Automated cloud service based quality requirement classification for software requirement specification

Merugu, R Raja Ramesh; Chinnam, Satyananda Reddy

doi:10.1007/s12065-019-00241-6

Cited by 3 publications

(3 citation statements)

References 13 publications

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“…The quality of software depends upon the quality of requirement specification. 28 In research on the software requirements engineering, lot of work has been done on proposing techniques for detecting ambiguity, inconsistency, and prioritizing software requirements during requirements analysis. [29][30][31][32][33][34] Some research work provided empirical evidence for requirements classification using semi-automated techniques.…”

Section: Requirements Classificationmentioning

confidence: 99%

“…Most traditional requirements classification techniques have been used for assisting developers in requirements analysis. The quality of software depends upon the quality of requirement specification 28 . In research on the software requirements engineering, lot of work has been done on proposing techniques for detecting ambiguity, inconsistency, and prioritizing software requirements during requirements analysis 29–34 .…”

Section: Related Workmentioning

confidence: 99%

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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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Section: Requirements Classificationmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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

Kaur

2022

J Software Evolu Process

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“…Some tools used natural language processing to automatically extract feedback into formal documents References [12,13,20,25]. Other tools classified requirements using machine learning [15]. According to [26], requirements validation is the process of eliminating ambiguities and inconsistencies in the SRS.…”

Section: Rq2 At Which Stages Of Requirements Engineering Are the Tool...mentioning

confidence: 99%

A Review and Analysis of Tools Used from 2018 till 2022 in Requirements Engineering

Yumun¹,

Embi²

2022

Proceedings of the International Conference on Computer, Information Technology and Intelligent Computing (CITIC 2022)

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The commonly known stages in Requirements Engineering (RE) include planning, elicitation, documentation, validation, and management. Each stage consists of multiple processes and activities between stakeholders that result in data collection and analysis. Activities and data can be collected/extracted/analysed by manual or automated software tools to streamline the requirements engineering process. The objective of this review is to collate and study the distribution of tools in each stage and to discover missing gaps (if any) in tools for each stage. An electronic search was conducted on four publication libraries to compile all tools introduced in the requirements engineering field from 2018 to April 2022. 33 publications were found, and data extraction was performed on them to classify the tools to discover common patterns and methodologies. The results from data analysis showed that most new tools focused on elicitation and validation stages of RE. Planning and documentation stages had the least number of new tools. No new methodologies for RE tool creation were introduced. NLP remained the popular approaches among researchers. 21 out of 33 publications did not present case studies to prove the practical applications of the tools they introduced. This review has aggregated knowledge in the tools used in different RE stages.

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A Review of Artificial Intelligence Techniques for Requirement Engineering

Kaur

Singh

Kaur

2020

Computational Methods and Data Engineering

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Automated cloud service based quality requirement classification for software requirement specification

Cited by 3 publications

References 13 publications

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

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

A Review and Analysis of Tools Used from 2018 till 2022 in Requirements Engineering

A Review of Artificial Intelligence Techniques for Requirement Engineering

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