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15th International Conference on Evaluation of Novel Approaches to Software engineering (ENASE)

作者： Franch, Xavier Univ Politecn Cataluna UPC BarcelonaTech Barcelona Spain

ISBN: (纸本)9783030700058;9783030700065

data-driven approaches are becoming dominant in almost every single software engineering activity, and requirements engineering is not the exception. The analysis of data coming from several sources may indeed become an extremely useful input to requirements elicitation and management. However, benefits do not come for free. Techniques such as natural language processing and machine learning are difficult to master and require high-quality data and specific competences from different fields, whilst their generalization remains as a challenge. This paper introduces the main concepts behind data-driven requirements engineering, provides an overview of the state of the art in the field and identifies the main challenges to be addressed.

关键词： requirements engineering data-driven requirements engineering Feedback Natural language processing Software analytics Monitoring Decision-making Release planning

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Towards Integrating data-driven requirements engineering into the Software Development Process: A Vision Paper 26th

Towards Integrating Data-Driven Requirements Engineering int...

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26th International Working Conference on requirements engineering - Foundation for Software Quality (REFSQ)

作者： Franch, Xavier Seyff, Norbert Oriol, Marc Fricker, Samuel Groher, Iris Vierhauser, Michael Wimmer, Manuel Univ Politecn Cataluna Barcelona Spain Univ Appl Sci & Arts Northwestern Switzerland FHN Windisch Switzerland Johannes Kepler Univ Linz Linz Austria CDL MINT Linz Austria

ISBN: (纸本)9783030444297;9783030444280

[Context and motivation] Modern software engineering processes have shifted from traditional upfront requirements engineering (RE) to a more continuous way of conducting RE, particularly including data-driven approaches. [Question/problem] However, current research on data-driven RE focuses more on leveraging certain techniques such as natural language processing or machine learning than on making the concept fit for facilitating its use in the entire software development process. [Principal ideas/results] In this paper, we propose a research agenda composed of six distinct research directions. These include a data-driven RE infrastructure, embracing data heterogeneity, context-aware adaptation, data analysis and decision support, privacy and confidentiality, and finally process integration. Each of these directions addresses challenges that impede the broader use of data-driven RE. [Contribution] For researchers, our research agenda provides topics relevant to investigate. For practitioners, overcoming the underlying challenges with the help of the proposed research will allow to adopt a data-driven RE approach and facilitate its seamless integration into modern software engineering. For users, the proposed research will enable the transparency, control, and security needed to trust software systems and software providers.

关键词： data-driven requirements engineering Feedback gathering requirements monitoring Model-driven engineering

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Leveraging Large Language Model ChatGPT for enhanced understanding of end-user emotions in social media feedbacks

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 261卷

作者： Khan, Nek Dil Khan, Javed Ali Li, Jianqiang Ullah, Tahir Zhao, Qing Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Univ Hertfordshire Sch Phys Engn & Comp Sci Dept Comp Sci Hatfield AL10 England Beijing Inst Technol Sch Comp Sci Beijing 100124 Peoples R China

For software evolution, user feedback has become a meaningful way to improve applications. Recent studies show a significant increase in analyzing end-user feedback from various social media platforms for software evolution. However, less attention has been given to the end-user feedback for low-rating software applications. Also, such approaches are developed mainly on the understanding of human annotators who might have subconsciously tried fora second guess, questioning the validity of the methods. For this purpose, we proposed an approach that analyzes end-user feedback for low-rating applications to identify the end-user opinion types associated with negative reviews (an issue or bug). Also, we utilized Generative Artificial Intelligence (AI), i.e., ChatGPT, as an annotator and negotiator when preparing a truth set for the deep learning (DL) classifiers to identify end-user emotion. For the proposed approach, we first used the ChatGPT Application Programming Interface (API) to identify negative end-user feedback by processing 71853 reviews collected from 45 apps in the Amazon store. Next, a novel grounded theory is developed by manually processing end-user negative feedback to identify frequently associated emotion types, including anger, confusion, disgust, distrust, disappointment, fear, frustration, and sadness. Next, two datasets were developed, one with human annotators using a content analysis approach and the other using ChatGPT API with the identified emotion types. Next, another round is conducted with ChatGPT to negotiate over the conflicts with the human-annotated dataset, resulting in a conflict-free emotion detection dataset. Finally, various DL classifiers, including LSTM, BILSTM, CNN, RNN, GRU, BiGRU and BiRNN, are employed to identify their efficacy in detecting end-users emotions by preprocessing the input data, applying feature engineering, balancing the data set, and then training and testing them using a cross-validation approach. We obtaine

关键词： data-driven requirements engineering Generative Artificial Intelligence Sentiment Analysis Large Language Models Software evolution ChatGPT

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Specifying requirements for collection and analysis of online user feedback

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requirements engineering 2023年第1期28卷 75-96页

作者： Astegher, M. Busetta, P. Gabbasov, A. Pedrotti, M. Perini, A. Susi, A. Delta Informat SpA Trento Italy Fdn Bruno Kessler Trento Italy

According to data-driven requirements engineering (RE), explicit and implicit user feedback can be considered a relevant source of requirements, thus supporting requirements elicitation. However, limited attention has been paid so far to the role of online feedback in RE tasks, such as requirements validation, and on how to specify what online feedback to collect and analyse. We performed an action research study, together with a company that developed a platform for online training. This paper presents the design and execution of the study, and a discussion of its results. This study provides evidence about the need of practitioners to follow a simple but systematic approach for specifying requirements for data collection and analysis, at design time. Another outcome of this study is a method to tackle this task that leverages goal-oriented requirements modelling combined with Goal-Question-Metric. The applicability of the method has been explored on two industrial evaluations, while the perceived effectiveness, efficiency and acceptance have been assessed with practitioners through a dedicated survey.

关键词： data-driven requirements engineering User feedback Goal-question-metric Goal-oriented requirements analysis

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Holistic data-driven requirements elicitation in the big data era

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SOFTWARE AND SYSTEMS MODELING 2022年第4期21卷 1389-1410页

作者： Henriksson, Aron Zdravkovic, Jelena Stockholm Univ Dept Comp & Syst Sci POB 7003 S-16407 Kista Sweden

Digital transformation stimulates continuous generation of large amounts of digital data, both in organizations and in society at large. As a consequence, there have been growing efforts in the requirements engineering community to consider digital data as sources for requirements acquisition, in addition to human stakeholders. The volume, velocity and variety of the data make requirements discovery increasingly dynamic, but also unstructured and complex, which current elicitation methods are unable to consider and manage in a systematic and efficient manner. We propose a framework, in the form of a conceptual metamodel and a method, for continuous and automated acquisition, analysis and aggregation of heterogeneous digital sources that aims to support data-driven requirements elicitation and management. The usability of the framework is partially validated by an in-depth case study from the business sector of video game development.

关键词： data-driven requirements engineering Big data requirements modeling Machine learning Natural language processing

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Nudge Me and I Change: The Effect of Digital Nudging on Software Engineers’ Motivation and Behaviour Towards data Quality 37

Nudge Me and I Change: The Effect of Digital Nudging on Soft...

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37th International BCS Human-Computer Interaction Conference, BCS HCI 2024

作者： Alkadhi, Bushra Hendley, Robert Bahsoon, Rami School of Computer Science University of Birmingham Birmingham United Kingdom

Maintaining high data Quality (DQ) is critical in data-driven approaches;however, it is still an open challenge in data-driven requirements engineering (DDRE) where data steers the requirements engineering process. The software engineering view of DQ is still limited and is often neglected due to a lack of motivation and the inadequacy of skills in identifying DQ issues. Issues such as bias, inconsistency, noise etc. can compromise the quality of the analysis and product. This work draws upon work in behaviour change, in particular nudge theory, to motivate software engineers to adopt better practices towards DQ. We report on a preliminary controlled experiment, where we empirically evaluate the effect of digital nudging in terms of performance, efficiency, and engagement. Our positive findings provide promising initial empirical insights and pave the way for further research on the use of digital nudging for altering human behaviour in general, and for software engineers in particular. © Alkadhi et al.

关键词： Behaviour Change data Quality data-driven requirements engineering Human-Centred Computing Motivation Nudge Theory Software engineering User Studies

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Detecting Feature Requests of Third-Party Developers through Machine Learning: A Case Study of the SAP Community 56

Detecting Feature Requests of Third-Party Developers through...

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56th Annual Hawaii International Conference on System Sciences (HICSS)

作者： Kauschinger, Martin Vieth, Niklas Schreieck, Maximilian Krcmar, Helmut Tech Univ Munich Munich Germany SAP Deutschland SE & Co KG Hamburg Germany Univ Innsbruck Innsbruck Austria

ISBN: (纸本)9780998133164

The elicitation of requirements is central for the development of successful software products. While traditional requirement elicitation techniques such as user interviews are highly labor-intensive, data-driven elicitation techniques promise enhanced scalability through the exploitation of new data sources like app store reviews or social media posts. For enterprise software vendors, requirements elicitation remains challenging because app store reviews are scarce and vendors have no direct access to users. Against this background, we investigate whether enterprise software vendors can elicit requirements from their sponsored developer communities through data-driven techniques. Following the design science methodology, we collected data from the SAP Community and developed a supervised machine learning classifier, which automatically detects feature requests of third-party developers. Based on a manually labeled data set of 1,500 questions, our classifier reached a high accuracy of 0.819. Our findings reveal that supervised machine learning models are an effective means for the identification of feature requests.

关键词： data-driven requirements engineering Enterprise Software Machine Learning Online Community Platform Ecosystem

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Unsupervised Topic Discovery in User Comments 29

Unsupervised Topic Discovery in User Comments

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29th IEEE International requirements engineering Conference (RE)

作者： Stanik, Christoph Pietz, Tim Maalej, Walid Univ Hamburg Hamburg Germany

ISBN: (纸本)9781665428569

On social media platforms like Twitter, users regularly share their opinions and comments with software vendors and service providers. Popular software products might get thousands of user comments per day. Research has shown that such comments contain valuable information for stakeholders, such as feature ideas, problem reports, or support inquiries. However, it is hard to manually manage and grasp a large amount of user comments, which can be redundant and of a different quality. Consequently, researchers suggested automated approaches to extract valuable comments, e.g., through problem report classifiers. However, these approaches do not aggregate semantically similar comments into specific aspects to provide insights like how often users reported a certain problem. We introduce an approach for automatically discovering topics composed of semantically similar user comments based on deep bidirectional natural language processing algorithms. Stakeholders can use our approach without the need to configure critical parameters like the number of clusters. We present our approach and report on a rigorous multiple-step empirical evaluation to assess how cohesive and meaningful the resulting clusters are. Each evaluation step was peer-coded and resulted in inter-coder agreements of up to 98%, giving us high confidence in the approach. We also report a thematic analysis on the topics discovered from tweets in the telecommunication domain.

关键词： data-driven requirements engineering Clustering Social Media Analytics Deep Learning Feedback Mining

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Specifying requirements for data Collection and Analysis in data-driven RE. A Research Preview 27th

Specifying Requirements for Data Collection and Analysis in ...

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27th International Working Conference on requirements engineering - Foundation for Software Quality (REFSQ)

作者： Astegher, Maurizio Busetta, Paolo Perini, Anna Susi, Angelo Delta Informat SpA Trento Italy Fdn Bruno Kessler Trento Italy

ISBN: (纸本)9783030731281;9783030731274

[Context and motivation] According to data-driven requirements engineering (RE), explicit and implicit user feedback can be considered a relevant source of requirements, thus supporting requirements elicitation. [Question/problem] Less attention has been paid so far to the role of implicit feedback in RE tasks, such as requirements validation, and on how to specify what implicit feedback to collect and analyse. [Principal idea/results] We propose an approach that leverages on goal-oriented requirements modelling combined with Goal-Question-Metric. We explore the applicability of the approach on an industrial project in which a platform for online training has been adapted to realise a citizen information service that has been used by hundreds of people during the COVID-19 pandemic. [Contributions] Our contribution is twofold: (i) we present our approach towards a systematic definition of requirements for data collection and analysis, at support of software requirements validation and evolution;(ii) we discuss our ideas using concrete examples from an industrial case study and formulate a research question that will be addressed by conducting experiments as part of our research.

关键词： data-driven requirements engineering User feedback Goal-Question-Metric Goal-Oriented requirements Analysis

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Towards a data-driven requirements Elicitation Tool through the Lens of Design Thinking 23

Towards a Data-Driven Requirements Elicitation Tool through ...

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23rd International Conference on Enterprise Information Systems (ICEIS)

作者： de Souza Filho, Jose Cezar Nakamura, Walter Takashi Teixeira, Ligia Marcia da Silva, Rogenis Pereira Gadelha, Bruno Freitas Conte, Tayana Uchoa Univ Fed Amazonas Inst Comp IComp UFAM Manaus Amazonas Brazil

ISBN: (纸本)9789897585098

data-driven requirements engineering (DDRE) proposes that software requirements development goes beyond the application of traditional elicitation techniques (e.g., interviews and questionnaires) by considering other sources of data, such as user reviews available on app stores, social networks, and forums. While many studies are looking for requirements mining and automatic classification through machine learning, information retrieval, and natural language processing algorithms, few studies investigate how to support software practitioners who will use this knowledge in practice, for instance, through tools to support the process. In this context, Design Thinking (DT) emerged as a promising approach to design user-centered solutions to this problem. Thus, in this paper, we conducted an exploratory study to investigate how DT benefits the development of a data-driven requirements elicitation tool. To do so, we applied the Double Diamond process, having in mind Brown's DT Cycles, supported by a set of DT techniques. Our results indicate that DT techniques can be integrated into the development process, allowing a better understanding of the problem and supporting the development of user-centered solutions. We provide the benefits and drawbacks of adopting DT as a toolbox in the context of DDRE tools.

关键词： data-driven requirements engineering Design Thinking User Reviews

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