Generative illustration for Guidelines for Conducting and Reporting Case Study Research in Software Engineering (Empir Software Eng, 2009)
Guidelines for Conducting and Reporting Case Study Research in Software Engineering (Empir Software Eng, 2009)
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Software Engineering Case Studies
- Introduces case study research as a methodology for studying contemporary software engineering phenomena in their natural contexts.
- Explains variation in how case studies are understood and how this affects study quality.
- Provides guidelines and recommended practices for researchers conducting case studies and readers evaluating published reports.
- Adapts terminology and methods from social science and information systems to software engineering.
- Presents empirically derived and evaluated checklists for conducting and assessing case study research.
Case Studies in Software Engineering
- Empirical research is increasingly recognized as essential in software engineering, yet it remains a very small share of computer science research, especially studies involving human subjects and real-world settings.
- Existing methodological guidance has largely emphasized quantitative experiments, measurements, and systematic reviews, while case studies and qualitative methods have received comparatively little attention.
- The term “case study” is applied inconsistently, alongside labels such as field study and observational study, creating confusion and making it difficult to compare or aggregate findings.
- Case studies are well suited to investigating contemporary software engineering phenomena that cannot easily be isolated, offering deeper contextual understanding even when they cannot establish causal relationships as controlled experiments can.
- Criticisms that case studies are biased, non-generalizable, or less valuable can be addressed through rigorous methodology and a broader view of knowledge than statistical significance alone; researchers therefore need stronger expertise to evaluate them.
Case studies do not generate the same results on e.g. causal relationships as controlled experiments do, but they provide deeper understanding of the phenomena under study.
Software Engineering Case Studies
- Case study methodology is more mature in social science and information systems than in software engineering, motivating guidance tailored to the software domain.
- Software engineering studies differ because they examine organizations developing software, project-based work, and advanced engineering performed by highly educated professionals.
- The field takes a pragmatic, results-oriented approach to methodology rather than relying primarily on philosophical positions.
- The paper synthesizes research-methodology literature and adapts it to software engineering, including checklists developed through systematic analysis and later evaluation by researchers.
- Rather than defining one absolute standard for a good case study, the paper emphasizes context-sensitive judgment and identifies issues that collectively influence research quality.
- The paper establishes terminology, situates case studies among empirical methods, explains their motivation, and introduces a research process that structures the rest of the discussion.
The minimum requirement for each issue must be judged in its context, and will most probably evolve over time.
Defining Case Studies
- The paper maps its structure across data collection, analysis, reporting, and the reading and review of case study reports, with checklists provided in the appendix.
- Three published examples from agile methods research—extreme programming, requirements engineering, and quality assurance—illustrate both effective solutions and problems in applying case study methods.
- Case studies are defined as empirical investigations of contemporary phenomena in context, typically using multiple evidence sources and involving limited experimental control.
- Surveys collect standardized information, experiments manipulate variables under controlled or quasi-controlled conditions, and action research actively seeks to influence or change its subject.
- Unlike action research, case studies are primarily observational; ethnographic studies are treated here as a specialized form of case study focused on cultural practices or extended observation.
More strictly, a case study is purely observational while action research is focused on and involved in the change process.
Case Study Methodologies
- Case studies overlap with other observational and empirical methods, often incorporating surveys, literature reviews, archival analysis, interviews, and ethnographic observation.
- Different research methodologies serve different purposes: exploration, description, explanation, and improvement; no single method is suitable for every research goal.
- Although case studies were initially associated mainly with exploratory research, they can also support descriptive, explanatory, confirmatory, and improvement-oriented studies.
- Case studies may be positivist, critical, or interpretive, depending on whether they test hypotheses, critique systems of domination, or understand phenomena through participants’ perspectives.
- Software engineering case studies tend to favor a positivist perspective, particularly when they aim to explain phenomena, but conducting research in real-world settings requires balancing experimental control against realism and complexity.
“Many flexible design studies, although not explicitly labeled as such, can be usefully viewed as case studies.”
Case Study Methodology
- Case studies take place in real-world settings, giving them high realism but generally less experimental control.
- They rely primarily on qualitative data for rich, deep descriptions, though combining qualitative and quantitative data through mixed methods can provide stronger understanding.
- Case studies typically use flexible designs, allowing key parameters to change during the research, unlike the fixed designs common to experiments and surveys.
- Triangulation strengthens empirical research by examining a phenomenon through multiple data sources, observers, methods, or theoretical perspectives.
- Yin emphasizes that case studies address situations with many variables and few data points, requiring converging evidence and theory-informed data collection and analysis.
- The methodologies differ in purpose and structure: surveys are descriptive and quantitative, case studies exploratory and qualitative, experiments explanatory and quantitative, and action research improvement-oriented and qualitative.
Case studies are by definition conducted in real world settings, and thus have a high degree of realism, mostly at the expense of the level of control.
Case Studies in Software Engineering
- Case studies do not aim for statistical significance; instead, they build strong conclusions by linking diverse forms of evidence into a clear chain of reasoning.
- A rigorous case study begins with defined research questions, planned and consistent data collection, systematic analysis, and explicit attention to threats to validity.
- Case studies are well suited to software engineering because the field is multidisciplinary and involves individuals, teams, organizations, and social and political factors.
- Their flexibility allows researchers to study complex phenomena in context, especially when the boundary between a software process and its environment is unclear.
- The case study process includes design, preparation, evidence collection, data analysis, and reporting, with iteration among these stages rather than a strictly linear progression.
Case studies offer an approach which does not need a strict boundary between the studied object and its environment; perhaps the key to understanding is in the interaction between the two?
Planning Case Studies
- Case study research can proceed incrementally, with additional data collection planned when evidence is insufficient, but it must begin with clear objectives.
- Changing the objectives substantially creates a new case study rather than merely modifying the original one, although the distinction requires judgment.
- A case study connects an overall objective to research questions, theories, hypotheses, an object of study, units of analysis, subjects, data, and a detailed protocol for collecting and analyzing evidence.
- The proposed guidelines follow the research process: setting objectives and preparing collection, gathering data, analyzing it, and reporting findings.
- Although case studies are flexible, careful planning remains essential; a strong plan identifies the case, theoretical framework, research questions, methods, and strategy for selecting data sources.
- Objectives and research questions may evolve during iterative research, becoming more focused as the study progresses.
This does not mean planning is unnecessary. On the contrary, good planning for a case study is crucial for its success.
Designing Case Studies
- A software engineering case study can examine almost any contemporary phenomenon in its real-life context, including projects, people, processes, products, policies, technologies, or events; artificial “toy programs” are excluded.
- Researchers must distinguish between holistic case studies, which treat the case as a whole, and embedded studies, which examine multiple units of analysis within a broader case.
- The classification of cases depends on the study’s chosen context and research goals: the same projects may be treated as embedded units or as separate holistic cases.
- A clear theoretical frame of reference helps define the study’s context and guide interpretation, although software engineering research may instead rely on an explicit viewpoint or use grounded theory without a prior theory.
- Data-collection methods are planned at the design stage, while cases and units of analysis are intentionally selected for their expected qualities—such as being typical, critical, revelatory, unique, extreme, or paradigmatic—rather than sampled randomly for population-wide generalization.
The case may in general be virtually anything which is a “contemporary phenomenon in its real-life context” (Yin 2003).
Case Selection and Design
- Case-study units must be chosen to contain the variation needed for comparison, although researchers often select cases based on availability.
- Replication requires especially careful selection: literal replication predicts similar findings, while theoretical replication predicts contrasting results for identifiable reasons.
- The three example studies pursued different goals, including examining agile processes, evaluating requirements prioritization, and developing defect-prediction models.
- The studies used different structures: XP involved two companies and units of analysis, RE used one holistic unit, and QA examined three projects within one company.
- Existing academic-industry relationships influenced company selection, while prior methods and studies shaped the research designs and frames of reference.
- Data sources varied across the cases, relying mainly on interviews for XP, questionnaires for RE, and defect metrics for QA.
A case study may be literally replicated, i.e. the case is selected to predict similar results, or it is theoretically replicated, i.e. the case is selected to predict contrasting results for predictable reasons (Yin 2003).
Case Study Protocols
- A case study protocol consolidates research design decisions and field procedures, evolving whenever the study’s plans change.
- Maintaining the protocol guides data collection, clarifies research questions and sources, and reduces the risk of overlooking important evidence.
- External review of the protocol can identify missing data sources, interview questions, participant roles, and inconsistencies between research and interview questions.
- The protocol also functions as a version-controlled research diary, documenting data collection, analysis, and adaptations for later reporting.
- Because protocols may contain confidential information, researchers should publish only suitable sections while addressing ethics such as consent, confidentiality, sensitive findings, and feedback.
Finally, it can serve as a log or diary where all conducted data collection and analysis is recorded together with change decisions based on the flexible nature of the research.
Ethics and Informed Consent
- Case study participants and organizations must explicitly consent, both to meet ethical standards and, in some jurisdictions, legal requirements.
- Researchers should not bypass consent through indirect data collection, because sustained trust in software engineering research depends on ethical conduct.
- Ethical review requirements vary internationally; where formal review boards do not exist, peer review of the case study protocol is still recommended.
- Consent is best documented through a form or contract covering the study purpose, procedures, voluntary participation, anonymity, risks, benefits, confidentiality, and ethical approvals.
- A detailed case study protocol promotes consistency by specifying procedures, research instruments, data analysis methods, participant invitations, and data storage practices.
It may be tempting for the researcher to collect data e.g. through indirect or independent data collection methods, without asking for consent.
Ethics in Case Studies
- Researchers should obtain dated signatures and separate consent for current research versus undefined future uses of collected data.
- Confidentiality agreements must protect both organizations and individual employees, while recognizing that anonymity can fail when people or companies are identifiable through distinctive characteristics.
- Participants should be warned in advance that findings may expose organizational weaknesses or poor performance; suspected legal violations must still be reported.
- Research incentives should be made explicit so their potential influence on study validity can be assessed.
- Returning interview transcripts and analyses to participants supports accuracy, trust, and research validity, while formal agreements can regulate publication and raw-data access.
However, not only can information be sensitive when leaking outside a company. Data collected from and opinions stated by individual employees may be sensitive if presented e.g. to their managers (Singer and Vinson 2002).
Triangulating Case Study Data
- Case study research should draw on multiple data sources to reduce the influence of any single interpretation.
- Triangulation strengthens conclusions when the same finding emerges across different sources, methods, roles, projects, or products.
- Researchers should deliberately examine differing viewpoints and contrasts between data sources, since these differences can reveal important findings.
- Data collection techniques are organized into three levels: direct interaction with subjects, indirect collection of raw data, and independent analysis of existing work artifacts.
- The design checklist emphasizes clear research questions, theoretical grounding, appropriate case and subject selection, validity, causal distinction, triangulation, and protection of individuals and organizations.
If the same conclusion can be drawn from several sources of information, i.e. triangulation (Section 2.2), this conclusion is stronger than a conclusion based a single source.
Case Study Data Collection
- First-degree methods, such as interviews, are more costly but give researchers substantial control over what data is collected and how.
- Third-degree methods, including organizational databases and stored metrics, are cheaper but offer limited control over data quality, validity, and completeness.
- Because third-degree data was originally collected for another purpose, it may not satisfy the requirements of the research study.
- Software engineering case studies commonly combine interviews, observations, archival data, and metrics, often using feedback from participating organizations.
- Interviews may be unstructured, semi-structured, or fully structured; semi-structured interviews balance planned coverage with flexibility and exploration.
The development of the conversation in the interview can decide which order the different questions are handled, and the researcher can use the list of questions to be certain that all questions are handled.
Designing Effective Interviews
- Interview sessions typically move from an explanation of purpose and simple background questions to the main questions, with sensitive topics introduced only after trust and confidentiality have been established.
- Researchers can structure interviews using funnel, pyramid, or time-glass models, varying the progression from open to specific questions.
- Recording interviews is strongly recommended because notes may omit important details; recordings should be transcribed by the researcher, who may gain new insights during transcription.
- Having interviewees review transcripts can clarify what was said, correct misunderstandings, and allow them to revise or qualify their responses.
- Qualitative case studies should select interviewees for differences in roles and perspectives, and continue interviewing until saturation—the point at which no new information emerges.
The funnel model begins with open questions and moves towards more specific ones. The pyramid model begins with specific ones, and opens the questions during the course of the interview.
Interviews and Observations
- Study XP used semi-structured interviews because researchers had preliminary hypotheses but lacked detailed knowledge of the problems involved in combining agile methods with a traditional stage-gate model.
- Interview guides organized topics and approximate time allocations, while researchers preserved flexibility for exploratory and explanatory questioning.
- Interviewees were selected with organizational cooperation, promised anonymity, and generally interviewed by two researchers whose recorded sessions were transcribed alongside field notes.
- Observation methods range from video recording and protocol analysis to think-aloud questioning, audio and keystroke capture, meeting observation, and participant feedback tools.
- Observational approaches can be classified by researcher interaction and subject awareness, distinguishing roles from observing participant to normal team participant or detached researcher.
Another alternative is to apply a “think aloud” protocol, where the researcher are repeatedly asking questions like “What is your strategy?” and “What are you thinking?”
Observations and Archives
- Observations can reveal a deep understanding of how work actually happens, especially when the official account differs from reality.
- Their richness comes at a cost: techniques such as video recording and think-aloud protocols generate substantial data that requires time-consuming analysis.
- An XP team study demonstrated this depth by combining a week of field observation with meeting recordings, photographs, and project artifacts.
- Archival data includes documents, meeting minutes, organizational charts, financial records, and historical measurements, with configuration-management tools providing access to document versions.
- Because archival records were created for organizational rather than research purposes, they may contain irrelevant material or omit needed information; researchers should assess their origins and combine them with methods such as surveys and interviews.
An advantage of observations is that they may provide a deep understanding of the phenomenon that is studied. It should however be noted that it produces a substantial amount of data which makes the analysis time consuming.
Data Collection and Analysis
- Case study data may be newly collected or drawn from existing sources; newly collected data offers greater flexibility and better alignment with research questions.
- The Goal Question Metric approach derives metrics from research goals through progressively refined questions, helping researchers gather relevant data while avoiding unnecessary measurements.
- Archival data—such as effort records, sales figures, defect counts, and module sizes—can be useful but may contain gaps and quality problems because it was collected for different purposes.
- The checklist emphasizes rigorous preparation, including a data-collection protocol, triangulation across sources, clearly defined instruments, sufficient measurements, ethical approval, and informed consent.
- Quantitative case-study analysis can include descriptive statistics, correlation analysis, predictive modeling, and hypothesis testing.
The researcher can neither control nor assess the quality of the data, since it was collected for another purpose, and as for other forms of archival analysis there is a risk of missing important data.
Qualitative Case Analysis
- Quantitative analysis uses descriptive statistics, correlation analysis, predictive models, and hypothesis testing to examine relationships and effects in collected data.
- Quantitative methods generally require a fixed research design; changing questions during data collection can make results difficult or impossible to interpret, especially with small single-case datasets.
- In the RE and QC studies, quantitative analysis revealed patterns and prompted deeper investigation, while the XP study primarily used qualitative analysis supplemented by defect counts.
- Qualitative case-study analysis aims to derive conclusions while maintaining a clear chain of evidence, allowing readers to trace findings back through the data and research decisions.
- Because case-study research is flexible, qualitative analysis must proceed alongside data collection: new insights may require additional data, revised instruments, and systematic procedures to limit researcher bias.
Analysis must be carried out in parallel with the data collection since the approach is flexible and that new insights are found during the analysis. In order to investigate these insights, new data must often be collected, and instrumentation such as interview questionnaires must be updated.
Iterative Qualitative Analysis
- Qualitative data analysis combines hypothesis generation with hypothesis confirmation, supporting both exploratory and explanatory case studies.
- Researchers should remain open-minded during hypothesis generation, using techniques such as constant comparison and cross-case analysis rather than imposing too many preexisting assumptions.
- Hypotheses can be tested through triangulation, replication, additional data, and negative case analysis, which seeks alternative explanations.
- Analysis typically begins with coding data into themes or constructs, organizing codes hierarchically, and adding researcher memos and reflections.
- The process is iterative: coding, hypothesis development, further data collection, and confirmation influence one another until broader generalizations and formal knowledge emerge.
This is, of course, not a simple sequence of steps. Instead, they are executed iteratively and they affect each other.
Structuring Qualitative Analysis
- Identifying hypotheses in qualitative research is not a mechanical procedure; it demands generalization, creativity, and analytical judgment from the researcher.
- Researchers can use tabulation to organize coded data, reveal patterns, and compare issues across interviewees, roles, or companies, but the design of each table must fit the individual case study.
- Software such as NVivo and Atlas.ti can support analysis, although ordinary word processors and spreadsheets may be sufficient for managing textual data.
- The XP study demonstrates a chain of evidence from interviews and transcripts through coding, grouped quotations, and conclusions, strengthening the transparency of the analysis.
- Qualitative analysis requires a structured record of decisions, instruments, codes, memos, and links between evidence, while allowing different levels of formalism.
- The approaches range from intuitive immersion methods to highly formal quasi-statistical techniques, with editing and template approaches offering intermediate structures based on emerging findings or research questions.
This is in no way a simple step that can be carried out by following a detailed, mechanical, approach. Instead it requires ability to generalize, innovative thinking, etc. from the researcher.
Case Study Validity
- Informal immersion approaches make it difficult to establish a clear chain of evidence and interpret findings such as word frequencies in documents and interviews.
- Study XP used an iterative editing approach in which initial codes were expanded and refined; for example, “communication” was divided into horizontal, vertical, internal, and external communication.
- Validity concerns whether findings are trustworthy, accurate, and free from researchers’ subjective bias, and it must be considered throughout the case study—not only during analysis.
- The framework identifies four validity dimensions: construct validity, internal validity, external validity, and reliability, each addressing a distinct source of potential error or misinterpretation.
- Case study validity can be strengthened through triangulation, detailed protocols, peer review, feedback from case subjects, and extended engagement with the data and setting.
It is, of course, too late to consider the validity during the analysis.
Case Study Validity and Reporting
- Researchers should actively examine negative cases and theories that contradict their findings, rather than only seeking confirming evidence.
- Validity threats can be reduced through triangulation, reviews by case representatives, parallel analysis by multiple researchers, and sustained engagement with the organization.
- The studies described used checklists and data triangulation to verify interpretations, including tracing defect reports to the project phase where they originated.
- Reporting is an integral part of empirical research because it communicates findings and enables readers to judge the study’s quality; reports may need to address different audiences.
- A strong case study report explains its purpose, conveys the character of the case, documents the inquiry’s history, presents focused evidence, and situates conclusions while protecting organizational and individual integrity.
An empirical study cannot be distinguished from its reporting. The report communicates the findings of the study, but is also the main source of information for judging the quality of the study.
Building Trustworthy Case Reports
- Case-study reports should document the history of the inquiry—including sequences of actions, participant roles, and review procedures—while balancing transparency against excessive detail.
- Analysis must reduce and organize abundant qualitative data into a traceable chain of evidence connecting findings to research questions and existing theory.
- Readers need representative data snapshots, such as quotations, images, narratives, and classification categories, to judge whether conclusions are adequately supported.
- Case studies need not generalize statistically to be valuable: their conclusions can inform theories, practice, further research, and comparisons with other cases.
- Researchers can structure reports in several ways, including linear-analytic, comparative, chronological, theory-building, suspense-based, and unsequenced formats.
Data is collected in abundance in a qualitative study, and the analysis has as its main focus to reduce and organize data to provide a chain of evidence for the conclusions. However, to establish trust in the study, the reader needs relevant snapshots from the data that support the conclusions.
Reporting Case Studies
- The linear-analytic structure is the most accepted format for academic case-study reporting, but it must be adapted from experimental-reporting models.
- Case-study reports distinguish between earlier studies and the theories that provide the analytical framework for the research.
- The design section documents the case-study protocol, including planning and measures taken to ensure validity.
- Because case studies use flexible designs, data collection and analysis may be combined and organized around coding schemes, cases, or timelines.
- The proposed structure also incorporates validity evaluation, conclusions, implications, limitations, future work, and a reporting checklist.
Since the case study is of flexible design, and data collection and analysis are more intertwined, these sections may be combined into one.
Evaluating Case Studies
- Case study reports are often lengthy because qualitative data cannot be compressed like quantitative results, and their conclusions depend on reasoned links between observations and interpretations rather than statistical significance.
- Readers and reviewers must judge a study’s quality from the report itself, using careful evaluation even when experimental-style standards and strict statistical criteria are unavailable.
- A useful reader’s checklist asks whether the case, objectives, questions, theory, data collection, analysis, validity threats, ethics, conclusions, and practical implications are clearly and credibly reported.
- Case study research examines contemporary phenomena in their natural contexts, focusing on in-depth analysis of typical or distinctive cases rather than statistically representative samples.
- The research proceeds iteratively through design, data collection, analysis, and reporting; maintaining a clear chain of evidence from original data to findings is essential for credibility and future use.
This does however not say that any report can do as a case study report. The reader must have a decent chance of finding the information of relevance, both to judge the quality of the case study and to get the findings from the study and set them into practice or build further research on.
Case Study Quality Checklist
- The checklist evaluates whether a case and its units of analysis are clearly defined, theoretically grounded, and suitably chosen to answer the research questions.
- It emphasizes rigorous preparation for data collection, including documented protocols, multiple sources and methods, well-defined instruments, and adequate measurements.
- Researchers are urged to present sufficient raw data, use transparent and repeatable analysis procedures, and maintain a clear chain of evidence from observations to conclusions.
- Validity threats should be analyzed systematically and reduced through countermeasures and triangulation across data sources, methods, researchers, and theories.
- Ethical responsibilities—including confidentiality, informed consent, researcher integrity, and review-board approval—must be addressed, while conclusions and implications should be tailored to the intended audience.
- The paper offers this framework based on prior literature and the authors’ experience, but stresses that the guidelines still require evaluation through practical use.
Is a clear chain of evidence established from observations to conclusions?
Case Study Quality Checklist
- Data collection should follow the case study protocol, accurately capture the phenomenon, preserve sensitive information, remain traceable, and generate evidence capable of addressing the research question.
- Analysis must define methods and responsibilities, establish a traceable chain from data to theory and research questions, consider alternative explanations, distinguish causes from confounding factors, and examine validity threats.
- Reports should clearly present the case, research objectives, questions, theory, collection and analysis procedures, supporting raw data, ethical issues, conclusions, and implications.
- The reader’s checklist consolidates these requirements into broader criteria covering case definition, theoretical grounding, data sufficiency, transparency, triangulation, ethics, and practical relevance.
- Overall, a credible case study is systematic, transparent, ethically responsible, realistic in its claims, and structured for its intended audience.
Is a clear chain of evidence established from observations to conclusions?
Empirical Research Foundations
- The references establish a broad foundation for empirical software engineering, spanning experiments, case studies, action research, interviews, metrics, and qualitative inquiry.
- Several works focus on making software engineering data and evidence reliable through rigorous collection methods, reporting guidelines, checklists, and systematic reviews.
- Case-study and action-research approaches are treated critically as means of studying real-world software practices, organizational change, risk, and process improvement.
- The bibliography connects research with practice through topics such as technology transfer, project telemetry, software quality monitoring, and cooperative method development.
- It also reflects an evolution toward combining agile development, stage-gate management, and other complementary approaches to improve software project outcomes.
Five misunderstandings about case-study research.
Empirical Software Engineering
- The references establish a broad methodological foundation for empirical software engineering, spanning experiments, case studies, field studies, systematic reviews, and qualitative research.
- Several works focus on designing, evaluating, and reporting empirical studies, emphasizing rigor, transparency, and reproducibility.
- Interpretive and ethnographic research is represented through studies of software practice, engineers, agile development, and requirements engineering in industrial settings.
- The bibliography highlights persistent challenges in software research, including replication, tacit knowledge, protocol analysis, and the integration of evidence from multiple sources.
- Ethical considerations and multidisciplinary approaches are treated as essential complements to technical research methods.
- Overall, the sources connect empirical methods to real-world software development practices rather than treating engineering research as purely laboratory-based.
“Beg, borrow, or steal: using multidisciplinary approaches in empirical software engineering research”
Empirical Software Engineering
- The references collectively emphasize empirical methods in software engineering, including controlled experiments, case studies, surveys, measurement, and technology validation.
- Several works focus on building theories and improving the rigor, review, and practical application of empirical software-engineering research.
- The bibliography highlights established methodological frameworks such as the goal/question/metric method and case-study research design.
- Per Runeson’s profile presents him as a software-engineering professor and empirical researcher focused on software quality, testing, inspections, and agile methods.
- Martin Höst’s work centers on software process improvement, software quality, empirical research, and simulation, using case studies, experiments, and surveys.
The research has a strong empirical focus including cooperation with major companies, resulting in more than 90 papers published in international journals and conference proceedings.
Case Studies in Software Engineering
Case studies do not generate the same results on e.g. causal relationships as controlled experiments do, but they provide deeper understanding of the phenomena under study.
Case Study Methodology
- Triangulation strengthens empirical research by examining a phenomenon through multiple data sources, observers, methods, or theoretical perspectives.
- Yin emphasizes that case studies address situations with many variables and few data points, requiring converging evidence and theory-informed data collection and analysis.
Case studies are by definition conducted in real world settings, and thus have a high degree of realism, mostly at the expense of the level of control.
Iterative Qualitative Analysis
- Hypotheses can be tested through triangulation, replication, additional data, and negative case analysis, which seeks alternative explanations.
- The process is iterative: coding, hypothesis development, further data collection, and confirmation influence one another until broader generalizations and formal knowledge emerge.
This is, of course, not a simple sequence of steps. Instead, they are executed iteratively and they affect each other.
Case Study Validity
- Validity concerns whether findings are trustworthy, accurate, and free from researchers’ subjective bias, and it must be considered throughout the case study—not only during analysis.
- The framework identifies four validity dimensions: construct validity, internal validity, external validity, and reliability, each addressing a distinct source of potential error or misinterpretation.
It is, of course, too late to consider the validity during the analysis.