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  • 1.
    Bauer, Andreas
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Frattini, Julian
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Alégroth, Emil
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Augmented Testing to support Manual GUI-based Regression Testing: An Empirical StudyManuscript (preprint) (Other academic)
    Abstract [en]

    Context: Manual graphical user interface (GUI) software testing presents a substantial part of the overall practiced testing efforts, despite various research efforts to further increase test automation. Augmented Testing (AT), a novel approach for GUI testing, aims to aid manual GUI-based testing through a tool-supported approach where an intermediary visual layer is rendered between the system under test (SUT) and the tester, superimposing relevant test information.

    Objective: The primary objective of this study is to gather empirical evidence regarding AT's efficiency compared to manual GUI-based regression testing. Existing studies involving testing approaches under the AT definition primarily focus on exploratory GUI testing, leaving a gap in the context of regression testing. As a secondary objective, we investigate AT's benefits, drawbacks, and usability issues when deployed with the demonstrator tool, Scout.

    Method: We conducted an experiment involving 13 industry professionals, from six companies, comparing AT to manual GUI-based regression testing. These results were complemented by interviews and Bayesian data analysis (BDA) of the study's quantitative results.

    Results: The results of the Bayesian data analysis revealed that the use of AT shortens test durations in 70% of the cases on average, concluding that AT is more efficient.When comparing the means of the total duration to perform all tests, AT reduced the test duration by 36% in total. Participant interviews highlighted nine benefits and eleven drawbacks of AT, while observations revealed four usability issues.

    Conclusion: This study makes an empirical contribution to understanding Augmented Testing, a promising approach to improve the efficiency of GUI-based regression testing in practice. Furthermore, it underscores the importance of continual refinements of AT.

  • 2.
    Fischbach, Jannik
    et al.
    Netlight Consulting GmbH, Germany.
    Adam, Max
    Technical University of Munich, Germany.
    Dzhagatspanyan, Victor
    Technical University of Munich, Germany.
    Mendez, Daniel
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Frattini, Julian
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Kosenkov, Oleksandr
    Fortiss GmbH, Germany.
    Elahidoost, Parisa
    Fortiss GmbH, Germany.
    Automatic ESG Assessment of Companies by Mining and Evaluating Media Coverage Data: NLP Approach and Tool2023In: Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023, Institute of Electrical and Electronics Engineers (IEEE), 2023, p. 2823-2830Conference paper (Refereed)
    Abstract [en]

    [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.

  • 3.
    Fischbach, Jannik
    et al.
    Qualicen GmbH, DEU.
    Frattini, Julian
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Mendez, Daniel
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Unterkalmsteiner, Michael
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Femmer, Henning
    Qualicen GmbH, DEU.
    Vogelsang, Andreas
    University of Cologne, DEU.
    How Do Practitioners Interpret Conditionals in Requirements?2021In: Lecture Notes in Computer Science / [ed] Ardito L., Jedlitschka A., Morisio M., Torchiano M., Springer Science and Business Media Deutschland GmbH , 2021, Vol. 13126, p. 85-102Conference paper (Refereed)
    Abstract [en]

    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.

  • 4.
    Fischbach, Jannik
    et al.
    Qualicen GmbH, DEU.
    Frattini, Julian
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Spaans, Arjen
    Qualicen GmbH, DEU.
    Kummeth, Maximilian
    Qualicen GmbH, DEU.
    Vogelsang, Andreas
    University of Cologne, DEU.
    Mendez, Daniel
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Unterkalmsteiner, Michael
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Automatic Detection of Causality in Requirement Artifacts: The CiRA Approach2021In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) / [ed] Dalpiaz F., Spoletini P., Springer Science and Business Media Deutschland GmbH , 2021, Vol. 12685, p. 19-36Conference paper (Refereed)
    Abstract [en]

    [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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  • 5.
    Fischbach, Jannik
    et al.
    Qualicen GmbH, DEU.
    Frattini, Julian
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Vogelsang, Andreas
    University of Cologne, DEU.
    CiRA: A Tool for the Automatic Detection of Causal Relationships in Requirements Artifacts2021In: CEUR Workshop Proceedings / [ed] Aydemir F.B.,Gralha C.,Daneva M.,Groen E.C.,Herrmann A.,Mennig P.,Abualhaija S.,Ferrari A.,Guo J.,Guizzardi R.,Horkoff J.,Perini A.,Susi A.,Breaux T.,Franch X.,Ernst N.,Paja E.,Seyff N., CEUR-WS , 2021, Vol. 2857Conference paper (Refereed)
    Abstract [en]

    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.
    Netlight Consulting GmbH, DEU.
    Frattini, Julian
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Vogelsang, Andreas
    University of Cologne, DEU.
    Mendez, Daniel
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Unterkalmsteiner, Michael
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Wehrle, Andreas
    Allianz Deutschland AG, DEU.
    Henao, Pablo Restrepo
    Netlight Consulting GmbH, DEU.
    Yousefi, Parisa
    Ericsson, SWE.
    Juricic, Tedi
    Ericsson, SWE.
    Radduenz, Jeannette
    Allianz Deutschland AG, DEU.
    Wiecher, Carsten
    Leopold Kostal GmbH & Co. KG, DEU.
    Automatic creation of acceptance tests by extracting conditionals from requirements: NLP approach and case study2023In: Journal of Systems and Software, ISSN 0164-1212, E-ISSN 1873-1228, Vol. 197, article id 111549Article in journal (Refereed)
    Abstract [en]

    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

  • 7.
    Fischbach, Jannik
    et al.
    Qualicen GmbH, DEU.
    Springer, Tobias
    Technical University of Munich, DEU.
    Frattini, Julian
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Femmer, Henning
    Qualicen GmbH, DEU.
    Vogelsang, Andreas
    University of Cologne, DEU.
    Mendez, Daniel
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Fine-Grained Causality Extraction from Natural Language Requirements Using Recursive Neural Tensor Networks2021In: Proceedings of the IEEE International Conference on Requirements Engineering / [ed] Yue T., Mirakhorli M., IEEE Computer Society , 2021, p. 60-69Conference paper (Refereed)
    Abstract [en]

    [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.

  • 8.
    Frattini, Julian
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    CEREC: Causality Extraction from Requirements Artifacts2020In: Proceedings - 7th International Workshop on Artificial Intelligence and Requirements Engineering, AIRE 2020, Institute of Electrical and Electronics Engineers Inc. , 2020, p. 79-82, article id 9233006Conference paper (Refereed)
    Abstract [en]

    The cause-effect recognition (CEREC) system provides an API for causality extraction tailored to the requirements engineering context. The library is written in Java and is released under the MIT open source license. In this paper, the underlying algorithm is described, and a demonstration of the active learning component for causality extraction is outlined. The results are promising and strengthen the confidence in exploring automation approaches for model-based testing. © 2020 IEEE.

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  • 9.
    Frattini, Julian
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Embracing the Silence2023Report (Other (popular science, discussion, etc.))
    Abstract [en]

    One major challenge in an online classroom is students' engagement. Despite university guidelines, teachers have experienced students' aversion against turning on their camera during a lecture and against speaking up at all. This results in a very unidirectional interaction in which teachers receive little direct feedback about students perception of the course, particularly of their struggles.

    Instead of tediously encouraging students to turn on their cameras, we chose to embrace the silence and accommodate the new way of participating in online courses.

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    Embracing the silence Julian Fruttini BP2023 pdf
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    Embracing the silence Presentation BP2023
  • 10.
    Frattini, Julian
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Towards good-enough Requirements Engineering: a theoretical Foundation for Requirements Quality2023Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Context: Requirements Engineering (RE) research has established a common agreement on the impact that the quality of requirements has on subsequent software development activities and artifacts. Furthermore, empirical investigations suppose that RE quality defects tend to scale in cost for remediation when left unattended. This motivates the need for requirements quality assurance.

    Problem: This need has been met with requirements quality research, which abounds with publications proposing writing rules and guidelines that are meant to ensure requirements of high quality. However, recent studies have questioned the rigor and relevance of these publications, which would undermine the practical applicability of requirements quality research: requirements quality is a means to an end and serves a specific purpose (i.e., minimizing the emitted risk on downstream activities), but when this purpose is not met due to lack of a rigor and practical relevance, the approach to researching requirements quality needs to be rethought.

    Aim: The notion of good-enough requirements engineering constitutes a context-sensitive, activity-based perspective on requirements quality. In this thesis, we aim at both (1) understanding and (2) exploring possibilities of operationalizing this notion.

    Methods: We employ a mixed-methods approach to achieve our aim. We use theory adoption in order to provide a theoretical foundation for requirements quality research, conduct a survey to understand the level of theory adherence in the requirements quality literature, and perform subject-based classification to generate an overview of theory-related elements proposed in literature. 

    Results: Through theory adoption we derive a harmonized, activity-based requirements quality theory that frames requirements quality according to its impact on subsequent activities and hence ensures its relevance. The subsequent survey confirms that there is a lack of rigor and relevance in previous requirements quality publications, which likely explains the lack of adoption of the research in practice. The overview of quality factors in a subject-based classification is a first step to centralize requirements quality research for visibility and effective reuse.

    Conclusion: The notion of good-enough requirements engineering has the potential to re-focus requirements quality research on a more profound notion of rigor and relevance. In this thesis, we report on a first requirements quality theory. Through adherence to this requirements quality theory and contribution to the central repository of subject-based classification, the operationalization of the concept of good-enough requirements engineering can effectively support predicting the impact that requirements quality has on subsequent software development activities in the future.

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  • 11.
    Frattini, Julian
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Fischbach, Jannik
    Netlight Consulting GmbH and Fortiss GmbH, Germany.
    Bauer, Andreas
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    CiRA: An Open-Source Python Package for Automated Generation of Test Case Descriptions from Natural Language Requirements2023In: Proceedings - 31st IEEE International Requirements Engineering Conference Workshops, REW 2023 / [ed] Schneider K., Dalpiaz F., Horkoff J., Institute of Electrical and Electronics Engineers (IEEE), 2023, p. 68-71Conference paper (Refereed)
    Abstract [en]

    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.

  • 12.
    Frattini, Julian
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Fischbach, Jannik
    Qualicen GmbH, GER.
    Mendez, Daniel
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Unterkalmsteiner, Michael
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Vogelsang, Andreas
    University of Cologne, GER.
    Wnuk, Krzysztof
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Causality in requirements artifacts: prevalence, detection, and impact2023In: Requirements Engineering, ISSN 0947-3602, E-ISSN 1432-010X, Vol. 28, no 1, p. 49-74Article in journal (Refereed)
    Abstract [en]

    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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  • 13.
    Frattini, Julian
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Fucci, Davide
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Mendez, Daniel
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Spinola, Rodrigo
    Virginia Commonwealth University, Richmond, USA.
    Mandic, Vladimir
    University of Novi Sad, Serbia.
    Tausan, Nebojsa
    University of Novi Sad, Serbia.
    Ahmad, Ovais
    Karlstad University.
    Gonzalez-Huerta, Javier
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    An initial Theory to Understand and Manage Requirements Engineering Debt in Practice2023In: Information and Software Technology, ISSN 0950-5849, E-ISSN 1873-6025, Vol. 159, article id 107201Article in journal (Refereed)
    Abstract [en]

    Context

    Advances in technical debt research demonstrate the benefits of applying the financial debt metaphor to support decision-making in software development activities. Although decision-making during requirements engineering has significant consequences, the debt metaphor in requirements engineering is inadequately explored.

    Objective

    We aim to conceptualize how the debt metaphor applies to requirements engineering by organizing concepts related to practitioners’ understanding and managing of requirements engineering debt (RED).

    Method

    We conducted two in-depth expert interviews to identify key requirements engineering debt concepts and construct a survey instrument. We surveyed 69 practitioners worldwide regarding their perception of the concepts and developed an initial analytical theory.

    Results

    We propose a RED theory that aligns key concepts from technical debt research but emphasizes the specific nature of requirements engineering. In particular, the theory consists of 23 falsifiable propositions derived from the literature, the interviews, and survey results.

    Conclusions

    The concepts of requirements engineering debt are perceived to be similar to their technical debt counterpart. Nevertheless, measuring and tracking requirements engineering debt are immature in practice. Our proposed theory serves as the first guide toward further research in this area.

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    IST22_RED
  • 14.
    Frattini, Julian
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Junker, Maximilian
    Qualicen GmbH, DEU.
    Unterkalmsteiner, Michael
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering. fortiss GmbH, DEU.
    Mendez, Daniel
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering. fortiss GmbH, DEU.
    Automatic Extraction of Cause-Effect-Relations from Requirements Artifacts2020In: Proceedings - 2020 35th IEEE/ACM International Conference on Automated Software Engineering, ASE 2020, Institute of Electrical and Electronics Engineers Inc. , 2020, p. 561-572, article id 9286079Conference paper (Refereed)
    Abstract [en]

    Background: The detection and extraction of causality from natural language sentences have shown great potential in various fields of application. The field of requirements engineering is eligible for multiple reasons: (1) requirements artifacts are primarily written in natural language, (2) causal sentences convey essential context about the subject of requirements, and (3) extracted and formalized causality relations are usable for a (semi-)automatic translation into further artifacts, such as test cases. Objective: We aim at understanding the value of interactive causality extraction based on syntactic criteria for the context of requirements engineering. Method: We developed a prototype of a system for automatic causality extraction and evaluate it by applying it to a set of publicly available requirements artifacts, determining whether the automatic extraction reduces the manual effort of requirements formalization. Result: During the evaluation we analyzed 4457 natural language sentences from 18 requirements documents, 558 of which were causal (12.52%). The best evaluation of a requirements document provided an automatic extraction of 48.57% cause-effect graphs on average, which demonstrates the feasibility of the approach. Limitation: The feasibility of the approach has been proven in theory but lacks exploration of being scaled up for practical use. Evaluating the applicability of the automatic causality extraction for a requirements engineer is left for future research. Conclusion: A syntactic approach for causality extraction is viable for the context of requirements engineering and can aid a pipeline towards an automatic generation of further artifacts from requirements artifacts. © 2020 ACM.

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  • 15.
    Frattini, Julian
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Lloyd, Montgomery
    Universität Hamburg, DEU.
    Jannik, Fischbach
    Netlight GmbH / fortiss GmbH, DEU.
    Unterkalmsteiner, Michael
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Mendez, Daniel
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Fucci, Davide
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    A Live Extensible Ontology of Quality Factors for Textual Requirements2022In: Proceedings of the IEEE International Conference on Requirements Engineering / [ed] Knauss E., Mussbacher G., Arora C., Bano M., Schneider, IEEE, 2022, p. 274-280Conference paper (Refereed)
    Abstract [en]

    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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  • 16.
    Frattini, Julian
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Montgomery, Lloyd
    Universität Hamburg, DEU.
    Fischbach, Jannik
    Qualicen GmbH, DEU.
    Mendez, Daniel
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Fucci, Davide
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Unterkalmsteiner, Michael
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Requirements Quality Research: a harmonized Theory, Evaluation, and RoadmapManuscript (preprint) (Other academic)
    Abstract [en]

    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.

  • 17.
    Frattini, Julian
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Montgomery, Lloyd
    University of Hamburg, Germany.
    Fischbach, Jannik
    Netlight Consulting GmbH, Germany.
    Mendez, Daniel
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Fucci, Davide
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Unterkalmsteiner, Michael
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Requirements quality research: a harmonized theory, evaluation, and roadmap2023In: Requirements Engineering, ISSN 0947-3602, E-ISSN 1432-010X, Vol. 28, no 4, p. 507-520Article in journal (Refereed)
    Abstract [en]

    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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  • 18.
    Frattini, Julian
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Montgomery, Lloyd
    University of Hamburg, Germany.
    Fucci, Davide
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Fischbach, Jannik
    Netlight Consulting GmbH, Germany.
    Unterkalmsteiner, Michael
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Mendez, Daniel
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Let’s Stop Building at the Feet of Giants: Recovering unavailable Requirements Quality Artifacts2023In: CEUR Workshop Proceedings / [ed] Ferrari A., Penzenstadler B., Penzenstadler B., Hadar I., Oyedeji S., Abualhaija S., Vogelsang A., Deshpande G., Rachmann A., Gulden J., Wohlgemuth A., Hess A., Fricker S., Guizzardi R., Horkoff J., Perini A., Susi A., Karras O., Dalpiaz F., Moreira A., Amyot D., Spoletini P., CEUR-WS , 2023, Vol. 3378Conference paper (Refereed)
    Abstract [en]

    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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  • 19.
    Henao, Pablo Restrepo
    et al.
    Technical University of Munich, DEU.
    Fischbach, Jannik
    Qualicen GmbH, DEU.
    Spies, Dominik
    Qualicen GmbH, DEU.
    Frattini, Julian
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Vogelsang, Anderas
    University of Cologne, DEU.
    Transfer Learning for Mining Feature Requests and Bug Reports from Tweets and App Store Reviews2021In: Proceedings of the IEEE International Conference on Requirements Engineering / [ed] Yue T., Mirakhorli M., IEEE Computer Society , 2021, p. 80-86Conference paper (Refereed)
    Abstract [en]

    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.

  • 20.
    Jadallah, Noah
    et al.
    Technical University of Munich, DEU.
    Fischbach, Jannik
    Qualicen GmbH, DEU.
    Frattini, Julian
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Vogelsang, Andreas
    University of Cologne, DEU.
    CATE: CAusality Tree Extractor from Natural Language Requirements2021In: Proceedings of the IEEE International Conference on Requirements Engineering / [ed] Yue T., Mirakhorli M., IEEE Computer Society , 2021, p. 77-79Conference paper (Refereed)
    Abstract [en]

    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.

  • 21.
    Zabardast, Ehsan
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Frattini, Julian
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Gonzalez-Huerta, Javier
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Mendez, Daniel
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Gorschek, Tony
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Asset Management in Software Engineering: What is it after all?2021Manuscript (preprint) (Other academic)
    Abstract [en]

    When developing and maintaining software-intensive products or services, we often depend on various "assets", denoting the inherent value to selected artefacts when carrying out development and maintenance activities. When exploring various areas in Software Engineering, such as Technical Debt and our work with industry partners, we soon realised that many terms and concepts are frequently intermixed and used inconsistently. Despite the central role of assets to software engineering, management, and evolution, little thoughts are yet invested into what assets eventually are. A clear terminology of "assets" and related concepts, such as "value" or "value degradation", just to name two, are crucial for setting up effective software engineering practices.

    As a starting point for our own work, we had to define the terminology and concepts, and extend the reasoning around the concepts. In this position paper, we critically reflect upon the resulting notion of Assets in Software Engineering. We explore various types of assets, their main characteristics, such as providing inherent value. We discuss various types of value degradation and the possible implications of this on the planning, realisation, and evolution of software-intensive products and services over time. 

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  • 22.
    Zabardast, Ehsan
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Frattini, Julian
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Gonzalez-Huerta, Javier
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Mendez, Daniel
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Gorschek, Tony
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Wnuk, Krzysztof
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Assets in Software Engineering: What are they after all?2022In: Journal of Systems and Software, ISSN 0164-1212, E-ISSN 1873-1228, Vol. 193, article id 111485Article, review/survey (Refereed)
    Abstract [en]

    During the development and maintenance of software-intensive products or services, we depend on various artefacts. Some of those artefacts, we deem central to the feasibility of a project and the product's final quality. Typically, these central artefacts are referred to as assets. However, despite their central role in the software development process, little thought is yet invested into what eventually characterises as an asset, often resulting in many terms and underlying concepts being mixed and used inconsistently. A precise terminology of assets and related concepts, such as asset degradation, are crucial for setting up a new generation of cost-effective software engineering practices. In this position paper, we critically reflect upon the notion of assets in software engineering. As a starting point, we define the terminology and concepts of assets and extend the reasoning behind them. We explore assets’ characteristics and discuss what asset degradation is as well as its various types and the implications that asset degradation might bring for the planning, realisation, and evolution of software-intensive products and services over time. We aspire to contribute to a more standardised definition of assets in software engineering and foster research endeavours and their practical dissemination in a common, more unified direction. © 2022 The Authors

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