Development of a human error taxonomy for software requirements: A systematic literature review

2018 ◽  
Vol 103 ◽  
pp. 112-124 ◽  
Author(s):  
Vaibhav Anu ◽  
Wenhua Hu ◽  
Jeffrey C Carver ◽  
Gursimran S Walia ◽  
Gary Bradshaw
Keyword(s):  
Literature Review ◽  
Systematic Literature Review ◽  
Human Error ◽  
Software Requirements ◽  
Error Taxonomy

scholarly journals Challenges of Software Requirements Quality Assurance and Validation: A Systematic Literature Review

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 137613-137634
Author(s):  
Issa Atoum ◽  
Mahmoud Khalid Baklizi ◽  
Izzat Alsmadi ◽  
Ahmed Ali Otoom ◽  
Taha Alhersh ◽  
...  
Keyword(s):  
Quality Assurance ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Software Requirements ◽  
Requirements Quality

Software requirements testing approaches: a systematic literature review

2019 ◽  
Vol 25 (3) ◽  
pp. 317-337
Author(s):  
Jemison dos Santos ◽  
Luiz Eduardo G. Martins ◽  
Valdivino A. de Santiago Júnior ◽  
Lucas Venezian Povoa ◽  
Luciana Brasil R. dos Santos
Keyword(s):  
Literature Review ◽  
Systematic Literature Review ◽  
Software Requirements

scholarly journals Software Requirements Prioritisation: A Systematic Literature Review on Significance, Stakeholders, Techniques and Challenges

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 71497-71523 ◽  
Author(s):  
Fadhl Hujainah ◽  
Rohani Binti Abu Bakar ◽  
Mansoor Abdullateef Abdulgabber ◽  
Kamal Z. Zamli
Keyword(s):  
Literature Review ◽  
Systematic Literature Review ◽  
Software Requirements

A systematic literature review of software requirements prioritization research

2014 ◽  
Vol 56 (6) ◽  
pp. 568-585 ◽  
Author(s):  
Philip Achimugu ◽  
Ali Selamat ◽  
Roliana Ibrahim ◽  
Mohd Naz’ri Mahrin
Keyword(s):  
Literature Review ◽  
Systematic Literature Review ◽  
Software Requirements ◽  
Requirements Prioritization

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

2020 ◽  
Vol 2020 ◽  
pp. 1-19
Author(s):  
Arshad Ahmad ◽  
Chong Feng ◽  
Muzammil Khan ◽  
Asif Khan ◽  
Ayaz Ullah ◽  
...  
Keyword(s):  
Machine Learning ◽  
Literature Review ◽  
Language Processing ◽  
Systematic Literature Review ◽  
Latent Dirichlet Allocation ◽  
Machine Learning Algorithms ◽  
Software Requirements ◽  
Quality Systems ◽  
Stack Overflow ◽  
Requirements Identification

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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