Introduction to the EASE 2016 special section: Evidence-based software engineering: Past, present, and future

Beecham, Sarah; Bowes, David; Stol, Klaas-Jan

doi:10.1016/j.infsof.2017.05.002

Cited by 7 publications

(2 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ere has been a rapid surge in the popularity of using the Evidence-Based Software Engineering (EBSE) among researchers due to the applicability of systematic literature review (SLR) in various domains [41][42][43]. e key goal of the SLR study is to systematically identify, classify, and thoroughly synthesize any new evidence based on the data extracted from the selected research publications.…”

Section: Slr Research Methodologymentioning

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

View full text Add to dashboard Cite

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.

show abstract

Section: Slr Research Methodologymentioning

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

View full text Add to dashboard Cite

show abstract

“…In recent years, the paradigm of Evidence-Based Software Engineering (EBSE) and its two main research methods, the systematic mapping studies (SMS) and the systematic literature reviews (SLR) have gained great notoriety [18][19][20][21]. Both tools provide a research method that systematically identifies, classifies and analyses the data reported in scientific publications (the evidence).…”

Section: Introductionmentioning

confidence: 99%

The impact of Use Cases in real-world software development projects: A systematic mapping study

Barros-Justo

Benitti

Tiwari

2019

Computer Standards & Interfaces

View full text Add to dashboard Cite

Context: There is abundant literature on the application of UML Use Cases. However, the impact that these applications have had on real projects (in industry) is not well known. It is necessary to know what the impact of the Use Cases really is, so that both, researchers and professionals can make the most pertinent decisions.Objective: To identify and classify the positive and negative impacts of using Use Cases in real-world settings. Method:We conducted a systematic mapping study. The search strategies retrieved a set of 4,431 papers out of which 47 were selected as primary studies. We defined four facets to classify these studies: 1) the positive impact (advantages), 2) the negative impact (disadvantages), 3) the industry's domain and 4) the type of research reported. Results:Our study identified eight categories of advantages related to the application of Use Cases. The most mentioned were estimation, analysis and automation. These advantages had a heterogeneous distribution along the years. On the other hand, the granularity of the scenarios described in the Use Cases, the lack of a standardized format for specifying requirements, and the lack of appropriate guidelines for analysing them were the most mentioned disadvantages. We identified a variety of industry domains, grouped into seven categories. As we can expect most of the papers report experiences in the Information Technology domain, followed by financials applications. Almost half the papers applied evaluation research, including an empirical validation. Only one third of the analysed papers reported threats to validity, the most mentioned being generalizability (38%). Conclusions:Use Cases have proven to be a useful tool in software development, particularly during the early stages. The positive effects far outweigh the few negative effects reported, although this may be due to the researchers' tendency of not reporting negative results.

show abstract

An Empirical Evaluation of Machine Learning Algorithms for Identifying Software Requirements on Stack Overflow: Initial Results

Ahmad

Feng

Tahir³

et al. 2019

2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS)

View full text Add to dashboard Cite

Introduction to the EASE 2016 special section: Evidence-based software engineering: Past, present, and future

Cited by 7 publications

References 26 publications

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

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

The impact of Use Cases in real-world software development projects: A systematic mapping study

An Empirical Evaluation of Machine Learning Algorithms for Identifying Software Requirements on Stack Overflow: Initial Results

Contact Info

Product

Resources

About