Identification of non-functional requirements in textual specifications: A semi-supervised learning approach

Casamayor, Agustin; Godoy, Daniela; Campo, Marcelo

doi:10.1016/j.infsof.2009.10.010

Cited by 137 publications

(106 citation statements)

References 38 publications

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“…Casamayor et al (2010) proposed a semisupervised text categorization technique for identifying NFRs from a textual document, the supervised text categorization technique proposed earlier lot of pre categorize requirements are required to train a classifier before finding an accurate NFRs, with supervised technique it required manually categorization of numerous requirements by the analyst. This study has tried to automate this process.…”

Section: Literature Reviewmentioning

confidence: 99%

A review on automatic extraction and classification of non-functional requirements

Khan¹,

Khan

2017

Int. j. adv. appl. sci

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Extraction and classification of NFRs plays a vital role in the software development process. NFRs are often misunderstood and ignored due to which certain important aspect of the software such as robustness availability usability performance and security are compromised. Several different extraction and classification techniques proposed in the literature. In this study, we have made an effort to critically analyze different NFRs extraction and classification techniques in order to see their merits and demerits, as well as the scenarios in which these techniques can be helpful. Specifically we have identified the limitations of these techniques, their significance and the possible improvements that can be made to make these techniques more useful. In this paper, we have critically evaluated various techniques and methods proposed in the literature on the automatic extraction and classification of NFRs. The findings of the existing techniques are provided in the paper. The critical analysis tables are provided in this study to highlight the significance of NFRs extraction and classification technique.

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Section: Literature Reviewmentioning

confidence: 99%

A review on automatic extraction and classification of non-functional requirements

Khan¹,

Khan

2017

Int. j. adv. appl. sci

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“…Casamayor, Godoy and Campo (2009) [11] developed a semi-supervised text categorization approach for the automatic identification and classification of non-functional requirements. The goal of the approach is the integration into a recommender system that will assist software engineers in the development of the system.…”

Section: A Requirements Elicitationmentioning

confidence: 99%

Functional Requirements Identification Using Item-to-Item Collaborative Filtering

Hidalgo¹,

Fernandez²

2015

IJIET

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Abstract-One of the most difficult tasks in the development of software is the identification of the functional requirements. A well-defined functional requirement will eventually map the success of a software project. A support tool that can recommend candidate functional requirements for a software project being developed will help software engineers to deliver the right software to the clients.The purpose of this study is to determine whether a collection of previously developed software applications can serve as basis for the development of a model to identify functional requirements of a project to be developed. Completed software project documentations of Master in Information Technology graduates from Higher Education Institutions in northern Luzon were collected and categorized, and projects on Faculty Evaluation Systems were analyzed in order to extract the vectors of functional requirements for the target software domain. Item-to-item collaborative filtering is then applied to these vectors to form the recommender model. Finally, the model was validated using the leave-one-out cross-validation technique. The resulting high accuracy rate indicates that the model built using the item-to-item collaborative filter may be used to build a tool that can automatically recommend important related functional requirements given one or more requirements identified by any of the project stakeholders.

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“…There are many methods developed to do classification process both for documents in general and requirement documents to identify non-functional requirement of software, consisting of the formation of NFR Locator system whose classification uses KNN and makes use of the function of distance Levenshtein [12], Naïve Bayes with some developments [6] [13] [14], using SVM method with the renewal in feature selection phase [15], the usage of TF-IDF weighting and measurement of similarity degree of cosine measure [8]. Basically, the process of classification consists of single-label and multi-label, some method developments mentioned above are still limited in single-label, whereas in fact, there is a possibility that one option of requirement sentence in requirement document contains more than one aspect of nonfunctional requirement (multi-label) [7] [16].…”

Section: Introductionmentioning

confidence: 99%

Classification of Non-Functional Requirements Using Semantic-FSKNN Based ISO/IEC 9126

2015

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Identification of non-functional requirements in textual specifications: A semi-supervised learning approach

Cited by 137 publications

References 38 publications

A review on automatic extraction and classification of non-functional requirements

A review on automatic extraction and classification of non-functional requirements

Functional Requirements Identification Using Item-to-Item Collaborative Filtering

Classification of Non-Functional Requirements Using Semantic-FSKNN Based ISO/IEC 9126

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