The Applications of Natural Language Processing (NLP) for Software Requirement Engineering - A Systematic Literature Review

Nazir, Farhana; Butt, Wasi Haider; Anwar, Muhammad Waseem; Khattak, Muazzam A. Khan

doi:10.1007/978-981-10-4154-9_56

Cited by 41 publications

(30 citation statements)

References 29 publications

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“…Developing an appropriate search strategy in SLR is quite momentous that lay down a base for the conduction of the subsequent phases . To design an effective search strategy, we have followed the SLR guidelines, as used by other researchers …”

Section: Methodsmentioning

confidence: 99%

Using agile methods for the development of green and sustainable software: Success factors for GSD vendors

Rashid

Khan

2017

J Software Evolu Process

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Over the last decade, the use of agile methods has grown dramatically for software development.Agile methods guarantee to accelerate the delivery of remarkable software with increased user satisfaction and reduced cost. However, in recent years, due to the emergence of green software engineering, software developers are compelled to focus more on green and sustainable aspects of software. Green software engineering aims to design, develop, and use the software with confined energy and computing resources. Recently, software engineers in global software development have adapted agile methods for quick, interactive, and environment-friendly software development.In this study, we have identified 16 success factors, through systematic literature review (SLR) and applied contrived search criteria derived from the research questions; 80 relevant papers were identified and reviewed. Findings of the SLR study were then empirically validated through questionnaire survey in global software development industry, in which 106 experts from 25 different countries participated.The findings of our industrial survey are mostly in coherence with the SLR findings. However, there is a difference in ranks of the various success factors across the 2 data sets (SLR and industrial survey). KEYWORDSagile software development, empirical study, green and sustainable software, GSD vendor, success factors, systematic literature review (SLR) | INRODUCTIONAgile software development is an invigorating approach towards quick and interactive software development. It provides a conceptual framework for undertaking a software project that is colocated or globally distributed. Unlike traditional methods of software development, agile methods attempt to scale down the risks and maximize software productivity by developing software in short iterations. 1,2 These methods rely on team members' skills and informal communication rather than formal and hefty documentations. 3 Thus, agile methods pursue to avoid overwhelming processes, having little contribution in actual software development.Agile methods are strongly linked to the history of incremental and iterative development. The concept of agile methods were coined in 2001 4 by a group of agile software developers, interested in modern, simple incremental and iterative development methods, who constituted the Agile Alliance (http://www.agilealliance.org). The establishment of the Agile Alliance provided a base for the formal publications of the agile manifesto that contributed agile methods to software engineering domain. The agile manifesto brought an industry-led vision for a reflective change in software engineering paradigm. These methods focus on customer satisfaction through early and consistent delivery of quality software, promotion of sustainable development, simple and polymorphic design, flexibility towards change, and rich communication and collaborations among the agile team members and customers. 5,6 The agile manifesto represents a quite innovative work in cleaving and outspreading ...

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Section: Methodsmentioning

confidence: 99%

Using agile methods for the development of green and sustainable software: Success factors for GSD vendors

Rashid

Khan

2017

J Software Evolu Process

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“…A common practice is using humans to classify them into FRs and NFRs since in most cases they are written in natural language. Instead, although a complex task for requirement engineering, we can use ML techniques such as NLP [37 ]. In this work, we propose a Requirement Classification module (see Fig.…”

Section: Proposal Of Interaction Design Patterns Recommendation Basmentioning

confidence: 99%

Classifying design‐level requirements using machine learning for a recommender of interaction design patterns

Silva-Rodríguez

Nava-Muñoz

Castro³

et al. 2020

IET softw.

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Software Engineering is a discipline that encompasses processes associated with the development of interactive systems. The perceived quality of an interactive system is heavily influenced by the user interface design, which may result in many challenges. One such challenge is design‐level requirements analysis. The success of the software system is mostly dependent on how well users’ requirements have been understood and translated into appropriate functionalities. During the interactive system design process, it is common to find recurring problems in human–computer interactions, for which reusing solutions is highly feasible. Interaction design patterns seek to support designers in decision making during the design of interactive systems. Due to the design task tends to be subjective and prone to errors. This work aims at presenting and evaluating an interaction design patterns recommendation model based on design‐level requirements classification, through the application of supervised machine learning algorithms. To compare the performance of four classification algorithms, a study was carried out, in which the linear support vector machine was the most suitable to this problem. The results of this work can be used for implementing frameworks that can better support designers’ decision making when designing user interfaces.

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“…NLP can be used to extract ontologies from requirements specifications and verify the consistency or completeness of a requirement specification. NLP techniques help classify and prioritize the requirements [12].…”

Section: Natural Language Artefacts In Software Repositoriesmentioning

confidence: 99%

“…Even though NLP is an utmost important part of mining to get the good results, NLP alone cannot perform all the tasks. Information retrieval techniques [11,99] and other techniques like ML need to be used to get good results. From the survey, it has been found that many of the authors have used either ML or information retrieval techniques along with NLP techniques [15,41,73,74,87] in their works to get the desired output or to overcome the shortcomings of NLP.…”

Section: Stengths and Limitations Of Nlp In Msrmentioning

confidence: 99%

Natural language processing in mining unstructured data from software repositories: a review

Gupta

2019

Sādhanā

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With the increasing popularity of open-source platforms, software data is easily available from various open-source tools like GitHub, CVS, SVN, etc. More than 80 percent of the data present in them is unstructured. Mining data from these repositories helps project managers, developers and businesses, in getting interesting insights. Most of the software artefacts present in these repositories are in the natural language form, which makes natural language processing (NLP) an important part of mining to get the useful results. The paper reviews the application of NLP techniques in the field of Mining Software Repositories (MSR). The paper mainly focuses on sentiment analysis, summarization, traceability, norms mining and mobile analytics. The paper presents the major NLP works performed in this area by surveying the research papers from 2000 to 2018. The paper firstly describes the major artefacts present in the software repositories where the NLP techniques have been applied. Next, the paper presents some popular open-source NLP tools that have been used to perform NLP tasks. Later the paper discusses, in brief, the research state of NLP in MSR field. The paper also lists down the various challenges along with the pointers for future work in this field of research and finally the conclusion.

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The Applications of Natural Language Processing (NLP) for Software Requirement Engineering - A Systematic Literature Review

Cited by 41 publications

References 29 publications

Using agile methods for the development of green and sustainable software: Success factors for GSD vendors

Using agile methods for the development of green and sustainable software: Success factors for GSD vendors

Classifying design‐level requirements using machine learning for a recommender of interaction design patterns

Natural language processing in mining unstructured data from software repositories: a review

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