Text Classification and Machine Learning Support for Requirements Analysis Using Blogs

Lange, Douglas S.

doi:10.1007/978-3-540-89778-1_14

Cited by 4 publications

(4 citation statements)

References 3 publications

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“…Some problems that have been explored in detail include identifying goals in stakeholder dialogs [10], support for dealing with requirements changes [1,8], using shallow natural language processing techniques to aid in synthesizing requirements [13,11], and requirements validation [6].…”

Section: Synthesis Of Workhop Discussionmentioning

confidence: 99%

Advances in Requirements Engineering: Bridging the Gap between Stakeholders’ Needs and Formal Designs

Luqi

Kordon

2008

Lecture Notes in Computer Science

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Abstract. The lions's share of the software faults can be traced to requirements and specification errors, so improvements in requirements engineering can have a large impact on the effectiveness of the overall system development process. A weak link in the chain is the transition from the vague and informal needs of system stakeholders to the formal models that support theoretical analysis and software tools. This paper explains the context for the 2007 Monterey workshop that was dedicated to this problem. It provides the case study that participants were asked to use to illustrate their new methods, and summarizes the discussion and conclusions of the workshop.

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Section: Synthesis Of Workhop Discussionmentioning

confidence: 99%

Advances in Requirements Engineering: Bridging the Gap between Stakeholders’ Needs and Formal Designs

Luqi

Kordon

2008

Lecture Notes in Computer Science

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“…About half of the included works applied available data from user-generated content (UGC) to aid requirement elicitation. The UGC includes textual data from e-commerce websites (n = 16), mobile app marketplace (n = 13), microblogs and social media (n = 5), organizational internal forum (Lange, 2008), GitHub repository (Nyamawe et al, 2019), and crowdsourcing platforms (Jones and Kim, 2015).…”

Section: The Data Source For Ml-based Requirement Elicitationmentioning

confidence: 99%

Machine learning in requirements elicitation: a literature review

Cheligeer

Huang

et al. 2022

AIEDAM

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A growing trend in requirements elicitation is the use of machine learning (ML) techniques to automate the cumbersome requirement handling process. This literature review summarizes and analyzes studies that incorporate ML and natural language processing (NLP) into demand elicitation. We answer the following research questions: (1) What requirement elicitation activities are supported by ML? (2) What data sources are used to build ML-based requirement solutions? (3) What technologies, algorithms, and tools are used to build ML-based requirement elicitation? (4) How to construct an ML-based requirements elicitation method? (5) What are the available tools to support ML-based requirements elicitation methodology? Keywords derived from these research questions led to 975 records initially retrieved from 7 scientific search engines. Finally, 86 articles were selected for inclusion in the review. As the primary research finding, we identified 15 ML-based requirement elicitation tasks and classified them into four categories. Twelve different data sources for building a data-driven model are identified and classified in this literature review. In addition, we categorized the techniques for constructing ML-based requirement elicitation methods into five parts, which are Data Cleansing and Preprocessing, Textual Feature Extraction, Learning, Evaluation, and Tools. More specifically, 3 categories of preprocessing methods, 3 different feature extraction strategies, 12 different families of learning methods, 2 different evaluation strategies, and various off-the-shelf publicly available tools were identified. Furthermore, we discussed the limitations of the current studies and proposed eight potential directions for future research.

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“…Lange et al [31] used an ML-based software requirement elicitation process while extending an existing military tool called skiweb. Different users posted and updated military events and information using this tool.…”

Section: A Requirements Elicitation and Discoverymentioning

confidence: 99%

A Bird's Eye View on Requirements Engineering and Machine Learning

Iqbal

Elahidoost

Levi

2018

2018 25th Asia-Pacific Software Engineering Conference (APSEC)

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Machine learning (ML) has demonstrated practical impact in a variety of application domains. Software engineering is a fertile domain where ML is helping in automating different tasks. In this paper, our focus is the intersection of software requirement engineering (RE) and ML. To obtain an overview of how ML is helping RE and the research trends in this area, we have surveyed a large number of research articles. We found that the impact of ML can be observed in requirement elicitation, analysis and specification, validation and management. Furthermore, in these categories, we discuss the specific problem solved by ML, the features and ML algorithms used as well as datasets, when available. We outline lessons learned and envision possible future directions for the domain.

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Text Classification and Machine Learning Support for Requirements Analysis Using Blogs

Cited by 4 publications

References 3 publications

Advances in Requirements Engineering: Bridging the Gap between Stakeholders’ Needs and Formal Designs

Advances in Requirements Engineering: Bridging the Gap between Stakeholders’ Needs and Formal Designs

Machine learning in requirements elicitation: a literature review

A Bird's Eye View on Requirements Engineering and Machine Learning

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