Using Linguistic Knowledge to Classify Non-functional Requirements in SRS documents

Hussain, Ishrar; Kosseim, Leila; Ormandjieva, Olga

doi:10.1007/978-3-540-69858-6_28

Cited by 42 publications

(44 citation statements)

References 8 publications

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“…to each word in each requirement. The POS tags 2 are necessary to perform the FR/NFR classification based on the approach of Hussain et al [6].…”

Section: A Part Of Speech (Pos) Taggingmentioning

confidence: 99%

“…For the classification of functional and non-functional requirements, we used the approach of Hussain et al [6]. We applied this approach to the unprocessed data set of requirements as well as on the processed one resulting from our preprocessing.…”

Section: A Rq1-influence Of Grammatical Temporal and Sentimental Sementioning

confidence: 99%

“…For each feature, we determine its rank based on the feature's probability of occurrence in the requirements of the data set. According to Hussain et al [6], we selected a cutoff threshold of > 0.8. Therefore, we determined number of cardinals and number of degree of adjectives/adverbs as valid features among all five ones for the unprocessed data set.…”

Section: A Rq1-influence Of Grammatical Temporal and Sentimental Sementioning

confidence: 99%

“…We make two contributions. (1) We investigate if and to which extent an existing decision tree learning algorithm [6] for classifying requirements into FRs and NFRs can be improved by preprocessing the requirements with a set of rules for (automated) standardizing and normalizing the requirements found in a requirements specification. (2) We study how well several existing machine learning methods perform for automated classification of NFRs into sub-categories such as availability, security, or usability.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

What Works Better? A Study of Classifying Requirements

Abad¹,

Karras

Ghazi

et al. 2017

2017 IEEE 25th International Requirements Engineering Conference (RE)

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Abstract-Classifying requirements into functional requirements (FR) and non-functional ones (NFR) is an important task in requirements engineering. However, automated classification of requirements written in natural language is not straightforward, due to the variability of natural language and the absence of a controlled vocabulary. This paper investigates how automated classification of requirements into FR and NFR can be improved and how well several machine learning approaches work in this context. We contribute an approach for preprocessing requirements that standardizes and normalizes requirements before applying classification algorithms. Further, we report on how well several existing machine learning methods perform for automated classification of NFRs into sub-categories such as usability, availability, or performance. Our study is performed on 625 requirements provided by the OpenScience tera-PROMISE repository. We found that our preprocessing improved the performance of an existing classification method. We further found significant differences in the performance of approaches such as Latent Dirichlet Allocation, Biterm Topic Modeling, or Naïve Bayes for the sub-classification of NFRs.

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“…to each word in each requirement. The POS tags 2 are necessary to perform the FR/NFR classification based on the approach of Hussain et al [6].…”

Section: A Part Of Speech (Pos) Taggingmentioning

confidence: 99%

Section: A Rq1-influence Of Grammatical Temporal and Sentimental Sementioning

confidence: 99%

Section: A Rq1-influence Of Grammatical Temporal and Sentimental Sementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

What Works Better? A Study of Classifying Requirements

Abad¹,

Karras

Ghazi

et al. 2017

2017 IEEE 25th International Requirements Engineering Conference (RE)

View full text Add to dashboard Cite

show abstract

“…dictionaries and handbooks. In addition, either a formal linguistic analysis implemented with the syntactic meta-language BackusNaur Form (BNF) [16] or a supervised machine learning decision tree [23] can be implemented to infer another implicit attribute that categorises the statements as functional or non-functional. These attributes coupled with the Apache Lucene 20 search engine, and the visualisation of semantic relationships (synonyms, hypernyms, hyponyms, etc.)…”

Section: Implementation Of the Framework -"How"mentioning

confidence: 99%

A Collaborative Requirement Mining Framework to Support OEMs

Pinquié

Véron

Segonds

et al. 2015

Lecture Notes in Computer Science

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is an open access repository that collects the work of Arts et Métiers ParisTech researchers and makes it freely available over the web where possible. Abstract. With the fastidiously ever-increasing complexity of systems, the relentless, massive customisation of products and the mushrooming accumulation of legal documents (standards, policies and laws), we can observe a significant increase in requirements. We consider the tremendous volume of requirements as big data with which companies struggle to make strategic decisions early on. This paper proposes a collaborative requirement mining framework to enable the decision-makers of an Original Equipment Manufacturer (OEM) to gain insight and discover opportunities in a massive set of requirements so as to make early effective strategic decisions. The framework supports OEMs willing to uncover a subset of key requirements by distilling large unstructured and semi-structured specifications.

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SABDM: A self‐attention based bidirectional‐RNN deep model for requirements classification

Kaur

2022

J Software Evolu Process

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The success of software depends upon functional and non‐functional requirements as both requirements are equally important in software development. However, the requirements engineering community still lacks in comprehensive understanding of functional and non‐functional requirements. In addition, the requirements in software documents are expressed in natural language and also intertwined with each other. Requirements classification is a crucial task that correctly extracts functional and non‐functional requirements and organizes them in specified categories. Automated classification of software requirements leads to reduced ambiguity, misunderstanding, and development cost. Most of the recent studies have used machine learning and deep learning techniques for automatic classification of requirements. However, there is one drawback of such techniques, that is, poor generalization. To address these problems, this research work proposes self‐attention based bidirectional LSTM deep model. This automated approach has used recurrent neural network, which handle long sequential natural language requirements statements and classify them into five classes such as capability, maintainability, performance, security, and usability. The proposed approach train and evaluate over pre‐labeled dataset comprised of 34 industrial requirements specifications and PROMISE dataset. Over this dataset, the proposed approach yields 95% of precision, 96% of recall, 96% of F‐measure, and 96% of accuracy. The proposed approach can be applied to wide variety of datasets with different domain. Furthermore, this paper applies pre‐processing techniques to improve the performance of the requirements classification model. The results of the proposed model are compared with existing baseline state‐of‐art techniques, and it is shown that the proposed model outperforms the baseline models in requirements classification.

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Using Linguistic Knowledge to Classify Non-functional Requirements in SRS documents

Cited by 42 publications

References 8 publications

What Works Better? A Study of Classifying Requirements

What Works Better? A Study of Classifying Requirements

A Collaborative Requirement Mining Framework to Support OEMs

SABDM: A self‐attention based bidirectional‐RNN deep model for requirements classification

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