From User Demand to Software Service: Using Machine Learning to Automate the Requirements Specification Process

Rooijen, Lorijn van; Bäumer, Frederik Simon; Platenius, Marie Christin; Geierhos, Michaela; Hamann, Heiko; Engels, Gregor

doi:10.1109/rew.2017.26

Cited by 7 publications

(4 citation statements)

References 16 publications

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“…By crowd [48,54,72], by textual data analysis [13,20,25,33,41,42,49,51,52,62,64,80,86,88,89], by prototyping [22], sentiment analysis [21,79], image and unstructured data analysis [21,73] 22…”

Section: Analysis and Validationmentioning

confidence: 99%

“…are typically used by developers and stakeholders to better understand and communicate about the requirements [27,12,1,59]. Requirements modeling is considered as challenging for massive crowds, it is only possible to build collaborative modelling tools for small or medium sized groups [27], or competition platforms for the crowd to bid for an award for best requirements specifications [1,64]. For example, Almaliki et al [3] suggested clustering the crowd and their different styles of input, the crowd is being modelled linked to feedback acquisition process by a model-driven process.…”

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confidence: 99%

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Crowd Intelligence in Requirements Engineering: Current Status and Future Directions

Khan

Liu

Wen

et al. 2019

Lecture Notes in Computer Science

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Software systems are the joint creative products of multiple stakeholders, including both designers and users, based on their perception, knowledge and personal preferences of the application context. The rapid rise in the use of Internet, mobile and social media applications make it even more possible to provide channels to link a large pool of highly diversified and physically distributed designers and end users, the crowd. Converging the knowledge of designers and end users in requirements engineering process is essential for the success of software systems. In this paper, we report the findings of a survey of the literature on crowd-based requirements engineering research. It helps us understand the current research achievements, the areas of concentration, and how requirements related activities can be enhanced by crowd intelligence. Based on the survey, we propose a general research map and suggest the possible future roles of crowd intelligence in requirements engineering.

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Section: Analysis and Validationmentioning

confidence: 99%

mentioning

confidence: 99%

Crowd Intelligence in Requirements Engineering: Current Status and Future Directions

Khan

Liu

Wen

et al. 2019

Lecture Notes in Computer Science

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“…The important thing is that the found studies are a good representation of the population, which we ensured in this study by adopting a rigorous paper selection process. All Stages Approach/Technique/Method RNN [63] All Stages Other Other [64] All Stages Other Other [56] All Stages Comparative Analysis Other [57] All Stages Other Other [69] All Stages Other RNN, RBM [14] All Stages Model/Framework Other [67] All Stages Tool RF [49] All Stages Other Other [62] All Stages Tool NLP [61] All Stages Other DT [48] All Stages Other Other [65] All Stages Other Other [1] All Stages Other Other [60] All Stages Other Other [68] All Stages Comparative Analysis LR, SVM, NB [66] All Stages Approach/Technique/Method LSTM [11] All Stages Other Other [59] All Stages Other Other [21] Requirements Approach/Technique/Method NB, KNN, RF [70] Requirements Approach/Technique/Method SVM, SMO, NB [82] Requirements Approach/Technique/Method PN [75] Requirements Model/Framework ProbPoly [71] Requirements Approach/Technique/Method Text2Model [84] Requirements Approach/Technique/Method RF [72] Requirements Approach/Technique/Method Other [22] Requirements Approach/Technique/Method NB, RF, LR, SGD, DT [23] Requirements Approach/Technique/Method Boosting [81] Requirements Approach/Technique/Method NSGA-II algorithm [24] Requirements Model/Framework CNN [73] Requirements Approach/Technique/Method FL [76] Requirements Approach/Technique/Method LP, SMO, NB, KNN [86] Requirements Approach/Technique/Method J48, FSS, CFS [25] Requirements Model/Framework RNN [85] Requirements Model/Framework KNN [...…”

Section: A External Validitymentioning

confidence: 99%

A Literature Review of Using Machine Learning in Software Development Life Cycle Stages

Shafiq¹,

Mashkoor

Mayr-Dorn

et al. 2021

IEEE Access

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The software engineering community is rapidly adopting machine learning for transitioning modern-day software towards highly intelligent and self-learning systems. However, the software engineering community is still discovering new ways how machine learning can offer help for various software development life cycle stages. In this article, we present a study on the use of machine learning across various software development life cycle stages. The overall aim of this article is to investigate the relationship between software development life cycle stages, and machine learning tools, techniques, and types. We attempt a holistic investigation in part to answer the question of whether machine learning favors certain stages and/or certain techniques.

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“…Fig. 10 shows the articles by the [3,113,133,156,165,174] Requirement Traceability [47,73,130,139,203,222] Architecture and Design Design Modeling [2,37,46,56,63,135,136,142,146,181,190,192,199,221,226…”

Section: Q13 ML Type and Techniquesmentioning

confidence: 99%

Machine Learning for Software Engineering: A Systematic Mapping

Shafiq,

Mashkoor,

Mayr-Dorn

et al. 2020

Preprint

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The software development industry is rapidly adopting machine learning for transitioning modern day software systems towards highly intelligent and self-learning systems. However, the full potential of machine learning for improving the software engineering life cycle itself is yet to be discovered, i.e., up to what extent machine learning can help reducing the effort/complexity of software engineering and improving the quality of resulting software systems. To date, no comprehensive study exists that explores the current state-of-the-art on the adoption of machine learning across software engineering life cycle stages. Objective: This article addresses the aforementioned problem and aims to present a state-of-the-art on the growing number of uses of machine learning in software engineering. Method: We conduct a systematic mapping study on applications of machine learning to software engineering following the standard guidelines and principles of empirical software engineering.Results: This study introduces a machine learning for software engineering (MLSE) taxonomy classifying the state-of-the-art machine learning techniques according to their applicability to various software engineering life cycle stages. Overall, 227 articles were rigorously selected and analyzed as a result of this study. Conclusion: From the selected articles, we explore a variety of aspects that should be helpful to academics and practitioners alike in understanding the potential of adopting machine learning techniques during software engineering projects.

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From User Demand to Software Service: Using Machine Learning to Automate the Requirements Specification Process

Cited by 7 publications

References 16 publications

Crowd Intelligence in Requirements Engineering: Current Status and Future Directions

Crowd Intelligence in Requirements Engineering: Current Status and Future Directions

A Literature Review of Using Machine Learning in Software Development Life Cycle Stages

Machine Learning for Software Engineering: A Systematic Mapping

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