Requirements elicitation techniques: a systematic literature review based on the maturity of the techniques

Pacheco, Carla; García, Iván A.; Reyes, M.

doi:10.1049/iet-sen.2017.0144

Cited by 105 publications

(72 citation statements)

References 147 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Our results reveal that workshops, interviews, and prototypes are among the most commonly used elicitation techniques, which supports the findings of Wagner et al [34]. It is also in line with Pacheco, García, & Reyes' work [35], which reports these techniques as demonstrating effectiveness across many studies.…”

Section: Discussionsupporting

confidence: 92%

Untitled

2019

IJSEA

View full text Add to dashboard Cite

This paper explores the requirements engineering (RE) process by conducting interviews with RE professionals and applying grounded theory to determine whether a theory of RE emerges. Analysis of the interviews revealed prominent data patterns that were used to model the RE process. The model depicts the RE process as one of establishing a match through discovery and streamlining, and which utilizes corrective measures in order to manage threats to establishing a match. The process involves many entities but is mainly conducted by RE professionals whose experience plays a major role in extracting complete requirements and detecting occasions of mismatch between customer needs and the software requirements, which represent their main concern during the process.This paper contributes to the empirical analysis of RE by presenting evidence of the RE process in its basic form as carried out in industry, which may form as a building block for further RE research.

show abstract

Section: Discussionsupporting

confidence: 92%

Untitled

2019

IJSEA

View full text Add to dashboard Cite

show abstract

“…During the development process, requirements engineering must elicit the stakeholder’s requirements, document the requirements in a suitable manner, validate and verify the requirements, and manage the requirements over the course of the entire life cycle of the system [ 16 ]. Requirements elicitation is a critical activity that forms part of the requirements engineering process because it has to discover what the software must do through a solid understanding of the wishes and needs of the various stakeholders and to transform them into software requirements [ 17 , 18 ].…”

Section: Background and Related Workmentioning

confidence: 99%

Software Requirements Classification Using Machine Learning Algorithms

Canedo

Mendes

2020

Entropy

View full text Add to dashboard Cite

The correct classification of requirements has become an essential task within software engineering. This study shows a comparison among the text feature extraction techniques, and machine learning algorithms to the problem of requirements engineer classification to answer the two major questions “Which works best (Bag of Words (BoW) vs. Term Frequency–Inverse Document Frequency (TF-IDF) vs. Chi Squared (CHI2)) for classifying Software Requirements into Functional Requirements (FR) and Non-Functional Requirements (NF), and the sub-classes of Non-Functional Requirements?” and “Which Machine Learning Algorithm provides the best performance for the requirements classification task?”. The data used to perform the research was the PROMISE_exp, a recently made dataset that expands the already known PROMISE repository, a repository that contains labeled software requirements. All the documents from the database were cleaned with a set of normalization steps and the two feature extractions, and feature selection techniques used were BoW, TF-IDF and CHI2 respectively. The algorithms used for classification were Logist Regression (LR), Support Vector Machine (SVM), Multinomial Naive Bayes (MNB) and k-Nearest Neighbors (kNN). The novelty of our work is the data used to perform the experiment, the details of the steps used to reproduce the classification, and the comparison between BoW, TF-IDF and CHI2 for this repository not having been covered by other studies. This work will serve as a reference for the software engineering community and will help other researchers to understand the requirement classification process. We noticed that the use of TF-IDF followed by the use of LR had a better classification result to differentiate requirements, with an F-measure of 0.91 in binary classification (tying with SVM in that case), 0.74 in NF classification and 0.78 in general classification. As future work we intend to compare more algorithms and new forms to improve the precision of our models.

show abstract

“…In parallel with our proposed approach, the traditional requirements elicitation approach (survey and interview) was carried out through an online survey and interview to elicit users' requirements for the smartwatch domain. Traditional requirements elicitation protocols were followed to conduct this survey [38,39]. The team T2 was responsible for executing this task.…”

Section: Preparationmentioning

confidence: 99%

“…Andrew et al [39] have enlisted the potentials of SNS for software engineering and the effectiveness of SNS to understand needs of end-users. Margaret et al [53] argued that SNS can support software development activities such as RE, development, testing, and documentation.…”

Section: Related Workmentioning

confidence: 99%

Value-Oriented Requirements: Eliciting Domain Requirements from Social Network Services to Evolve Software Product Lines

Ali¹,

Hong²

2019

Applied Sciences

View full text Add to dashboard Cite

Social network services allow a large population of end-users of software products to publicly share their concerns and experiences about software systems. From a software engineering perspective, such data can be collected and analyzed to help software development organizations to infer users’ emerging demands, receive their feedback, and plan the rapid evolution of software product lines. For the evolution of software product lines, organizations supplement emerging requirements in their products to meet user’s needs and also to retain their dominance in the market. Therefore, social network services, being a communication channel, have supported a number of software development activities such as requirements engineering. It has supported software development organizations to cope with numerous limitations of the traditional requirements engineering approaches by eliciting, prioritizing, and negotiating user requirements. However, these approaches do not consider eliciting requirements in terms of variability and commonality while identifying requirements. To address this issue, we have proposed a social network service-based requirement engineering process. It considers the attributes of users’ opinions to determine variability and commonality. In order to justify our proposed approach, a controlled experiment was conducted on a sample set of end-users on Facebook and Twitter. The experimental results show that the team using the proposed approach performed better in terms of efficiency and effectiveness than the team that used a traditional requirements engineering approach.

show abstract

Requirements elicitation techniques: a systematic literature review based on the maturity of the techniques

Cited by 105 publications

References 147 publications

Untitled

Untitled

Software Requirements Classification Using Machine Learning Algorithms

Value-Oriented Requirements: Eliciting Domain Requirements from Social Network Services to Evolve Software Product Lines

Contact Info

Product

Resources

About