The who, what, how of software engineering research: a socio-technical framework


Software engineering is a socio-technical endeavor, and while many of our contributions focus on technical aspects, human stakeholders such as software developers are directly affected by and can benefit from our research and tool innovations. In this paper, we question how much of our research addresses human and social issues, and explore how much we study human and social aspects in our research designs. To answer these questions, we developed a socio-technical research framework to capture the main beneficiary of a research study (the who), the main type of research contribution produced (the what), and the research strategies used in the study (how we methodologically approach delivering relevant results given the who and what of our studies). We used this Who-What-How framework to analyze 151 papers from two well-cited publishing venues—the main technical track at the International Conference on Software Engineering, and the Empirical Software Engineering Journal by Springer—to assess how much this published research explicitly considers human aspects. We find that although a majority of these papers claim the contained research should benefit human stakeholders, most focus predominantly on technical contributions. Although our analysis is scoped to two venues, our results suggest a need for more diversification and triangulation of research strategies. In particular, there is a need for strategies that aim at a deeper understanding of human and social aspects of software development practice to balance the design and evaluation of technical innovations. We recommend that the framework should be used in the design of future studies in order to steer software engineering research towards explicitly including human and social concerns in their designs, and to improve the relevance of our research for human stakeholders.

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  1. 1.

    Cooperative and Human Aspects of Software Engineering, co-located with ICSE since 2008

  2. 2.

    We shorten this to “Humans” in the rest of the paper.

  3. 3.

    We recognize that most technical systems are studied or improved with the final goal to benefit a human stakeholder. However, we found in many papers that these human stakeholders are not discussed and that the research is aimed at understanding or improving the technical system.

  4. 4.

    By in silico, we mean performed on a computer or via computer simulation.

  5. 5.

    For example, one EMSE paper we read reported a user study but did not indicate how many participants were involved, nor who the participants were.

  6. 6.

  7. 7.

    Visual Languages and Human-Centric Computing,

  8. 8.

    ACM Conference on Computer Supported Cooperative Work

  9. 9.

    Stol and Fitzgerald interpret and extend this model quite differently to us as they are not concerned with using their framework to discriminate which strategies directly involve human actors. Runkel and McGrath developed their model to capture behavioral aspects and we maintain the behavioral aspect in our extension of their model.


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We would like to thank Cassandra Petrachenko, Alexey Zagalsky and Soroush Yousefi for their invaluable help with this paper and research. We also thank Marian Petre and the anonymous reviewers for their insightful suggestions to improve our paper. We also acknowledge the support of the Natural Sciences and Engineering Research Council of Canada (NSERC).

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Correspondence to Margaret-Anne Storey.

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Communicated by: Burak Turhan


Appendix A: The Circumplex of Runkel and McGrath

Figure 6 shows a sketch of the research strategy circumplex designed by Runkel and McGrath (1972) for categorizing behavioral research strategies. We adapted their model for the How part of our research framework. Runkel and McGrath’s model of research strategies was developed in the 1970s for categorizing human behavioral research, hence it provides a good model for examining socio-technical factors in software engineering.

Fig. 6

Runkel and McGrath’s research strategy circumplex

The McGrath model has been used by other software engineering researchers to reflect on research strategy choice and its implications on research design (Easterbrook et al. 2008), and most recently by Stol and Fitzgerald (2018) as a way to to provide consistent terminology for research strategies (Stol and Fitzgerald 2018) Footnote 9 It is used in the field of Human Computer Interaction (Baecker et al. 1995) and CSCW (Cruz et al. 2012) to guide research design on human aspects.

Three of our quadrants (Respondent, Lab, Field) mirror three of the quadrants in Runkel and McGrath’s book (although we refer to Experimental Strategies as Lab Strategies as we find this less confusing). The fourth quadrant they suggest captures non-empirical research methods: they refer to this quadrant as Theoretical Strategies. We consider two types of non-empirical strategies in our framework: Meta (e.g., systematic literature review), and Formal Theory. We show these non empirical strategies separately to the four quadrants of empirical strategies in our framework. Our fourth quadrant includes Computer Simulations (which we consider empirical), but it also includes other types of data strategies that rely solely on previously collected data in addition to simulated data. We call this fourth quadrant in our framework “Data Strategies”.

One of the core contributions of the Runkel and McGrath research strategy model is to highlight the trade-offs inherent in choosing a research strategy and how each strategy has strengths and weaknesses in terms of achieving higher levels of generalizability, realism and control. Runkel and McGrath refer to these criteria as “quality criteria”, since achieving higher levels of these criteria is desirable. Generalizability captures how generalizable the findings may be to the population outside of the specific actors under study. Realism captures how closely the context under which evidence is gathered may match real life. Control refers to the control over the measurement of variables that may be relevant when human behaviors are studied. Field strategies typically exhibit low generalizability, but have higher potential for higher realism. Lab studies have high control over human variables, but lower realism. Respondent strategies show higher potential for generalizability, but lower realism and control over human variables.

We added a fourth research quality criterion to our model, data precision. Data strategies have higher potential for collecting precise measurements of system data over other strategies. Data studies may be reported as ‘controlled’ by some authors when they really mean precision over data collected, therefore, we reserve the term control in this paper for control over variables in the data generation process (e.g., applying a treatment to one of two groups and observing effects on a dependent variable). McGrath himself debated the distinction between precision and control in his later work. We note that McGrath’s observations were based on work in sociology and less likely to involve large data studies, unlike in software engineering. The Who-What-How framework (bottom of Fig. 1) denotes these criteria in italics outside the quadrants. The closer a quadrant to the criterion, the more the quadrant has the potential to maximize that criterion.

We recommend that the interested reader refer to Runkel and McGrath’s landmark book (Runkel and McGrath 1972) for additional insights on methodology choice that we could not include in our paper.

Appendix B: Sample Paper Classification

Table 3 shows a 15-paper sample classified using our Who-What-How framework. Full data is available at

Table 3 Examples of our paper classification and coding. FS: Field Study, D: Data Study, LE: Lab Experiment, JS: Judgment Study, FT: Formal Theory, SS: Sample Study

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Storey, M., Ernst, N.A., Williams, C. et al. The who, what, how of software engineering research: a socio-technical framework. Empir Software Eng 25, 4097–4129 (2020).

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  • Empirical methods
  • Human studies
  • Software engineering
  • Meta-research
  • Survey