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Empirical Software Engineering (ESE)

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Impact Factor:
3.6
Publisher:
Springer
ISSN:
1382-3256
Viewed:
31360
Tracked:
34

Call For Papers

Empirical Software Engineering (ESE) is an academic journal published by Springer. (ISSN 1382-3256, impact factor 3.6, CCF B).

Aims and scope Empirical Software Engineering provides a forum for applied software engineering research with a strong empirical component. Over the last decade, it has become clear that empirical studies are a fundamental component of software engineering research and practice: Software development practices and technologies must be investigated by empirical means in order to be understood, evaluated, and deployed in proper contexts. This stems from the observation that higher software quality and productivity is more likely to be achieved if well-understood, tested practices and technologies are introduced in software development. Reported empirical studies usually involve the collection and analysis of data and experience that can be used to characterize, evaluate and reveal relationships between software development deliverables, practices, and technologies. Over time, it is expected that such empirical results will form a body of knowledge leading to widely accepted and well-formed theories. Empirical Software Engineering aims at promoting the publication of industry-relevant research since one of its goals is to address the significant gap between research and practice. Research Articles Both original and replicated studies can be reported, varying from controlled experiments to field studies, from data intensive to qualitative. Preference is given to studies that can be replicated or expanded upon. Papers on the infrastructure for supporting empirical research are also of interest. Specific topics relevant to the journal include the following: Analysis and design Model-driven development Requirements engineering Verification and validation Maintenance and evolution Quality assurance Dependability analysis Project management Organization models for software development Predictive models for software dependability Software engineering economics Applications of artificial intelligence techniques to software engineering Qualitative analysis
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