A methodology for the classification of quality of requirements using machine learning techniques

Parra, Eugenio; Dimou, Christos; Lloréns, Juan; Moreno, Valentín; Fraga, Anabel

doi:10.1016/j.infsof.2015.07.006

Cited by 44 publications

(38 citation statements)

References 34 publications

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“…In the case study presented by Kaindl, 43 even if the word ontology is not mentioned, the author uses a “domain model” as a way to define concepts appearing in the requirements. It is possible to train ontologies directly with requirements documents instead of general texts on the domain 44,45 . Fraga et al 46 propose to generate templates from ontologies, in order to provide more flexibility.…”

Section: Related Workmentioning

confidence: 99%

Toward a better integration of requirements and model‐based specifications

Lebeaupin

Rauzy

2020

Systems Engineering

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As of today, most specifications of technical systems still rely on requirements written in natural language. However, this approach is known to be problem‐prone, due to the inherent ambiguity of natural languages. On the other hand, fully formal or model‐based approaches seem to be out of reach in many practical cases, especially in early design phases of systems. In this article, we study how to combine in a pragmatic way natural language requirements with models. We propose to keep both formats and to link pieces of text in requirements with elements of models. In other words, corpuses of requirements are managed as hypertexts with links to models. For this approach to be fully efficient, the text of requirements is not free, but relies on controlled natural language techniques leading to a partial structuring of the text. We show that this makes it possible to design (semi)automatic verifications on requirements and models, which would be impossible with unconstrained natural language. We illustrate here our approach on a small illustrative example and we report results obtained on a full size industrial application.

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Section: Related Workmentioning

confidence: 99%

Toward a better integration of requirements and model‐based specifications

Lebeaupin

Rauzy

2020

Systems Engineering

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“…Quality attributes: In a software system, functional requirements describe the intended actions of the system, while non-functional requirements define the overall behavior and constraints of the system. Early identification of non-functional requirements can reduce software development cost and time while boosting user satisfaction [29,30]. We classified all those opinions into quality attributes which tell us about software constraints, hardware constraints or talk about quality characteristics and sub-characteristics defined by ISO 9126 [31].…”

Section: B Classificationmentioning

confidence: 99%

Your Opinions Let us Know: Mining Social Network Sites to Evolve Software Product Lines

Ali¹,

Hwang²,

Hong³

2019

KSII TIIS

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Software product lines (SPLs) are complex software systems by nature due to their common reference architecture and interdependencies. Therefore, any form of evolution can lead to a more complex situation than a single system. On the other hand, software product lines are developed keeping long-term perspectives in mind, which are expected to have a considerable lifespan and a long-term investment. SPL development organizations need to consider software evolution in a systematic way due to their complexity and size. Addressing new user requirements over time is one of the most crucial factors in the successful implementation SPL. Thus, the addition of new requirements or the rapid context change is common in SPL products. To cope with rapid change several researchers have discussed the evolution of software product lines. However, for the evolution of an SPL, the literature did not present a systematic process that would define activities in such a way that would lead to the rapid evolution of software. Our study aims to provide a requirements-driven process that speeds up the requirements engineering process using social network sites in order to achieve rapid software evolution. We used classification, topic modeling, and sentiment extraction to elicit user requirements. Lastly, we conducted a case study on the smartwatch domain to validate our proposed approach. Our results show that users' opinions can contain useful information which can be used by software SPL organizations to evolve their products. Furthermore, our investigation results demonstrate that machine learning algorithms have the capacity to identify relevant information automatically.

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“…Normally software requirements are of two types, namely, FRs and NFRs. e research work on the difference between FRs and NFRs is defined and well known; however, the automatic identification and classification of the software requirements stated in different natural language is still a huge challenge [14][15][16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

A Systematic Literature Review on Using Machine Learning Algorithms for Software Requirements Identification on Stack Overflow

Ahmad

Feng

Khan

et al. 2020

Security and Communication Networks

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Context. The improvements made in the last couple of decades in the requirements engineering (RE) processes and methods have witnessed a rapid rise in effectively using diverse machine learning (ML) techniques to resolve several multifaceted RE issues. One such challenging issue is the effective identification and classification of the software requirements on Stack Overflow (SO) for building quality systems. The appropriateness of ML-based techniques to tackle this issue has revealed quite substantial results, much effective than those produced by the usual available natural language processing (NLP) techniques. Nonetheless, a complete, systematic, and detailed comprehension of these ML based techniques is considerably scarce. Objective. To identify or recognize and classify the kinds of ML algorithms used for software requirements identification primarily on SO. Method. This paper reports a systematic literature review (SLR) collecting empirical evidence published up to May 2020. Results. This SLR study found 2,484 published papers related to RE and SO. The data extraction process of the SLR showed that (1) Latent Dirichlet Allocation (LDA) topic modeling is among the widely used ML algorithm in the selected studies and (2) precision and recall are amongst the most commonly utilized evaluation methods for measuring the performance of these ML algorithms. Conclusion. Our SLR study revealed that while ML algorithms have phenomenal capabilities of identifying the software requirements on SO, they still are confronted with various open problems/issues that will eventually limit their practical applications and performances. Our SLR study calls for the need of close collaboration venture between the RE and ML communities/researchers to handle the open issues confronted in the development of some real world machine learning-based quality systems.

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A methodology for the classification of quality of requirements using machine learning techniques

Cited by 44 publications

References 34 publications

Toward a better integration of requirements and model‐based specifications

Toward a better integration of requirements and model‐based specifications

Your Opinions Let us Know: Mining Social Network Sites to Evolve Software Product Lines

A Systematic Literature Review on Using Machine Learning Algorithms for Software Requirements Identification on Stack Overflow

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