Evidence-Based Software Engineering and Systematic Reviews

Kitchenham, Barbara; Budgen, David; Brereton, Pearl

doi:10.1201/b19467

Cited by 459 publications

(651 citation statements)

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“…The main goal of evidence-based software engineering (EBSE) is "to provide the means by which current best evidence from research can be integrated with practical experience and human values in the decision making process regarding the development and maintenance of software." 25 There are several types of empirical studies that fall under the umbrella of EBSE.…”

Section: Systematic Mapping Studymentioning

confidence: 99%

“…11 Systematic mapping studies (SMSs) aim to provide an overview of the results available within an area by categorizing them along criteria such as type, forum, and frequency. 9,11,26 SMSs are used when the research questions for the literature review are broader or the field of study is less explored. 11 Closely related to SMSs are SLRs whose main role is establishing whether particular techniques or practices work better than other and under what conditions.…”

Section: Systematic Mapping Studymentioning

confidence: 99%

“…11 Closely related to SMSs are SLRs whose main role is establishing whether particular techniques or practices work better than other and under what conditions. 26 Some examples are identifying the benefits of using tools in a particular context or the state of adoption in industry of a particular tool or development approach. 26 We opted to use an SMS because our goal was to provide an overview of the state-of-the-art research at the intersection of 2 fields.…”

Section: Systematic Mapping Studymentioning

confidence: 99%

“…26 Some examples are identifying the benefits of using tools in a particular context or the state of adoption in industry of a particular tool or development approach. 26 We opted to use an SMS because our goal was to provide an overview of the state-of-the-art research at the intersection of 2 fields. We performed our study based on the protocol proposed by Petersen et al, 9 whose main stages we present in Figure 3.…”

Section: Systematic Mapping Studymentioning

confidence: 99%

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A systematic mapping study of information visualization for software product line engineering

Lopez-Herrejon

Illescas

Egyed

2017

J Software Evolu Process

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Software product lines (SPLs) are families of related systems whose members are distinguished by the set of features they provide. Over 2 decades of research and practice can attest to the substantial benefits of applying SPL practices such as better customization, improved software reuse, and faster time to market. Software product line engineering (SPLE) refers to the paradigm of developing SPLs. Typical SPLE efforts involve a large number of features that are combined to form also large numbers of products, implemented using multiple and different types of software artifacts. Because of the sheer amount of information and its complexity, visualization techniques have been used for different SPLE activities. In this paper, we present an extended systematic mapping study on this subject. Our research questions aim to gather information regarding the techniques that have been applied, at what SPLE activities, how they were implemented, the publication fora used, the methods of empirical evaluation, and the provenance of the evaluation examples. Our driving goal is to identify common trends, gaps, and opportunities for further research and application.

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Section: Systematic Mapping Studymentioning

confidence: 99%

Section: Systematic Mapping Studymentioning

confidence: 99%

Section: Systematic Mapping Studymentioning

confidence: 99%

Section: Systematic Mapping Studymentioning

confidence: 99%

See 2 more Smart Citations

A systematic mapping study of information visualization for software product line engineering

Lopez-Herrejon

Illescas

Egyed

2017

J Software Evolu Process

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“…Our systematic review follows the guidelines of a systematic review approach for software engineering presented in [5]. First, we clarify our research questions, then search and identify relevant primary studies, next we synthesise results from the selected primary studies, finally we explore and answer our research questions through metaanalysis.…”

Section: Review Methodologymentioning

confidence: 99%

A systematic review of unsupervised learning techniques for software defect prediction

Shepperd

Guo

2020

Information and Software Technology

150

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Background: Unsupervised machine learners have been increasingly applied to software defect prediction. It is an approach that may be valuable for software practitioners because it reduces the need for labeled training data. Objective: Investigate the use and performance of unsupervised learning techniques in software defect prediction. Method: We conducted a systematic literature review that identified 48 studies containing 2348 individual experimental results, which satisfied our inclusion criteria published between January 2000 and March 2018. In order to compare prediction performance across these studies in a consistent way, we (re-)computed the confusion matrices and employed Matthew's correlation coefficient (MCC) as our main performance measure. Results: Our meta-analysis shows that unsupervised models are comparable with supervised models for both withinproject and cross-project prediction. Among 21 unsupervised models, Fuzzy CMeans (FCM) and Fuzzy SOMs (FSOMs) perform best. In addition, where we were able to check, we found that almost 11% (262/2348) of published results (contained in 16 papers) were internally inconsistent and a further 30% (705/2348) provided insufficient details for us to check.Conclusion: Although many factors impact the performance of a classifier, e.g., dataset characteristics, broadly speaking, unsupervised classifiers do not seem to perform worse than the supervised classifiers in our review. However, we note a worrying prevalence of (i) demonstrably erroneous experimental results, (ii) undemanding benchmarks and (iii) incomplete reporting. We particularly encourage researchers to be comprehensive in their reporting.

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Gender digital divide in education 4.0: A systematic literature review of factors and strategies for inclusion

Peláez‐Sánchez,

George Reyes,

Glasserman‐Morales

2023

Future in Educational Research

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The gender digital divide (GDD) is a phenomenon that refers to the inequalities and differences between men and women in accessing the digital ecosystem in various environments, such as the scenarios of Education 4.0. Bridging the GDD in access, use, and appropriation of technologies would promote the digital inclusion in spaces traditionally dominated by men. A systematic literature review was conducted using seven databases, considering Scopus and Web of Science. Through this, the factors with the most significant impact on the widening of this type of gap were identified. The findings suggest that the elements that limit the GDD are (a) access and use of technologies, (b) social barriers, and (c) gender stereotypes and roles. On the other hand, factors that need to be considered for the digital inclusion of women were identified as follows: (a) the mode of interaction with technologies, (b) digital competencies in Education 4.0, (c) the mode of technology‐mediated teaching and learning, (d) digital competencies in the workplace, and (e) digital participation. It is concluded that more digital inclusion strategies are needed to reduce the GDD, thus requiring greater participation from society and universities to achieve digital equity.

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Evidence-Based Software Engineering and Systematic Reviews

Cited by 459 publications

References 0 publications

A systematic mapping study of information visualization for software product line engineering

A systematic mapping study of information visualization for software product line engineering

A systematic review of unsupervised learning techniques for software defect prediction

Gender digital divide in education 4.0: A systematic literature review of factors and strategies for inclusion

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