Empirical Software Engineering

Aims & 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.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	3.6
2024	3.50

SJR (SCImago Journal Rank)

Year	Value
2024	0.895

Quartile

Year	Value
2024	Q1

h-index

Year	Value
2024	100

Journal Rank

Year	Value
2024	5745

Journal Citation Indicator

Year	Value
2024	2199

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Computer Science, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• Guidelines for conducting and reporting case study research in software engineering

  Citation: 2596

  Authors: Per, Martin

• Supporting Controlled Experimentation with Testing Techniques: An Infrastructure and its Potential Impact

  Citation: 896

  Authors: Hyunsook, Sebastian, Gregg

• On the relative value of cross-company and within-company data for defect prediction

  Citation: 564

  Authors: Burak, Tim, AyÅŸe B., Justin

• Using Students as Subjects—A Comparative Study of Students and Professionals in Lead-Time Impact Assessment

  Citation: 468

  Authors: Martin, Björn, Claes

• Evaluating defect prediction approaches: a benchmark and an extensive comparison

  Citation: 453

  Authors: Marco, Michele, Romain

• What are developers talking about? An analysis of topics and trends in Stack Overflow

  Citation: 449

  Authors: Anton, Stephen W., Ahmed E.

• Studying Software Engineers: Data Collection Techniques for Software Field Studies

  Citation: 338

  Authors: Timothy C., Susan Elliott, Janice

• Comparing and experimenting machine learning techniques for code smell detection

  Citation: 322

  Authors: Francesca, Mika V., Marco, Alessandro

• Parameter tuning or default values? An empirical investigation in search-based software engineering

  Citation: 311

  Authors: Andrea, Gordon

• The empirical investigation of Perspective-Based Reading

  Citation: 308

  Authors: Victor R., Scott, Oliver, Filippo, Forrest, Sivert, Marvin V.

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