| 1. |
- Elahidoost, Parisa, 1989-, et al.
(författare)
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Designing NLP-Based Solutions for Requirements Variability Management : Experiences from a Design Science Study at Visma
- 2024
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Ingår i: Requirements Engineering. - : Springer Science+Business Media B.V.. - 9783031573262 ; , s. 191-204
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Konferensbidrag (refereegranskat)abstract
- Context and motivation: In this industry-academia collaborative project, a team of researchers, supported by a software architect, business analyst, and test engineer explored the challenges of requirement variability in a large business software development company. Question/ problem: Following the design science paradigm, we studied the problem of requirements analysis and tracing in the context of contractual documents, with a specific focus on managing requirements variability. This paper reports on the lessons learned from that experience, highlighting the strategies and insights gained in the realm of requirements variability management.Principal ideas/results: This experience report outlines the insights gained from applying design science in requirements engineering research in industry. We show and evaluate various strategies to tackle the issue of requirement variability. Contribution: We report on the iterations and how the solution development evolved in parallel with problem understanding. From this process, we derive five key lessons learned to highlight the effectiveness of design science in exploring solutions for requirement variability in contract-based environments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
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| 2. |
- Fischbach, Jannik, et al.
(författare)
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Automatic creation of acceptance tests by extracting conditionals from requirements : NLP approach and case study
- 2023
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Ingår i: Journal of Systems and Software. - : Elsevier. - 0164-1212 .- 1873-1228. ; 197
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Tidskriftsartikel (refereegranskat)abstract
- Acceptance testing is crucial to determine whether a system fulfills end-user requirements. However, the creation of acceptance tests is a laborious task entailing two major challenges: (1) practitioners need to determine the right set of test cases that fully covers a requirement, and (2) they need to create test cases manually due to insufficient tool support. Existing approaches for automatically deriving test cases require semi-formal or even formal notations of requirements, though unrestricted natural language is prevalent in practice. In this paper, we present our tool-supported approach CiRA (Conditionals in Requirements Artifacts) capable of creating the minimal set of required test cases from conditional statements in informal requirements. We demonstrate the feasibility of CiRA in a case study with three industry partners. In our study, out of 578 manually created test cases, 71.8% can be generated automatically. Additionally, CiRA discovered 80 relevant test cases that were missed in manual test case design. CiRA is publicly available at www.cira.bth.se/demo/. © 2022
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| 3. |
- Fischbach, Jannik, et al.
(författare)
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Automatic Detection of Causality in Requirement Artifacts : The CiRA Approach
- 2021
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Ingår i: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - Cham : Springer Science and Business Media Deutschland GmbH. - 9783030731274 ; , s. 19-36
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Konferensbidrag (refereegranskat)abstract
- [Context & motivation:] System behavior is often expressed by causal relations in requirements (e.g., If event 1, then event 2). Automatically extracting this embedded causal knowledge supports not only reasoning about requirements dependencies, but also various automated engineering tasks such as seamless derivation of test cases. However, causality extraction from natural language (NL) is still an open research challenge as existing approaches fail to extract causality with reasonable performance. [Question/problem:] We understand causality extraction from requirements as a two-step problem: First, we need to detect if requirements have causal properties or not. Second, we need to understand and extract their causal relations. At present, though, we lack knowledge about the form and complexity of causality in requirements, which is necessary to develop a suitable approach addressing these two problems. [Principal ideas/results:] We conduct an exploratory case study with 14,983 sentences from 53 requirements documents originating from 18 different domains and shed light on the form and complexity of causality in requirements. Based on our findings, we develop a tool-supported approach for causality detection (CiRA, standing for Causality in Requirement Artifacts). This constitutes a first step towards causality extraction from NL requirements. [Contribution:] We report on a case study and the resulting tool-supported approach for causality detection in requirements. Our case study corroborates, among other things, that causality is, in fact, a widely used linguistic pattern to describe system behavior, as about a third of the analyzed sentences are causal. We further demonstrate that our tool CiRA achieves a macro-F 1 score of 82% on real word data and that it outperforms related approaches with an average gain of 11.06% in macro-Recall and 11.43% in macro-Precision. Finally, we disclose our open data sets as well as our tool to foster the discourse on the automatic detection of causality in the RE community. © 2021, Springer Nature Switzerland AG.
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| 4. |
- Fischbach, Jannik, et al.
(författare)
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Automatic ESG Assessment of Companies by Mining and Evaluating Media Coverage Data : NLP Approach and Tool
- 2023
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Ingår i: Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023. - : Institute of Electrical and Electronics Engineers (IEEE). - 9798350324457 ; , s. 2823-2830
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Konferensbidrag (refereegranskat)abstract
- [Context:] Society increasingly values sustainable corporate behaviour, impacting corporate reputation and customer trust. Hence, companies regularly publish sustainability reports to shed light on their impact on environmental, social, and governance (ESG) factors. [Problem:] Sustainability reports are written by companies and therefore considered a company-controlled source. Contrarily, studies reveal that non-corporate channels (e.g., media coverage) represent the main driver for ESG transparency. However, analysing media coverage regarding ESG factors is challenging since (1) the amount of published news articles grows daily, (2) media coverage data does not necessarily deal with an ESG-relevant topic, meaning that it must be carefully filtered, and (3) the majority of media coverage data is unstructured. [Research Goal:] We aim to automatically extract ESG-relevant information from textual media reactions to calculate an ESG score for a given company. Our goal is to reduce the cost of ESG data collection and make ESG information available to the general public. [Contribution:] Our contributions are three-fold: First, we publish a corpus of 432,411 news headlines annotated as being environmental-, governance-, social-related, or ESG-irrelevant. Second, we present our tool-supported approach called ESG-Miner, capable of automatically analysing and evaluating corporate ESG performance headlines. Third, we demonstrate the feasibility of our approach in an experiment and apply the ESG-Miner on 3000 manually labelled headlines. Our approach correctly processes 96.7% of the headlines and shows great performance in detecting environmental-related headlines and their correct sentiment. © 2023 IEEE.
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| 5. |
- Fischbach, Jannik, et al.
(författare)
-
CiRA : A Tool for the Automatic Detection of Causal Relationships in Requirements Artifacts
- 2021
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Ingår i: CEUR Workshop Proceedings. - : CEUR-WS.
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Konferensbidrag (refereegranskat)abstract
- Requirements often specify the expected system behavior by using causal relations (e.g., If A, then B). Automatically extracting these relations supports, among others, two prominent RE use cases: Automatic test case derivation and dependency detection between requirements. However, existing tools fail to extract causality from natural language with reasonable performance. In this paper, we present our tool CiRA (Causality detection in Requirements Artifacts), which represents a first step towards automatic causality extraction from requirements. We evaluate CiRA on a publicly available data set of 61 acceptance criteria (causal: 32; non-causal: 29) describing the functionality of the German Corona-Warn-App. We achieve a macro1 score of 83 %, which corroborates the feasibility of our approach. © 2021 CEUR-WS. All rights reserved.
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| 6. |
- Fischbach, Jannik, et al.
(författare)
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Fine-Grained Causality Extraction from Natural Language Requirements Using Recursive Neural Tensor Networks
- 2021
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Ingår i: Proceedings of the IEEE International Conference on Requirements Engineering. - : IEEE Computer Society. - 9781665418980 ; , s. 60-69
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Konferensbidrag (refereegranskat)abstract
- [Context:] Causal relations (e.g., If A, then B) are prevalent in functional requirements. For various applications of AI4RE, e.g., the automatic derivation of suitable test cases from requirements, automatically extracting such causal statements are a basic necessity. [Problem:] We lack an approach that is able to extract causal relations from natural language requirements in fine-grained form. Specifically, existing approaches do not consider the combinatorics between causes and effects. They also do not allow to split causes and effects into more granular text fragments (e.g., variable and condition), making the extracted relations unsuitable for automatic test case derivation. [Objective Contributions:] We address this research gap and make the following contributions: First, we present the Causality Treebank, which is the first corpus of fully labeled binary parse trees representing the composition of 1,571 causal requirements. Second, we propose a fine-grained causality extractor based on Recursive Neural Tensor Networks. Our approach is capable of recovering the composition of causal statements written in natural language and achieves a F1 score of 74% in the evaluation on the Causality Treebank. Third, we disclose our open data sets as well as our code to foster the discourse on the automatic extraction of causality in the RE community. © 2021 IEEE.
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| 7. |
- Fischbach, Jannik, et al.
(författare)
-
How Do Practitioners Interpret Conditionals in Requirements?
- 2021
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Ingår i: Lecture Notes in Computer Science. - Cham : Springer Science and Business Media Deutschland GmbH. - 9783030914516 ; , s. 85-102
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Konferensbidrag (refereegranskat)abstract
- Context: Conditional statements like “If A and B then C” are core elements for describing software requirements. However, there are many ways to express such conditionals in natural language and also many ways how they can be interpreted. We hypothesize that conditional statements in requirements are a source of ambiguity, potentially affecting downstream activities such as test case generation negatively. Objective: Our goal is to understand how specific conditionals are interpreted by readers who work with requirements. Method: We conduct a descriptive survey with 104 RE practitioners and ask how they interpret 12 different conditional clauses. We map their interpretations to logical formulas written in Propositional (Temporal) Logic and discuss the implications. Results: The conditionals in our tested requirements were interpreted ambiguously. We found that practitioners disagree on whether an antecedent is only sufficient or also necessary for the consequent. Interestingly, the disagreement persists even when the system behavior is known to the practitioners. We also found that certain cue phrases are associated with specific interpretations. Conclusion: Conditionals in requirements are a source of ambiguity and there is not just one way to interpret them formally. This affects any analysis that builds upon formalized requirements (e.g., inconsistency checking, test-case generation). Our results may also influence guidelines for writing requirements. © 2021, Springer Nature Switzerland AG.
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| 8. |
- Fischbach, Jannik, et al.
(författare)
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What makes agile test artifacts useful? : An activity-based quality model from a practitioners' perspective
- 2020
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Ingår i: International Symposium on Empirical Software Engineering and Measurement. - New York, NY, USA : IEEE Computer Society. - 9781450375801
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Konferensbidrag (refereegranskat)abstract
- Background: The artifacts used in Agile software testing and the reasons why these artifacts are used are fairly well-understood. However, empirical research on how Agile test artifacts are eventually designed in practice and which quality factors make them useful for software testing remains sparse. Aims: Our objective is two-fold. First, we identify current challenges in using test artifacts to understand why certain quality factors are considered good or bad. Second, we build an Activity-Based Artifact Quality Model that describes what Agile test artifacts should look like. Method: We conduct an industrial survey with 18 practitioners from 12 companies operating in seven different domains. Results: Our analysis reveals nine challenges and 16 factors describing the quality of six test artifacts from the perspective of Agile testers. Interestingly, we observed mostly challenges regarding language and traceability, which are well-known to occur in non-Agile projects. Conclusions: Although Agile software testing is becoming the norm, we still have little confidence about general do's and don'ts going beyond conventional wisdom. This study is the first to distill a list of quality factors deemed important to what can be considered as useful test artifacts. © 2020 IEEE Computer Society. All rights reserved.
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| 9. |
- Frattini, Julian, 1995-, et al.
(författare)
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A Live Extensible Ontology of Quality Factors for Textual Requirements
- 2022
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Ingår i: Proceedings of the IEEE International Conference on Requirements Engineering. - : IEEE. - 9781665470001 ; , s. 274-280
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Konferensbidrag (refereegranskat)abstract
- Quality factors like passive voice or sentence length are commonly used in research and practice to evaluate the quality of natural language requirements since they indicate defects in requirements artifacts that potentially propagate to later stages in the development life cycle. However, as a research community, we still lack a holistic perspective on quality factors. This inhibits not only a comprehensive understanding of the existing body of knowledge but also the effective use and evolution of these factors. To this end, we propose an ontology of quality factors for textual requirements, which includes (1) a structure framing quality factors and related elements and (2) a central repository and web interface making these factors publicly accessible and usable. We contribute the first version of both by applying a rigorous ontology development method to 105 eligible primary studies and construct a first version of the repository and interface. We illustrate the usability of the ontology and invite fellow researchers to a joint community effort to complete and maintain this knowledge repository. We envision our ontology to reflect the community's harmonized perception of requirements quality factors, guide reporting of new quality factors, and provide central access to the current body of knowledge.
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| 10. |
- Frattini, Julian, 1995-, et al.
(författare)
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Causality in requirements artifacts : prevalence, detection, and impact
- 2023
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Ingår i: Requirements Engineering. - : Springer Science+Business Media B.V.. - 0947-3602 .- 1432-010X. ; 28:1, s. 49-74
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Tidskriftsartikel (refereegranskat)abstract
- Causal relations in natural language (NL) requirements convey strong, semantic information. Automatically extracting such causal information enables multiple use cases, such as test case generation, but it also requires to reliably detect causal relations in the first place. Currently, this is still a cumbersome task as causality in NL requirements is still barely understood and, thus, barely detectable. In our empirically informed research, we aim at better understanding the notion of causality and supporting the automatic extraction of causal relations in NL requirements. In a first case study, we investigate 14.983 sentences from 53 requirements documents to understand the extent and form in which causality occurs. Second, we present and evaluate a tool-supported approach, called CiRA, for causality detection. We conclude with a second case study where we demonstrate the applicability of our tool and investigate the impact of causality on NL requirements. The first case study shows that causality constitutes around 28 % of all NL requirements sentences. We then demonstrate that our detection tool achieves a macro-F 1 score of 82 % on real-world data and that it outperforms related approaches with an average gain of 11.06 % in macro-Recall and 11.43 % in macro-Precision. Finally, our second case study corroborates the positive correlations of causality with features of NL requirements. The results strengthen our confidence in the eligibility of causal relations for downstream reuse, while our tool and publicly available data constitute a first step in the ongoing endeavors of utilizing causality in RE and beyond. © 2022, The Author(s).
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| 11. |
- Frattini, Julian, 1995-, et al.
(författare)
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CiRA : An Open-Source Python Package for Automated Generation of Test Case Descriptions from Natural Language Requirements
- 2023
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Ingår i: Proceedings - 31st IEEE International Requirements Engineering Conference Workshops, REW 2023. - : Institute of Electrical and Electronics Engineers (IEEE). - 9798350326918 ; , s. 68-71
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Konferensbidrag (refereegranskat)abstract
- Deriving acceptance tests from high-level, natural language requirements that achieve full coverage is a major manual challenge at the interface between requirements engineering and testing. Conditional requirements (e.g., 'If A or B then C.') imply causal relationships which - when extracted - allow to generate these acceptance tests automatically. This paper presents a tool from the CiRA (Causality In Requirements Artifacts) initiative, which automatically processes conditional natural language requirements and generates a minimal set of test case descriptions achieving full coverage. We evaluate the tool on a publicly available data set of 61 requirements from the requirements specification of the German Corona-Warn-App. The tool infers the correct test variables in 84.5% and correct variable configurations in 92.3% of all cases, which corroborates the feasibility of our approach. © 2023 IEEE.
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| 12. |
- Frattini, Julian, 1995-, et al.
(författare)
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Let’s Stop Building at the Feet of Giants : Recovering unavailable Requirements Quality Artifacts
- 2023
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Ingår i: CEUR Workshop Proceedings. - : CEUR-WS.
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Konferensbidrag (refereegranskat)abstract
- Requirements quality literature abounds with publications presenting artifacts, such as data sets and tools. However, recent systematic studies show that more than 80% of these artifacts have become unavailable or were never made public, limiting reproducibility and reusability. In this work, we report on an attempt to recover those artifacts. To that end, we requested corresponding authors of unavailable artifacts to recover and disclose them according to open science principles. Our results, based on 19 answers from 35 authors (54% response rate), include an assessment of the availability of requirements quality artifacts and a breakdown of authors’ reasons for their continued unavailability. Overall, we improved the availability of seven data sets and seven implementations. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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| 13. |
- Frattini, Julian, 1995-, et al.
(författare)
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Measuring the Fitness-for-Purpose of Requirements : An initial Model of Activities and Attributes
- 2024
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Ingår i: Proceedings of the IEEE International Conference on Requirements Engineering. - : IEEE Computer Society. - 9798350395112 ; , s. 398-406
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Konferensbidrag (refereegranskat)abstract
- Requirements engineering aims to fulfill a purpose, i.e., inform subsequent software development activities about stakeholders' needs and constraints that must be met by the system under development. The quality of requirements artifacts and processes is determined by how fit for this purpose they are, i.e., how they impact activities affected by them. However, research on requirements quality lacks a comprehensive overview of these activities and how to measure them. In this paper, we specify the research endeavor addressing this gap and propose an initial model of requirements-affected activities and their attributes. We construct a model from three distinct data sources, including both literature and empirical data. The results yield an initial model containing 24 activities and 16 attributes quantifying these activities. Our long-term goal is to develop evidence-based decision support on how to optimize the fitness for purpose of the RE phase to best support the subsequent, affected software development process. We do so by measuring the effect that requirements artifacts and processes have on the attributes of these activities. With the contribution at hand, we invite the research community to critically discuss our research roadmap and support the further evolution of the model. © 2024 IEEE.
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| 14. |
- Frattini, Julian, 1995-, et al.
(författare)
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Requirements quality research : a harmonized theory, evaluation, and roadmap
- 2023
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Ingår i: Requirements Engineering. - : Springer Science+Business Media B.V.. - 0947-3602 .- 1432-010X. ; 28:4, s. 507-520
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Tidskriftsartikel (refereegranskat)abstract
- High-quality requirements minimize the risk of propagating defects to later stages of the software development life cycle. Achieving a sufficient level of quality is a major goal of requirements engineering. This requires a clear definition and understanding of requirements quality. Though recent publications make an effort at disentangling the complex concept of quality, the requirements quality research community lacks identity and clear structure which guides advances and puts new findings into an holistic perspective. In this research commentary, we contribute (1) a harmonized requirements quality theory organizing its core concepts, (2) an evaluation of the current state of requirements quality research, and (3) a research roadmap to guide advancements in the field. We show that requirements quality research focuses on normative rules and mostly fails to connect requirements quality to its impact on subsequent software development activities, impeding the relevance of the research. Adherence to the proposed requirements quality theory and following the outlined roadmap will be a step toward amending this gap. © 2023, The Author(s).
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| 15. |
- Frattini, Julian, 1995-, et al.
(författare)
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Requirements Quality Research: a harmonized Theory, Evaluation, and Roadmap
-
Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- High-quality requirements minimize the risk of propagating defects to later stages of the software development life-cycle. Achieving a sufficient level of quality is a major goal of requirements engineering. This requires a clear definition and understanding of requirements quality. Though recent publications make an effort at disentangling the complex concept of quality, the requirements quality research community lacks identity and clear structure which guides advances and puts new findings into an holistic perspective. In this research commentary we contribute(1) a harmonized requirements quality theory organizing its core concepts, (2) an evaluation of the current state of requirements quality research, and (3) a research roadmap to guide advancements in the field.
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| 16. |
- Frattini, Julian, 1995-, et al.
(författare)
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Requirements quality research artifacts : Recovery, analysis, and management guideline
- 2024
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Ingår i: Journal of Systems and Software. - : Elsevier. - 0164-1212 .- 1873-1228. ; 216
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Tidskriftsartikel (refereegranskat)abstract
- Requirements quality research, which is dedicated to assessing and improving the quality of requirements specifications, is dependent on research artifacts like data sets (containing information about quality defects) and implementations (automatically detecting and removing these defects). However, recent research exposed that the majority of these research artifacts have become unavailable or have never been disclosed, which inhibits progress in the research domain. In this work, we aim to improve the availability of research artifacts in requirements quality research. To this end, we (1) extend an artifact recovery initiative, (2) empirically evaluate the reasons for artifact unavailability using Bayesian data analysis, and (3) compile a concise guideline for open science artifact disclosure. Our results include 10 recovered data sets and 7 recovered implementations, empirical support for artifact availability improving over time and the positive effect of public hosting services, and a pragmatic artifact management guideline open for community comments. With this work, we hope to encourage and support adherence to open science principles and improve the availability of research artifacts for the requirements research quality community. © 2024 The Author(s)
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| 17. |
- Henao, Pablo Restrepo, et al.
(författare)
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Transfer Learning for Mining Feature Requests and Bug Reports from Tweets and App Store Reviews
- 2021
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Ingår i: Proceedings of the IEEE International Conference on Requirements Engineering. - : IEEE Computer Society. - 9781665418980 ; , s. 80-86
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Konferensbidrag (refereegranskat)abstract
- Identifying feature requests and bug reports in user comments holds great potential for development teams. However, automated mining of RE-related information from social media and app stores is challenging since (1) about 70% of user comments contain noisy, irrelevant information, (2) the amount of user comments grows daily making manual analysis unfeasible, and (3) user comments are written in different languages. Existing approaches build on traditional machine learning (ML) and deep learning (DL), but fail to detect feature requests and bug reports with high Recall and acceptable Precision which is necessary for this task. In this paper, we investigate the potential of transfer learning (TL) for the classification of user comments. Specifically, we train both monolingual and multilingual BERT models and compare the performance with state-of-the-art methods. We found that monolingual BERT models outperform existing baseline methods in the classification of English App Reviews as well as English and Italian Tweets. However, we also observed that the application of heavyweight TL models does not necessarily lead to better performance. In fact, our multilingual BERT models perform worse than traditional ML methods. © 2021 IEEE.
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| 18. |
- Jadallah, Noah, et al.
(författare)
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CATE : CAusality Tree Extractor from Natural Language Requirements
- 2021
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Ingår i: Proceedings of the IEEE International Conference on Requirements Engineering. - : IEEE Computer Society. - 9781665418980 ; , s. 77-79
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Konferensbidrag (refereegranskat)abstract
- Causal relations (If A, then B) are prevalent in requirements artifacts. Automatically extracting causal relations from requirements holds great potential for various RE activities (e.g., automatic derivation of suitable test cases). However, we lack an approach capable of extracting causal relations from natural language with reasonable performance. In this paper, we present our tool CATE (CAusality Tree Extractor), which is able to parse the composition of a causal relation as a tree structure. CATE does not only provide an overview of causes and effects in a sentence, but also reveals their semantic coherence by translating the causal relation into a binary tree. We encourage fellow researchers and practitioners to use CATE at https://causalitytreeextractor.com/ © 2021 IEEE.
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| 19. |
- Jedrzejewski, Felix, 1994-, et al.
(författare)
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Adversarial Machine Learning in Industry : A Systematic Literature Review
- 2024
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Ingår i: Computers & security (Print). - : Elsevier. - 0167-4048 .- 1872-6208. ; 145
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Forskningsöversikt (refereegranskat)abstract
- Adversarial Machine Learning (AML) discusses the act of attacking and defending Machine Learning (ML) Models, an essential building block of Artificial Intelligence (AI). ML is applied in many software-intensive products and services and introduces new opportunities and security challenges. AI and ML will gain even more attention from the industry in the future, but threats caused by already-discovered attacks specifically targeting ML models are either overseen, ignored, or mishandled. Current AML research investigates attack and defense scenarios for ML in different industrial settings with a varying degree of maturity with regard to academic rigor and practical relevance. However, to the best of our knowledge, a synthesis of the state of academic rigor and practical relevance is missing. This literature study reviews studies in the area of AML in the context of industry, measuring and analyzing each study's rigor and relevance scores. Overall, all studies scored a high rigor score and a low relevance score, indicating that the studies are thoroughly designed and documented but miss the opportunity to include touch points relatable for practitioners. © 2024 The Author(s)
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| 20. |
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| 21. |
- Wiecher, Carsten, et al.
(författare)
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Model-based analysis and specification of functional requirements and tests for complex automotive systems
- 2024
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Ingår i: Systems Engineering. - : John Wiley & Sons. - 1098-1241 .- 1520-6858. ; 27:4, s. 728-744
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Tidskriftsartikel (refereegranskat)abstract
- The specification of requirements and tests are crucial activities in automotive development projects. However, due to the increasing complexity of automotive systems, practitioners fail to specify requirements and tests for distributed and evolving systems with complex interactions when following traditional development processes. To address this research gap, we propose a technique that starts with the early identification of validation concerns from a stakeholder perspective, which we use to systematically design tests that drive a scenario-based modeling and analysis of system requirements. To ensure complete and consistent requirements and test specifications in a form that is required in automotive development projects, we develop a Model-Based Systems Engineering (MBSE) methodology. This methodology supports system architects and test designers in the collaborative application of our technique and in maintaining a central system model, in order to automatically derive the required specifications. We evaluate our methodology by applying it at KOSTAL (Tier1 supplier) and within student projects as part of the masters program Embedded Systems Engineering. Our study corroborates that our methodology is applicable and improves existing requirements and test specification processes by supporting the integrated and stakeholder-focused modeling of product and validation systems, where the early definition of stakeholder and validation concerns fosters a problem-oriented, iterative and test-driven requirements modeling. © 2024 Wiley Periodicals, Inc.
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