Functional grouping of natural language requirements for assistance in architectural software design

Casamayor, Agustin; Godoy, Daniela; Campo, Marcelo

doi:10.1016/j.knosys.2011.12.009

Cited by 35 publications

(16 citation statements)

References 22 publications

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“…Cleland et al achieved high recall with the tradeoff of really low precision. This experiment was reproduced by several researchers in the past [15,16]. Casamayor et al [16] employed multinomial naive Bayes classifier coupled with an Expectation Maximization algorithm.…”

Section: Related Workmentioning

confidence: 99%

Study of Various Classifiers for Identification and Classification of Non-functional Requirements

Tóth

Vidács

2018

Computational Science and Its Applications – ICCSA 2018

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Identification of non-functional requirements in an early phase of software development process is crucial for creating a proper software design. These requirements are often neglected or given in too general forms. However, interviews and other sources of requirements often include important references also to non-functional requirements which are embedded in a bigger textual context. The non-functional requirements have to be extracted from these contexts and should be presented in a formulated and standardized way to support software design. The set of requirements extracted from their textual context have to be classified to formalize them. This task is to be accomplished manually but it can be very demanding and error-prone. Several attempts have been made to support identification and classification tasks using supervised and semisupervised learning processes. These efforts have achieved remarkable results. Researchers were mainly focused on the performance of classification measured by precision and recall. However, creating a tool which can support business analysts with their requirements elicitation tasks, execution time is also an important factor which has to be taken into account. Knowing the performance and the results of benchmarks can help business analysts to choose a proper method for their classification tasks. Our study presented in this article focuses on both the comparison of performances of the classification processes and their execution time to support the choice among the methods.

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Section: Related Workmentioning

confidence: 99%

Study of Various Classifiers for Identification and Classification of Non-functional Requirements

Tóth

Vidács

2018

Computational Science and Its Applications – ICCSA 2018

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“…Limitations of LSA that were mentioned in Hoenkamp (2011) included scalability issues and LSA did not recover optimal semantic factors as it supposed to, resulting in more proposal to enhance the LSA algorithm. To improve similar requirements identification, some studies proposed clustering algorithm used in information retrieval area such as Incremental Diffusive Clustering or IDC (as appeared in , Centroid-based clustering (Davril et al, 2013;Casamayor et al, 2012)), K-means clustering (as appeared again in S9 and S10), integrating Hierarchical Agglomerative Clustering (HAC) in S6 and S4, Latent Dirichlet Allocation (LDA)in S10 and S13, and more.…”

Section: Feature Extraction Approaches Were Done In Phases and Suppormentioning

confidence: 99%

Feature extraction approaches from natural language requirements for reuse in software product lines: A systematic literature review

Bakar

Kasirun

Salleh

2015

Journal of Systems and Software

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“…Casamayor et al [4] proposed an approach for mining and classifying functionality from textual descriptions of requirements using text mining techniques. The experimental evaluation case studies show that technique assists software designers in this complex and time consuming task.…”

Section: Research Overview and Problematicmentioning

confidence: 99%

“…Table 2 Descriptive Statistics V. MEASUREMENT For each exercise, subjects were given the relevant system description document and were asked to draw the use case models. In our experiment, a list of potential mistakes based on the existing use case literature [1], [2], [3], [4] were used to validate all the use case models. Table 4 shows the use case attributes and their respective common mistakes.…”

Section: Subject Slectionmentioning

confidence: 99%

Software Requirements Elicitation -- A Controlled Experiment to Measure the Impact of a Native Natural Language

Mahmood

Ajila

2013

2013 IEEE 37th Annual Computer Software and Applications Conference

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AbstractȄSoftware development is a collaborative activity where the quality of the end product depends to a great extent on the quality of the requirements engineering process. This paper examines the impact of multi-natural language backgrounds (English and Arabic) of the requirements engineers on the quality and correctness of the use case modeling. The study is done through a controlled experiment. The results show that using a native language for system description improves the functional correctness of the use case model. However, the time required to perform use case modeling and the quality of use case diagrams is not affected by using either native or English (as a second language).

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Functional grouping of natural language requirements for assistance in architectural software design

Cited by 35 publications

References 22 publications

Study of Various Classifiers for Identification and Classification of Non-functional Requirements

Study of Various Classifiers for Identification and Classification of Non-functional Requirements

Feature extraction approaches from natural language requirements for reuse in software product lines: A systematic literature review

Software Requirements Elicitation -- A Controlled Experiment to Measure the Impact of a Native Natural Language

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