Selecting Empirical Methods for Software Engineering Research

By Steve M. Easterbrook, Janice Singer, Margaret-Anne D. Storey, Daniela E. Damian. Guide to Advanced Empirical Software Engineering, 2008

1,363 citations recorded by the source corpus.

Subfields: Computer Science Applications, Information Systems

View Paper: https://www.semanticscholar.org/paper/acdc89274e34d2f905a055aad41e7f815aef52eb

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Science Applications

1. Christian Bird
2. Margaret M. Burnett
3. Baishakhi Ray
4. Thomas D. Latoza
5. Amy J. Ko
6. Per Runeson
7. Tony Gorschek
8. Michael Unterkalmsteiner

In Information Systems

1. Christian Bird
2. Baishakhi Ray
3. Margaret M. Burnett
4. Sebastian Baltes
5. Paul Ralph
6. Amy J. Ko
7. Thomas D. Latoza
8. Mika V. Mäntylä

Citations in the corpus

• A large scale study of programming languages and code quality in github (2014, methodology): “Our approach is best described as mixed-methods, or triangulation [7] approach.”
• Sampling in software engineering research: a critical review and guidelines (2020, methodology): “Most general methods textbooks, such a Trochim’s excellent Research Methods Knowledge Base (Trochim and Donnelly 2001) and SE-specific methods texts (e.g. Wohlin et al. 2012; Foster 2014; Easterbrook et al. 2008) discuss the logic of sampling, and many of the strategies described in Section 2.”
• Gender and Tenure Diversity in GitHub Teams (2015, methodology): “We followed a mixed-methods approach with a sequential exploratory strategy [25].”
• Grounded Theory in Software Engineering Research: A Critical Review and Guidelines (2016, background): “Many overviews and guiding literature for SE researchers do not even include grounded theory (cf. [ 27, 77]).”
• Guidelines for including the grey literature and conducting multivocal literature reviews in software engineering (2017, methodology): “[62] provide a classification of RQ types that we used to classify a total of 267 RQs studied in a pool of 101 literature reviews in software testing [63].”
• Fair and balanced?: bias in bug-fix datasets (2009, background): “The effects of bias in survey data, for instance, are well known [18].”
• Experimentation in Software Engineering (2012, background): “[50] provide more advice on selection of research strategies.”
• Evaluation and Measurement of Software Process Improvement—A Systematic Literature Review (2012, methodology): “• Survey if the study collects quantitative and/or qualitative data by means of a questionnaire or inter-views [61], [63], [64].”
• A practical guide to controlled experiments of software engineering tools with human participants (2013, background): “Methodological literature instead focuses on other important but more general issues, providing conceptual background on experiments, measurement, and research design (e.g., Wohlin et al. 2000; Easterbrook et al. 2008; Shull et al. 2006; Juristo and Moreno 2001; Kitchenham et al. 2002).”