in the modelling
environment AOAME4STC, which was used for the proof of concept.
As a future work, we regard important to evaluate the perceived usefulness of the new DSML
ontoST with software testers and to continue validating ontoST by modeling additional software
test cases.
References
[1] A. S. Verma, A. Choudhary, and S. Tiwari, ‘Software Test Case Generation Tools and
Techniques: A Review’, Int. J. Math. Eng. Manag. Sci., vol. 8, no. 2, pp. 293–315, Apr. 2023,
doi: 10.33889/IJMEMS.2023.8.2.018.
[2] P. Kamde, V. Nandavadekar, and R. Pawar, ‘Value of Test Cases in Software Testing’, in 2006
IEEE International Conference on Management of Innovation and Technology, Singapore,
China: IEEE, Jun. 2006, pp. 668–672. doi: 10.1109/ICMIT.2006.262303.
[3] D. Almog and T. Heart, ‘What Is a Test Case? Revisiting the Software Test Case Concept’, in
Software Process Improvement, vol. 42, R. V. O’Connor, N. Baddoo, J. Cuadrago Gallego, R.
Rejas Muslera, K. Smolander, and R. Messnarz, Eds., in Communications in Computer and
Information Science, vol. 42. , Berlin, Heidelberg: Springer Berlin Heidelberg, 2009, pp. 13–
31. doi: 10.1007/978-3-642-04133-4_2.
[4] T. Parveen, S. Tilley, and G. Gonzalez, ‘A case study in test management’, in Proceedings of
the 45th annual southeast regional conference, Winston-Salem North Carolina: ACM, Mar.
2007, pp. 82–87. doi: 10.1145/1233341.1233357.
[5] N. Shete and A. Jadhav, ‘An empirical study of test cases in software testing’, in International
Conference on Information Communication and Embedded Systems (ICICES2014), Chennai,
India: IEEE, Feb. 2014, pp. 1–5. doi: 10.1109/ICICES.2014.7033883.
[6] K. Juhnke, M. Tichy, and F. Houdek, ‘Challenges concerning test case specifications in
automotive software testing: assessment of frequency and criticality’, Softw. Qual. J., vol. 29,
no. 1, pp. 39–100, Mar. 2021, doi: 10.1007/s11219-020-09523-0.
[7] S. O. Barraood, H. Mohd, and F. Baharom, ‘An initial investigation of the effect of quality
factors on Agile test case quality through experts’ review’, Cogent Eng., vol. 9, no. 1, p.
2082121, Dec. 2022, doi: 10.1080/23311916.2022.2082121.
[8] V. Vaishnavi and B. Kuechler, ‘Design Science Research in Information Systems’, Assoc. Inf.
Syst., Jan. 2004.
[9] K. Juhnke, M. Tichy, and F. Houdek, ‘Challenges Concerning Test Case Specifications in
Automotive Software Testing’, in 2018 44th Euromicro Conference on Software Engineering
and Advanced Applications (SEAA), Prague: IEEE, Aug. 2018, pp. 33–40. doi:
10.1109/SEAA.2018.00015.
[10] P. Mudholkar, M. Mudholkar, and S. Kulkarni, ‘Software testing’, in Proceedings of the
International Conference and Workshop on Emerging Trends in Technology, Mumbai
Maharashtra India: ACM, Feb. 2010, pp. 1024–1024. doi: 10.1145/1741906.1742242.
[11] M. A. Jamil, M. Arif, N. S. A. Abubakar, and A. Ahmad, ‘Software Testing Techniques: A
Literature Review’, in 2016 6th International Conference on Information and Communication
Technology for The Muslim World (ICT4M), Jakarta, Indonesia: IEEE, Nov. 2016, pp. 177–182.
doi: 10.1109/ICT4M.2016.045.
[12] P. Nidagundi and L. Novickis, ‘Introducing Lean Canvas Model Adaptation in the Scrum
Software Testing’, Procedia Comput. Sci., vol. 104, pp. 97–103, 2017, doi:
10.1016/j.procs.2017.01.078.
[13] K. Beck, Test Driven Development By Example. Hoboken: Pearson Education, Limited, 2002.
[14] T. Dyba, ‘Improvisation in Small Software Organizations’, IEEE Softw., vol. 17, no. 5, pp. 82–
87, Sep. 2000, doi: 10.1109/52.877872.
[15] J. Fischbach, H. Femmer, D. Mendez, D. Fucci, and A. Vogelsang, ‘What Makes Agile Test
Artifacts Useful? An Activity-Based Quality Model from a Practitioners’ Perspective’, 2020,
doi: 10.48550/ARXIV.2009.01722.
[16] B. V. Rompaey and S. Demeyer, ‘Establishing Traceability Links between Unit Test Cases and
Units under Test’, in 2009 13th European Conference on Software Maintenance and
Reengineering, Kaiserslautern, Germany: IEEE, 2009, pp. 209–218. doi:
10.1109/CSMR.2009.39.
[17] K. Kārkliņa and R. Pirta, ‘Quality metrics in Agile Software Development Projects’, Inf.
Technol. Manag. Sci., vol. 21, pp. 54–59, Dec. 2018, doi: 10.7250/itms-2018-0008.
[18] S.-T. Lai, ‘A Maintainability Enhancement Procedure for Reducing Agile Software
Development Risk’, Int. J. Softw. Eng. Appl., vol. 6, no. 4, pp. 29–40, Jul. 2015, doi:
10.5121/ijsea.2015.6403.
[19] R. Romli, S. Sarker, M. Omar, and M. Mahmod, ‘Automated Test Cases and Test Data
Generation for Dynamic Structural Testing in Automatic Programming Assessment Using
MC/DC’, Int. J. Adv. Sci. Eng. Inf. Technol., vol. 10, no. 1, pp. 120–127, Feb. 2020, doi:
10.18517/ijaseit.10.1.10166.
[20] R. Lachmann and I. Schaefer, Towards Efficient and Effective Testing in Automotive Software
Development. in GI-Jahrestagung. 2014. [Online]. Available:
https://api.semanticscholar.org/CorpusID:4870726
[21] M. Broy, ‘Challenges in automotive software engineering’, in Proceedings of the 28th
international conference on Software engineering, Shanghai China: ACM, May 2006, pp. 33–
42. doi: 10.1145/1134285.1134292.
[22] A. Meriem and M. Abdelaziz, ‘A Meta-Model for Model-Based Testing Technique: A Review’,
J. Softw. Eng., vol. 12, no. 1, pp. 1–11, Dec. 2017, doi: 10.3923/jse.2018.1.11.
[23] S. Schulz, J. Honkola, and A. Huima, ‘Towards Model-Based Testing with Architecture
Models’, in 14th Annual IEEE International Conference and Workshops on the Engineering of
Computer-Based Systems (ECBS’07), Tucson, AZ, USA: IEEE, Mar. 2007, pp. 495–502. doi:
10.1109/ECBS.2007.73.
[24] S. R. Dalal, A. Jain, Karunanithi, N., Leaton, J. M., Lott, C. M., Patton, G. C., & Horowitz, B. M,
‘Model-based testing in practice’, in Proceedings of the 21st international conference on
Software engineering, Los Angeles California USA: ACM, May 1999, pp. 285–294. doi:
10.1145/302405.302640.
[25] S. Dhawan, N. Kumar, and S. Saini, ‘MODEL BASED TESTING CONSIDERING STEPS, LEVELS,
TOOLS & STANDARDS OF SOFTWARE QUALITY’, vol. 2, no. 4, 2011.
[26] ‘Model based testing vs. Record based testing’, Tricentis. Accessed: Dec. 09, 2023. [Online].
Available: https://www.tricentis.com/blog/model-based-vs-record-based-testing/
[27] L. Anton, ‘How to Improve Your Workflow Using Model-Based Testing’, freeCodeCamp.org.
Accessed: Dec. 10, 2023. [Online]. Available:
https://www.freecodecamp.org/news/improve-your-workflow-using-model-based-
testing/
[28] T. R. Gruber, ‘Toward principles for the design of ontologies used for knowledge sharing?’,
Int. J. Hum.-Comput. Stud., vol. 43, no. 5–6, pp. 907–928, Nov. 1995, doi:
10.1006/ijhc.1995.1081.
[29] S. Ul Haq and U. Qamar, ‘Ontology Based Test Case Generation for Black Box Testing’, in
Proceedings of the 2019 8th International Conference on Educational and Information
Technology, Cambridge United Kingdom: ACM, Mar. 2019, pp. 236–241. doi:
10.1145/3318396.3318442.
[30] H. N. Anjalika, M. T. Yasantha, and P. I. Siriwardhana, ‘An Ontology Based Test Case
Generation Framework’, 2017.
[31] H.-J. Happel and S. Seedorf, ‘Applications of Ontologies in Software Engineering’, 2006.
Accessed: Dec. 11, 2023. [Online]. Available:
https://www.semanticscholar.org/paper/Applications-of-Ontologies-in-Software-
Engineering-Happel-Seedorf/11e2c3bfe1dd68446180f17e476addc947dad095
[32] T. Hamilton, ‘Test Case vs Test Scenario – Difference Between Them’. Accessed: Dec. 11,
2023. [Online]. Available: https://www.guru99.com/test-case-vs-test-scenario.html
[33] M. Zipfl, N. Koch, and J. M. Zöllner, ‘A Comprehensive Review on Ontologies for Scenario-
based Testing in the Context of Autonomous Driving’, Apr. 21, 2023, arXiv:
arXiv:2304.10837. Accessed: Dec. 11, 2023. [Online]. Available:
http://arxiv.org/abs/2304.10837
[34] M. Hasnain, I. Ghani, M. F. Pasha, and S.-R. Jeong, ‘Ontology-Based Regression Testing: A
Systematic Literature Review’, Appl. Sci., vol. 11, no. 20, p. 9709, Oct. 2021, doi:
10.3390/app11209709.
[35] S. Vasanthapriyan, J. Tian, and J. Xiang, ‘An Ontology-Based Knowledge Framework for
Software Testing’, in Knowledge and Systems Sciences, vol. 780, J. Chen, T. Theeramunkong,
T. Supnithi, and X. Tang, Eds., in Communications in Computer and Information Science, vol.
780. , Singapore: Springer Singapore, 2017, pp. 212–226. doi: 10.1007/978-981-10-6989-
5_18.
[36] Z. Sun, C. Hu, C. Li, and L. Wu, ‘Domain Ontology Construction and Evaluation for the Entire
Process of Software Testing’, IEEE Access, vol. 8, pp. 205374–205385, 2020, doi:
10.1109/ACCESS.2020.3037188.
[37] V. Tarasov , H. Tan, A. Adlemo, A. Andersson, M. Ismail, M. Johansson, & D. Olsson , ‘Ontology-
based Software Test Case Generation (OSTAG)’:, in European Projects in Knowledge
Applications and Intelligent Systems, Lisbon, Portugal: SCITEPRESS - Science and Technology
Publications, 2015, pp. 135–159. doi: 10.5220/0007901301350159.
[38] A. Westerinen and R. Tauber, ‘Ontology development by domain experts (without using the
“O” word)’, Appl. Ontol., vol. 12, pp. 1–13, Aug. 2017, doi: 10.3233/AO-170183.
[39] A. Van Deursen, P. Klint, and J. Visser, ‘Domain-specific languages: an annotated
bibliography’, ACM SIGPLAN Not., vol. 35, no. 6, pp. 26–36, Jun. 2000, doi:
10.1145/352029.352035.
[40] U. Frank, ‘The MEMO meta modelling language (MML) and language architecture’, ICB Res.
Rep., Art. no. 24, 2008, Accessed: Apr. 14, 2024. [Online]. Available:
https://ideas.repec.org//p/zbw/udeicb/24.html
[41] T. Kosar N. Oliveira, M. Mernik, M. João, M. V. Pereira, M. Repinšek, D. C. da Cruz, & P. R.
Henrique , ‘Comparing general-purpose and domain-specific languages: An empirical study’,
Comput. Sci. Inf. Syst., vol. 7, no. 2, pp. 247–264, 2010, doi: 10.2298/CSIS1002247K.
[42] T. R. G. Green and M. Petre, ‘Usability Analysis of Visual Programming Environments: A
“Cognitive Dimensions” Framework’, J. Vis. Lang. Comput., vol. 7, no. 2, pp. 131–174, Jun.
1996, doi: 10.1006/jvlc.1996.0009.
[43] M. Mernik, J. Heering, and A. M. Sloane, ‘When and how to develop domain-specific
languages’, ACM Comput. Surv., vol. 37, no. 4, pp. 316–344, Dec. 2005, doi:
10.1145/1118890.1118892.
[44] P. Hudak, ‘Building domain-specific embedded languages’, ACM Comput. Surv., vol. 28, no.
4es, p. 196, Dec. 1996, doi: 10.1145/242224.242477.
[45] K. Hinkelmann, E. Laurenzi, A. Martin, and B. Thönssen, ‘Ontology-Based Metamodeling’, in
Business Information Systems and Technology 4.0, vol. 141, R. Dornberger, Ed., in Studies in
Systems, Decision and Control, vol. 141. , Cham: Springer International Publishing, 2018, pp.
177–194. doi: 10.1007/978-3-319-74322-6_12.
[46] E. Laurenzi, K. Hinkelmann, and A. Van Der Merwe, ‘An Agile and Ontology-Aided Modeling
Environment’, in The Practice of Enterprise Modeling, vol. 335, R. A. Buchmann, D.
Karagiannis, and M. Kirikova, Eds., in Lecture Notes in Business Information Processing, vol.
335. , Cham: Springer International Publishing, 2018, pp. 221–237. doi: 10.1007/978-3-
030-02302-7_14.
[47] H. Cho, J. Gray, and E. Syriani, ‘Creating visual Domain-Specific Modeling Languages from
end-user demonstration’, in 2012 4th International Workshop on Modeling in Software
Engineering (MISE), Zurich, Switzerland: IEEE, Jun. 2012, pp. 22–28. doi:
10.1109/MISE.2012.6226010.
[48] ‘How to Write Effective Test Cases (With Templates) - TestRail’. Accessed: Apr. 07, 2024.
[Online]. Available: https://www.testrail.com/blog/effective-test-cases-templates/
[49] A. Beer, M. Junker, H. Femmer, and M. Felderer, ‘Initial Investigations on the Influence of
Requirement Smells on Test-Case Design’, in 2017 IEEE 25th International Requirements
Engineering Conference Workshops (REW), Lisbon, Portugal: IEEE, Sep. 2017, pp. 323–326.
doi: 10.1109/REW.2017.43.
[50] R. D. Craig and S. P. Jaskiel, Systematic software testing. in Artech House computing library.
Boston: Artech House, 2002.
[51] S. Ganji, ‘Test Case Design - A Guide for QA Engineers With Examples’, ACCELQ Inc. Accessed:
Apr. 21, 2024. [Online]. Available: https://www.accelq.com/blog/test-case-design/
[52] H. K. V. Tran, N. B. Ali, J. Börstler, and M. Unterkalmsteiner, ‘Test-Case Quality –
Understanding Practitioners’ Perspectives’, in Product-Focused Software Process
Improvement, X. Franch, T. Männistö, and S. Martínez-Fernández, Eds., Cham: Springer
International Publishing, 2019, pp. 37–52. doi: 10.1007/978-3-030-35333-9_3.
[53] R. Li and S. L. Ma, The Use of Ontology in Case Based Reasoning for Reusable Test Case
Generation. 2015. doi: 10.2991/aiie-15.2015.102.
[54] Zhang Juan, L. Cai, W. Tong, Yuan Song, and Li Ying, ‘Test Case Reusability Metrics Model’, in
2010 2nd International Conference on Computer Technology and Development, Cairo, Egypt:
IEEE, Nov. 2010, pp. 294–298. doi: 10.1109/ICCTD.2010.5645869.
[55] W. Afzal, Metrics in Software Test Planning and Test Design Processes. 2007. Accessed: Apr.
28, 2024. [Online]. Available: https://urn.kb.se/resolve?urn=urn:nbn:se:bth-6193
[56] ‘IEEE Standard for Software Test Documentation’, IEEE Std 829-1983, pp. 1–48, Feb. 1983,
doi: 10.1109/IEEESTD.1983.81615.
[57] U. Frank, ‘Domain-Specific Modeling Languages: Requirements Analysis and Design
Guidelines’, in Domain Engineering, I. Reinhartz-Berger, A. Sturm, T. Clark, S. Cohen, and J.
Bettin, Eds., Berlin, Heidelberg: Springer Berlin Heidelberg, 2013, pp. 133–157. doi:
10.1007/978-3-642-36654-3_6.
[58] D. Karagiannis, ‘Agile modeling method engineering’, in Proceedings of the 19th Panhellenic
Conference on Informatics, Athens Greece: ACM, Oct. 2015, pp. 5–10. doi:
10.1145/2801948.2802040.