Complete study guide for Course Unit 7 & 8 (Direct Dec 2024 Exam Q2 focus [20 Marks]). Master Ontological, Epistemological, and Axiomatic assumptions, and contrast Positivism, Interpretivism, and Pragmatism in Computer Science.
Every research project is guided by underlying philosophical assumptions regarding the nature of reality, knowledge, values, and methods.
Question: What is the nature of reality?
- Realism (Objective): Reality exists independently of human observation (e.g., CPU execution clock cycles exist regardless of whether a developer observes them).
- Relativism / Subjectivism: Reality is constructed socially by human experiences (e.g., software user experience is subjective to each user).
Question: What constitutes valid knowledge and how is it acquired?
- Positivist Epistemology: Knowledge is obtained through objective measurement, observation, and quantifiable empirical proof.
- Interpretivist Epistemology: Knowledge is acquired by interpreting human meanings, social contexts, and subjective developer experiences.
Question: What is the role of values and ethics in research?
- Value-Free: The researcher remains detached and neutral (e.g., automated hardware benchmarking).
- Value-Laden: The researcher acknowledges personal values, ethical biases, and social impacts (e.g., investigating algorithmic bias in AI hiring tools).
Question: What logic and process is used to study reality?
- Deductive Reasoning: Theory $\rightarrow$ Hypothesis $\rightarrow$ Data Collection $\rightarrow$ Confirmation / Rejection.
- Inductive Reasoning: Observation $\rightarrow$ Pattern Recognition $\rightarrow$ Tentative Hypothesis $\rightarrow$ Theory Building.
| Dimension | Positivism | Interpretivism | Pragmatism (Design Science) |
|---|---|---|---|
| Ontology | Single objective reality. | Multiple socially constructed realities. | Reality is chosen based on practical problem utility. |
| Epistemology | Empirical measurement, statistical testing. | Understanding subjective human experience. | |
| Axiology | Value-free and objective. | Value-bound and contextual. | Value-driven toward solving problem utility. |
| Typical CS Method | Controlled benchmarking, experimental algorithm evaluation. | Qualitative developer case studies, usability interviews. | Design Science Research (building and evaluating IT artifacts). |
If asked to defend the philosophical assumptions of a software engineering research project:
State that the study adopts a Positivist paradigm for algorithmic benchmarking (objective measurement of latency/throughput) and a Pragmatist / Interpretivist paradigm for user adoption evaluation, justifying a Mixed Methods approach.